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1 Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2014 The Concentration of Crime in Cities Across the U.S. Kevin T. Wolff Follow this and additional works at the FSU Digital Library. For more information, please contact

2 FLORIDA STATE UNIVERSITY COLLEGE OF CRIMINOLOGY AND CRIMINAL JUSTICE THE CONCENTRATION OF CRIME IN CITIES ACROSS THE U.S. By KEVIN T. WOLFF A Dissertation submitted to the College of Criminology and Criminal Justice in partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Summer Semester, 2014

3 Kevin T. Wolff defended this dissertation on June 19, The members of the supervisory committee were: Eric Baumer Professor Directing Dissertation Mark Horner University Representative Eric Stewart Committee Member Brian Stults Committee Member The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements. ii

4 ACKNOWLEDGMENTS There are many people to thank for their assistance as I undertook the process of completing this dissertation. First I would like to thank my committee members, Dr. Eric Baumer, Dr. Eric Stewart, Dr. Brian Stults and Dr. Mark Horner. It means a great deal to me to know they all took a great deal of time to provide their guidance during the course of this project. I learned a great deal throughout this process and have them all to thank for that. I would like to offer a special thank you to my mentor and chair, Dr. Eric Baumer. Throughout my time at Florida State University his patience and careful thought has taught me so much about the research process and the mastery of this discipline. I am extremely indebted to him for the time he has taken to mentor me and help me to produce the best work possible. Without his guidance I would not be in the position I am today. For that I am extremely grateful. I hope to continue to learn from him as we work together in the future. Additionally, I would like to thank the rest of the faculty, the staff and my classmates in the College of Criminology and Criminal Justice. Without the support of all the people that have been a part of my life during the last six years in Tallahassee, the goal of obtaining my doctorate would not have materialized. To my colleagues Jon Intravia, Josh Kuch and Ashley Arnio, thank you for commiserating with me when it was essential to my sanity and for your patience as we both developed our skills throughout the course of this program. Last but not least, I would like to thank my friends and family for their continuing and incalculable support. Thank you to my parents, Julie and Steven Wolff who have made it possible to further my education and pursue my goals. Without your support, this would not have become a reality. Thank you, Dr. Karla Dhungana for leading the way through the program here at FSU and for setting the bar so high. To the rest of my friends in Tallahassee and from afar, your support and friendship continues to drive me to be the best I can be and I appreciate all that you have done. iii

5 TABLE OF CONTENTS List of Tables... vii List of Figures... ix Abstract... x 1. THE CONCENTRATION OF CRIME Existing Knowledge on the Concentration of Crime and Current Limitations Including a Spatial Dimension of Concentration Variation in the Degree of Concentration across Cities Potential Consequences of the Variation in Crime Concentration The Current Study ANTICIPATING VARIATION IN CRIME S CONCENTRATION Introduction Ecological Conditions Known to Impact Crime The Impact of Economic Deprivation The Impact of Racial and Ethnic Heterogeneity The Impact of Unemployment The Existence of Crime Generators and Crime Attractors Criminogenic Pathways and Crime Social Change and the Concentration and Clustering of Criminogenic Conditions Deindustrialization Suburbanization Immigration The Political Economy The Concentration and Clustering of Crime Generating Places City-specific Approaches to Crime and Other Social Problems Public Housing Urban Redevelopment Policing THE CONCENTRATION OF CRIME AND CITY CRIME RATES Existing Knowledge on City Crime Rates Adding to our Understanding of Between-City Differences in Crime The Contagious Nature of Crime Adversary Effects and the Code of the Street iv

6 Retaliation Crime Concentration and Clustering and Social Contagion The Concentration of Crime and Effective Policing MEASURING THE CONCENTRATION AND CLUSTERING OF CRIME Data Sources Measuring the Concentration and Clustering of Crime The Traditional Measure of Crime s Concentration A Measure of the Spatial Concentration of Crime Converting Crime Data to a Grid of Crime Densities Accounting for the Spatial Proximity of Subareas Calculating the Spatial H Index Isolating the Impact of Crime s Concentration VARIATION IN THE CONCENTRATION AND CLUSTERING OF CRIME A Description of Crime s Concentration in Cities across America The Association between the Concentration and Clustering of Crime Variability in the Concentration and Clustering of Crime across Crime Types THE IMPACT OF CRIME S CONCENTRATION ON CITY CRIME RATES Overview The Concentration and Clustering of Homicide and City Homicide Rates The Concentration and Clustering of Robbery and City Robbery Rates The Concentration and Clustering of Assault and City Assault Rates The Concentration and Clustering of Burglary and City Burglary Rates The Effect of the Concentration and Clustering of Crime in Equivalent Samples Robustness Checks and Alternative Modeling Specifications CONCLUSIONS AND IMPLICATIONS Theoretical Expectations, Data and Methods Employed, and Research Findings Does the Concentration and Clustering of Crime Vary Across Cities? Does the Concentration of Crime have an Impact on City Crime Rates? The Theoretical Implications of the Current Research Limitations of the Current Work and Avenues for Future Research Conclusion APPENDICES v

7 A. VISUALIZATION OF A LOCAL ENVIRONMENT. DRAWN FROM LEE, REARDON, FIREBAUGH, FARRELL MATTHEWS AND O SULLIVAN (2008) B. CITIES INCLUDED IN DISSERTATION ON THE CONCENTRATION OF CRIME ACROSS U.S. CITIES C. SUPPLEMENTARY TABLES FOR CHAPTER FOUR D. SUPPLEMENTARY TABLES FOR CHAPTER FIVE E. SUPPLEMENTARY TABLES FOR CHAPTER SIX REFERENCES BIOGRAPHICAL SKETCH vi

8 LIST OF TABLES 4.1 Summary Statistics for City Characteristics used in Analysis of City-Crime Rates; Max Sample (n=86) Summary Statistics for the Concentration of Crime Summary Statistics for the Spatial Concentration (i.e. Clustering) of Crime Summary Statistics for the Concentration and Clustering of Crime with Outliers Removed Correlation between Aspatial and Spatial Measures of Concentration Rank Ordering of Cities by Measures of Concentration Correlation of Crime Concentration / Clustering Across Crime Types Rank Ordering of Cities by Measures of Concentration across Crime Types Summary Statistics for City Characteristics used in Analysis of City Homicide Rates (n=81) Bivariate Correlations between Variables Included in Analysis of City Homicide Rates (n=81) Baseline Regression Results for Multivariate Analysis of City Homicide Rates (n=81) Baseline Regression Results for Multivariate Analyses of City Homicide Rates, Outliers Removed (n=79) Fully-Specified Regression Results for Multivariate Analyses of City Homicide Rates, Outliers removed (n=79) Summary Statistics for City Characteristics used in Analysis of City Robbery Rates (n=85) Bivariate Correlations between Variables Included in Analysis of City Robbery Rates (n=85) Baseline Results for Multivariate Analysis of City Robbery Rates (n=85) Fully-Specified Regression Results for Multivariate Analysis of City Robbery Rates (n=85) Summary Statistics for City Characteristics used in Analysis of City Assault Rates (n=73) vii

9 6.11 Bivariate Correlations between Variables Included in Analysis of City Assault Rates (n=73) Baseline Results for Multivariate Analysis of City Assault Rates (n=73) Baseline Results for Multivariate Analysis of City Assault Rates, Outliers Removed (n=71) Fully-Specified Regression Results for Multivariate Analyses of City Assault Rates, Outliers Removed (n=71) Summary Statistics for City Characteristics used in Analysis of City Burglary Rates (n=85) Bivariate Correlations between Variables Included in Analysis of City Burglary Rates (n=85) Baseline Results for Multivariate Analysis of City Burglary Rates Fully-Specified Regression Results for Multivariate Analyses of City Burglary (n=85) Fully-Specified Regression Results for Multivariate Analyses of City Crime, Listwise Sample, Outliers Removed (n=68) B.1 Cities Included in Dissertation on the Concentration of Crime Across U.S. Cities C.1 Summary Statistics for All City Characteristics Used in Analysis of City Crime Rates, Crime- Specific Samples Shown D.1 Summary Statistics for the Concentration and Clustering of Crime, Listwise Sample, Outliers Removed (n=73) D.2 Correlation between Aspatial and Spatial Measures of Concentration, Listwise Sample (n=73) D.3 Rank Ordering of Cities by Measures of Concentration D.4 Correlation of Clustering Across Crime Types Using Various Radii E.1 Summary Statistics for City Characteristics used in Analysis of City Crime Rates, Listwise Sample, Outliers Removed (n=68) E.2 Correlation between the Concentration and Clustering of Crime and Crime Rates, Listwise Sample (n=68) E.3 Results for Multivariate Analysis of City Crime Rates using Different Radii, Listwise Sample, Outliers Removed (n=68) viii

10 ix

11 LIST OF FIGURES 1 The Spatial Concentration of Robbery in Houston, TX and Columbus, OH Reardon and O Sullivan s (2004) Dimensions of Spatial Segregation Varying Degrees of Crime Clustering Across U.S. Cities Distribution of Aspatial Measure of Concentration Distribution of the Spatial Concentration of Robbery Distribution of the Spatial Concentration of Aggravated Assault Distribution of the Spatial Concentration of Homicide Distribution of the Spatial Concentration of Burglary Cities in which Crime is Concentrated by not Spatially Clustered Cities in which Crime is Concentrated and also Spatially Clustered Variation in Concentration across Crime Types in Chandler, AZ Variation in Clustering Across Crime Types in Worcester, MA Predicted Logged Homicide Rate at Different Levels of Homicide Concentration Predicted Logged Homicide Rate at Different Levels of Homicide Clustering x

12 ABSTRACT Prior empirical research has shown that a large proportion of a city s total crime arises from a relatively small number of locations within its jurisdiction. Drawing from results of research on the distribution of crime in a handful of cities, scholars have suggested that this dramatic concentration of crime is likely to be comparable in cities across the U.S. Although evidence from existing research is compelling, there are theoretical reasons to believe that the concentration of crime may not be as invariant as suggested in the past. Additionally, the traditional measures of concentration utilized in prior research fail to account for how tightly clustered these high-crime places are within space, leading to a relatively ambiguous definition of the term concentration. Finally, there are theoretical reasons to believe that accounting for the concentration of crime may add to our knowledge on the factors which contribute to the between-city difference in crime. To date, this possibility has not been explored in prior research. Thus, our knowledge of the concentration of criminal activity and its consequences remains relatively limited. To expand our knowledge on the concentration of crime, this dissertation addresses two primary research questions: (1) Does the concentration of crime vary across cities?, (2) Does variation in the concentration of crime have a significant impact on between-city differences in crime? These questions are answered by first exploring the variation revealed from two measures that reflect slightly different dimensions of concentration (i.e. evenness and clustering), disaggregated by crime-type, for a relatively large sample of American cities. Subsequently, the study assesses the effects of these measures on between-city difference in city crime rates. Tract-level crime data drawn from the National Neighborhood Crime Survey, a multicity database on crime in 91 cities from across the country, provided the information from which the measures of concentration and clustering were created. In combination with city-level data on socioeconomic and demographic characteristics drawn from a number of sources, the impact of crime s concentration on city crime rates was then examined in an empirical context. Results indicate that the concentration of crime is not as invariant as suggested in prior research. Additionally, multivariate analyses indicate that greater concentration of homicide is associated with lower homicide rates. Similar findings are observed for robbery, though in this instance conclusions are sensitive to model specification and sample composition. No significant link is found between concentration and crime rates for assault and burglary. The implications of xi

13 the results of this dissertation for theory and research on the concentration of crime and aggregate crime rates are discussed. xii

14 CHAPTER ONE THE CONCENTRATION OF CRIME It is now well-known that crime is not equally distributed across space. Dating back to the Chicago School and beyond, it has been observed that some places suffer from significantly higher rates of crime than others (Guerry, 1833; Park, 1925; Shaw and McKay, 1942; Reiss and Tonry, 1986; Sampson, 1985; Smith, 1986). Criminologists have explored crime s distribution and the factors thought to contribute to these between-area differences in crime at many levels of geography, examining the impact of structural characteristics on levels of crime across states (Loftin and Hill, 1974), cities (Baumer, Lauritsen, Rosenfeld, and Wright, 1998), neighborhoods (Bursik and Grasmick, 1983; Sampson, 1985; Wilson, 1987) and even single addresses (Sherman, Gartin and Buerger, 1989). Recently, the term concentration has been used to describe the tendency of crime to be tied to a small number of places within a given city. Indeed, research in a few select cities has shown that a large proportion of a city s total crime arises from a small number of locations within its jurisdiction (Eck, Gersh and Taylor, 2000; Sherman et al., 1989; Pierce, Spaar and Briggs, 1988; Spelman, 1995; Weisburd and Mazerolle, 2000; Weisburd and Amram, 2014). However, a broad look at the research on aggregate crime rates suggests our knowledge of the concentration of crime in cities across the U.S. remains limited in a number of ways. First, perhaps due to the dearth of comparative literature in this area, past research has not considered the possibility that the concentration of crime may vary significantly from one city to another. Although evidence from existing research is compelling, there are theoretical reasons to believe that the concentration of crime may not be as invariant as suggested in the past. Secondly, the traditional measures of concentration utilized in past research fail to account for how tightly clustered these high-crime places are within space leading to a relatively ambiguous definition of the term concentration, thus, our knowledge of the concentration and spatial patterning of criminal activity remains relatively limited. Finally, there are theoretical reasons to believe that any observed variation in the concentration of crime may have implications for the total volume of crime which occurs within a city s borders. Accordingly, accounting for the concentration and/or clustering of crime may add to our knowledge on the factors which contribute to between-city difference in crime. To date, this possibility has not been considered in previous research. 1

15 This dissertation adds to the literature on the concentration of crime by addressing two central research questions: (1) Does the concentration of crime vary across cities? (2) Does variation in the concentration of crime have a significant impact on between-city differences in crime? These questions are addressed by examining crime s concentration, disaggregated by crime-type, for a relatively large sample of cities across the U.S. In addition to being one of the first comparative evaluations of crime concentration using a traditional measure drawn from past research, this project contributes to the literature by adapting a new measure developed by demography and stratification scholars (Reardon and Firebaugh, 2002) which adds an explicitly spatial component to the definition of concentration. The current project broadens the scope of research is this area by documenting observed differences in the concentration of crime across a large sample of cities using two measures that reflect slightly different dimensions of concentration (i.e. evenness and clustering) and by assessing the implications of city-level variance in crime concentration for city-level differences in the overall volume of crime. The remainder of this chapter reviews prior research, highlighting gaps in the existing knowledge on the concentration of crime, ending with an overview of the current project and an outline of the chapters that follow. 1.1 Existing Knowledge on the Concentration of Crime and Current Limitations Over the years, much of the aggregate-level research has been devoted to explaining why some neighborhoods, cities, counties, and states exhibit higher rates of crime than others (Bursik and Grasmick, 1993; Baumer et al., 1998; Kornhauser, 1978; Morenoff, Sampson and Raudenbush, 2001; Shaw and McKay, 1942; Taylor, 2001). More recently, with the help of advances in data availability, researchers have explored the distribution of crime at much smaller levels of aggregation such as the address-, blockface-, or street segment-level. Both cross-sectional and longitudinal studies of the distribution of crime at these micro-levels of aggregation have found evidence that that crime is strongly coupled to a small number of micro-areas within a given city (Brantingham and Brantingham, 1994; Groff, Weisburd and Yang, 2010; Weisburd, Groff, and Yang, 2004; Weisburd, Groff and Yang, 2012). The dramatic concentration of crime has been referenced numerous times in recent research. For instance, Weisburd and others have described the concentration of crime within a given city by citing the small percentage of high-crime street segments, or addresses, which account for the majority of the total volume of crime (e.g. Sherman et al., 1989; Weisburd et al., 2004). In other studies of crime, concentration has been connected to the identification of crime hot spots using a series of statistical procedures which examine whether the 2

16 observed pattern of crime is significantly different than what would be expected from a random distribution (Sherman et al., 1989; Eck, 2005; Bailey and Gatrell, 1995). Indeed, research in a few select cities has shown that a very large percentage of total crime arises from a relatively small geographic space within those jurisdictions (Eck, Gersh and Taylor, 2000; Sherman et al., 1989; Pierce, Spaar and Briggs, 1988; Spelman, 1995; Weisburd and Mazerolle, 2000; Weisburd and Amram, 2014). For example, Shaw and McKay s (1942) pioneering research in Chicago during the early 1900s identified several small areas of Chicago that appeared to contribute substantially to the city s overall crime rate. In their study of routine activities theory, Sherman and colleagues (1989) found that only 3 percent of the addresses in Minneapolis (designated as hot spots ) accounted for over half of the calls to police. More recently, Weisburd and colleagues have documented similar patterns of crime concentration in Seattle. These scholars note that between 4 and 6 percent of street segments in Seattle accounted for 50 percent of the incidents reported to the police and that this number remained consistent over a period of 14 years (Weisburd et al., 2004; Weisburd et al., 2012). Finally, largely parallel results have been reported for Boston (Pierce, Spaar and Brigg, 1988; Braga, Papchristos and Hureau, 2010) and Tel Aviv, Israel (Weisburd and Amram, 2014). Although the evidence on crime s concentration from existing research studies is highly suggestive, the breadth of research on this topic remains limited in a number of ways. First, existing research on the concentration of crime within cities has failed to consider the inherently spatial nature of crime data, leaving out potentially meaningful information regarding the clustering of high crime places to one another within space. Accounting for the degree of spatial proximity of highcrime places may yield important insights into a city s crime problem. Second, existing research has not systematically evaluated the possibility that variation in degree of crime concentration exists across cities in the U.S. As elaborated on below, there are both empirical and theoretical reasons to believe that there may be meaningful variation in the degree to which crime is concentrated across cities in the U.S., and that these patterns may take unique forms and vary based on the type of crime being considered. Finally, perhaps due to the absence of comparative (e.g. cross-city) work in this area, prior research has not considered that the unequal distribution and spatial clustering of crime may have an independent impact on the total volume of crime within a given city. There are theoretical reasons to believe that accounting for the degree to which crime is concentrated in a small number of areas within a city s border as well as the clustering of these high-crime places within space, may advance our knowledge regarding the between-city differences in crime. The 3

17 following three sections outline each of these limitations in greater detail. This chapter ends with an overview of the current project and an outline of the chapters that follow Including a Spatial Dimension of Concentration Recent aggregate-level research has utilized the term concentration to describe the unequal distribution of crime across addresses, street-segments, and neighborhoods within cities in the U.S. As mentioned above, research in a small number of cities has shown that a very large percentage of total crime arises from a relatively small geographic space within those jurisdictions (Eck, Gersh and Taylor, 2001; Sherman, Gartin and Buerger, 1989; Pierce, Spaar and Briggs, 1988; Spelman, 1995; Weisburd and Mazerolle, 2000; Weisburd and Amram, 2012). Importantly, however, the term concentration, as it has been used in past research, misses an important element: the spatial nature of the phenomena being considered. While measures referenced in prior work, such as the proportion of addresses which are capable of accounting for the majority of criminal events in a given city provide a measure of the unequal distribution of crime, they do not necessarily provide any information on the proximity of these high-crime places (e.g. so-called hot spots ) to one another. A more complete conceptualization of concentration would include a spatial component in addition to the distributional one. That is, I suggest a more useful way to conceptualize "concentration" in crime studies would entail consideration of the extent to which crime is both unequally distributed as well as cluster closely within space. This distinction, between the unequal distribution of crime at the neighborhood-level (i.e. the traditional aspatial conceptualization of concentration) and the clustering of high-crime places to one another is potentially meaningful for a number of reasons. First, while it is possible for two cities to contain the same number of high-crime areas which account for an equal share of the total volume of crime, the spatial patterning of those places may be quite distinct from one another, leading to very different portraits of the crime problem. Secondly, both the degree to which crime is concentrated in a select number of places, and the degree to which those high-crime places are clustered, may have a distinct impact on the total rate of crime experienced in cities. In order to keep these two dimensions clear, this dissertation refers to the unequal distribution of crime as the degree of concentration and to the spatial proximity of high-crime places as the degree of clustering. In the sections and chapters that follow, these two concepts are also distinguished from one another by referring to the unequal distribution of crime as the degree of aspatial concentration and to the clustering of high-crime places as the spatial concentration of crime. 4

18 Importantly, this dissertation uses each of the two terms which represent a single concept (i.e. clustering and spatial concentration) interchangeably. To illustrate the importance of the distinction between concentration and clustering, Figure 1 depicts neighborhood robbery rates in the cities of Houston, Texas and Columbus, Ohio. Here high-crime tracts are designated as those tracts which fall in the top 25% of all neighborhoods within the city. Although both Houston and Columbus have roughly the same number of highcrime tracts, as seen in Figure 1, in Columbus the vast majority of high-crime tracts are clustered in the center of the map, while in Houston they are more widely dispersed throughout the city. It seems reasonable to consider crime more spatially concentrated in Columbus than in Houston given that crime is not only unequally distributed across the neighborhoods, (i.e. some neighborhoods have much higher rates of crime than others), but also since these high-crime locations are also tightly clustered within space. Figure 1 illustrates a potentially meaningful limitation of past research devoted to the concentration of crime which has utilized methods that are unable to account for the different spatial patterns shown. Specifically, measures used in past research are capable of differentiating between general patterns in the distribution of crime, but do not include information on the spatial proximity of high-crime places to one another. Failing to account for the spatial clustering of high crime areas may lead to different conclusions regarding the true spatial concentration of crime. Two cities could have a similar distribution of crime across neighborhoods (i.e. the same number of highcrime areas that account for the majority of crime within the city), but face very different realities when it comes to the spatial concentration of crime due to variation in the spatial proximity of those high-crime places to one another. Accounting for the degree of clustering of these high-crime locations within space can therefore yield important insights regarding the crime problem in a given city, adding to our understanding of the importance of the criminology of place (Weisburd et al., 2012). To date, a systematic exploration of the concentration of crime, either spatial or aspatial, has yet to be conducted using a diverse sample of cities located across the U.S. In order to account for the unequal distribution of crime and spatial proximity of high-crime places to one another within space, in addition to the more traditional measure of crime concentration (the proportion of micro areas that account for the majority of crime within the city), this dissertation uses a second measure to represent the spatial concentration of crime. Specifically, it adapts a spatial version of the Information Theory index (H); a measure used in recent research on racial segregation. In their development of this spatial segregation index, Reardon and O Sullivan 5

19 (2004) suggest that segregation measures should recognize individuals true proximity to different groups in residential space. The measure H can be interpreted as the difference between the diversity of the larger area and the weighted average diversity of smaller units, expressed as a fraction of the total diversity of the larger area (Reardon and Firebaugh, 2002). As expanded upon in Chapter 4, this measure has recently been shown to be conceptually and mathematically superior to a second commonly used measure of evenness, the index of dissimilarity (D) (Reardon and Firebaugh, 2002; Reardon and O Sullivan, 2004). Like D, H provides a measure of how evenly a given group or class of objects is distributed across a given environment, but does so by comparing the proximity-weighted composition of a local environment with the composition of the larger area as a whole. Accordingly, the spatial version of the measure H represents an appropriate tool for measuring the unequal distribution of crime across neighborhoods in a given city, as well accounting for the proximity of high-crime neighborhoods to one another, therefore contributing significantly to our knowledge on the spatial concentration of crime in cities across the U.S Variation in the Degree of Concentration across Cities While the evidence that crime is concentrated in a very small number of places is persuasive, it is limited to a relatively small number of cities. Given the relatively limited information available from existing work, it is unknown whether such patterns of crime concentration are as invariant across places as implied in past research (Weisburd et al., 2012). To date, research has not systematically evaluated the possibility that variation in degree of crime concentration exists when a larger number of jurisdictions is considered. Additionally, past research has yet to examine the possibility that the concentration of crime varies by crime type. As elaborated below, different forms of crime are tied more closely to specific structural characteristics of a given area, with instrumental crimes being driven primarily by the distribution of criminal opportunities, thus it may be anticipated that the concentration of crime may vary according to the form of crime considered. At a minimum, there is limited empirical evidence which suggests that there may be meaningful variation across cities in the degree to which crime is concentrated. Using measures of concentration which focus on the unequal distribution of crime across micro-areas, past studies highlight the possibility that crime may be more tightly concentrated in some cities than in others. For example, in the city of Boston, Spelman (1995) found that it took 10% of the most crime ridden locations to account for 50% of the total volume of crime reported. In Lima, Ohio, it took 15.5% of the 650 census blocks in order to account for over half of the city s violent crime totals 6

20 (Ackerman and Murray, 2004). While either of these proportions represent a relatively high level of crime concentration, it suggests that crime is considerably less concentrated in Boston or Lima than in, for example, Minneapolis, where Sherman et al. (1989) observed that just 3% of addresses accounted for 50% of total city crime. The results from this handful of studies motivate the first research question of this dissertation: Does the concentration of crime vary across cities in the U.S.? In addition to the existing empirical evidence, there are a number of theoretical reasons to believe that variation in both concentration and clustering of crime exists across cities. A broad look at the literature on aggregate crime rates, both at the neighborhood- and city-levels suggests two key reasons why crime may be more highly concentrated in some cities than in others. First, previous research linking crime hot spots to theories of social disorganization, social control and opportunity theories highlights that a number of structural characteristics are relevant to where criminal activity is likely occur. Specifically, past research has shown the distribution of structural characteristics such as economic disadvantage, ethnic heterogeneity and population movement have the ability to impact the distribution of crime at the neighborhood-level, through their effect on the creation of motivated offenders and the degree to which those with higher propensities towards crime act on that propensity (Block and Block, 1995; Brantingham and Brantingham, 1984; 1993; Curtis, 1974; Rengert, 1980; Skogan, 1990). Accordingly, then, it may be anticipated that in cities where these criminogenic conditions are more unequally distributed (i.e. they are highly concentrated), high concentrations of crime are more likely to result. Furthermore, research on racial and economic segregation highlights a number of broad historical forces which have shaped the distribution and spatial concentration of high-poverty and/or minority neighborhoods within cities across America. For example, through their impact on the availability of affordable housing and low-skill job opportunities in many U.S. cities, deindustrialization and suburbanization during the latter half of the past century shaped, to a large degree, where the most impoverished segments of the population are likely to live. Similarly, historic levels of immigration to the U.S. during the same period are believed to have contributed to the concentration of poverty and increased levels of racial and ethnic segregation in destination cities across America. Prior research suggests that the degree to which cities were impacted by these sweeping socio-economic forces has contributed to between-city variation in the concentration of the conditions well-known to be associated with higher rates of crime. Additionally, although relatively unexplored in past research, there is reason to believe that the same forces (i.e. suburbanization, deindustrialization, and immigration) contributed to variation 7

21 in the spatial concentration or clustering of these criminogenic conditions. For example, where the conditions associated with crime are not only concentrated in a select number of areas within a given city, but those highly concentrated areas are clustered together within space, it may be anticipated that crime would be more spatially concentrated or clustered as well. As described in the following chapter, the literature which has explored the clustering of certain structural conditions remains extremely limited; however, there are reasons to believe that many of the same forces that lead to high levels of concentration may also lead to higher degrees of clustering in select cities, thus translating into higher spatial concentrations of crime. Second, city-specific approaches to crime and other social problems, such as local housing policy, the availability of public transportation, redevelopment programs and targeted policing tactics, may have an independent impact on both the concentration and clustering of crime. For example, housing policies designed to distribute the most disadvantaged sections of the population throughout cities, such as Section 8 and Moving to Opportunity (MTO) voucher programs, may also lead to the diffusion of the deleterious economic conditions associated with higher rates of crime. Other city-specific policies, such as targeted urban redevelopment or the use of hot spot policing tactics, also have implications for the concentration of crime. Therefore, considering what we know about the correlates of aggregate-level crime rates, it is possible to anticipate that a broader assessment of the distribution of crime, one that encompasses a relatively large and diverse sample of cities, may yield evidence of significant variation in both the spatial and aspatial concentration of crime, observed at the city-level. Existing research on the concentration of crime is also limited in that it has not considered the potential for the concentration of crime to vary by crime type. The majority of the studies cited above did not explore the degree of concentration for different forms of crime. Instead, the data utilized to date has included information on overall crime or calls for service. The one exception is an analysis of robbery, burglary and assault incidents in Cincinnati. Grusbesic and Mack (2008) found that these three crime-types possess their own unique spatial-temporal signatures. Although authors mention that the spatial and temporal patterning of crime vary according to crime type, they did not make any direct reference to their concentration as it has been used in past research. Failing to assess the concentration for specific forms of crime is a potentially important limitation because there are compelling theoretical reasons to believe that significant differences exist. These differences are anticipated to be the largest between instrumental and expressive forms of crime. Instrumental crimes include those which are committed in the pursuit of material gain (i.e. 8

22 robbery and burglary), while expressive crimes are committed in the context of an argument, lover s quarrel, or fit of rage (i.e. homicide and assault) (Maume and Lee, 2003). Existing research suggests that the conditions that shape the spatial distribution of each form of crime vary according to crime type, with instrumental crimes being driven primarily by the distribution of criminal opportunities, while expressive crimes may be more closely tied to levels of social control and value systems which encourage the use of violence to settle disputes (Anderson, 1999; Morenoff et al., 2001; Rosenfeld, 2009). The distinct etiology of instrumental and expressive crimes suggests that in addition to variation across cities, it is plausible that the degree to which crime is concentrated may vary by crime type. For example, some cities may offer a wide range of criminal opportunities which are distributed relatively equally across the city, while at the same time subcultural values which promote violence may exist only in a small number of areas. In this situation, one may anticipate a measure of concentration, either spatial and aspatial, to be much higher for violent crimes when compared to property crimes in the same city. Given the limitations of past research, this dissertation contributes to the literature on the concentration of crime by being the first to systematically explore both the spatial and aspatial concentration of crime, disaggregated by crime-type for a large sample of cities across the U.S Potential Consequences of Variation in Crime Concentration What is more, the degree to which crime is concentrated, either spatially or aspatially, may have implications for explaining city-level differences in the volume of crime. Prior research has shown that crime rates tend to be higher in cities with larger total populations, larger youth and Black populations, and higher levels of family instability and resource deprivation (Bursik and Grasmick, 1993; Cantor and Land, 1985; Liska and Bellair, 1995). More recently, between-city differences in crime have also been linked to levels of crack involvement, the use of proactive policing tactics, and levels of immigration (Baumer et al., 1998; Kubrin, Messner, Deane, McGeever and Stucky, 2010; Stowell, Messner, McGeever, and Raffalovich; 2009). As elaborated in the third chapter, a broad look at epidemiological research on the spread and containment of disease, as well as past research on the potential contagious nature of violence, suggests that the concentration of crime within a given city may have an independent effect on the total volume of crime observed at the city-level. The reciprocal nature of expressive violence, the use of violence to enforce social control when lawful alternatives are unavailable, as well as the development of a subculture that promotes the use of violence more generally, imply a causal relationship between the concentration 9

23 of criminal activity and city-level crime rates. Specifically, a more diffuse spatial patterning of crime (low levels of concentration) may lead a larger proportion of a city s population to perceive crime as a major problem and because it is may be more difficult to contain through formal social controls, may translate into higher rates of violence through its impact on perceived threat. Perhaps because of the dearth of comparative research in this area, prior research on city-level crime has not evaluated this possibility. It is also possible that the effects described above are contingent on the degree of clustering that crime exhibits. That is that the aspatial concentration of crime may or may not have an impact on the total volume of crime, but once the degree of clustering is accounted for, a significant effect will be observed. For example, it could be anticipated that in cities where crime is more widely distributed across space (i.e. low levels of spatial concentration) that mechanisms described in Black s (1983) theory of self-help, and Anderson s (1990) code of the street, become prevalent because a larger proportion city residents are likely to be exposed to crime within their frequentlyaccessed areas. Additionally, high-crime areas which are tightly clustered within the city may allow police to more effectively employ hot-spot policing strategies making it more likely that their interventions will impact rates of crime through a diffusion of crime control benefits. Accordingly, this dissertation contributes to the literature by examining the extent to which variation in either the aspatial or spatial concentration of crime is associated with the between-city differences in crime. 1.2 The Current Study Although the evidence from existing research studies is highly suggestive, the conclusion that has been arrived at by some (Weisburd et al., 2012), the concentration of crime is invariant across cities, is based on insights from a very small number of cities that may not be highly representative. Furthermore, past research has failed to account for the spatial distribution of high-crime micro areas in any systematic way, nor have scholars considered the potential consequences of varying degrees of crime concentration. Thus, what remains unclear is whether these patterns hold when a large and diverse sample of cities is examined and whether varying degrees of concentration are associated with the between-city differences in crime. Without a more comprehensive and comparative research approach, one which assesses the potential variation in the level of crime concentration across a large number of cities, the contribution to criminological theory and resulting policy recommendations remain on unstable ground (Weisburd et al., 2012, p. 52). 10

24 The current work hopes to push research in this area forward and heed the call made by leading researchers in the area for replication and expansion, in order to build on the criminology of place and aggregate-level research more generally (Weisburd et al., 2012, p. 193). Specifically, this dissertation contributes to the literature by examining the concentration of criminal events, disaggregated by crime-type, for a relatively large sample of cities across the U.S. As stated above, the current research is centered around two central research questions: (1) Does variation in the degree to which crime is concentrated (spatially or aspatially) exist across cities? (2) Does variation in the concentration and clustering of crime have a significant impact on the overall volume of crime in a given city? To answer these questions, the current project uses two measures of crime concentration. The first measure, the proportion of neighborhoods that account for the majority of crime within the city, represents the unequal distribution or aspatial concentration of crime as it has been used in past research. The second, the spatial version of Information Theory H, incorporates information regarding the unequal distribution of crime as well as the spatial clustering of high-crime places to one another within space (i.e. spatial concentration). This dissertation contributes to the body of literature on the concentration of crime by highlighting the between-city differences that may lead to variation in the concentration of crime (both spatial and aspatial), documenting observed differences in the concentration of crime across a large sample of cities, and assessing the implications of city-level variance in crime concentration for city-level differences in the overall volume of crime. As described in detail in Chapter 2, there are a number of theoretical reasons to anticipate that the concentration of crime does in fact vary across American cities and that the historical and structural characteristics unique to U.S. cities have contributed to this variability. Chapter 3 discusses the theoretical perspectives of self-help and the contagion of crime, and draws a number of connections to epidemiological literature on the spread or containment of disease, highlighting the potential for the concentration of crime (aspatial or spatial) to have an independent impact on city crime rates. Using this review of the literature as a launching point, the current project will be one of the first studies to assess the degree of variation present in concentration and clustering of crime across a sample of U.S. cities and to assess its potential to impact city crime rates. In the fourth chapter, the data and empirical methods used in this project are described in greater detail. Utilizing tract-level crime data collected for 91 cities from the National Neighborhood Crime Study (NNCS), this project assesses the variability in and impact of the two measures of crime concentration described above. Following the data and methods chapter, results which address the 11

25 two major research questions are presented in Chapter 5. This dissertation ends with a discussion of the implications which stem from the current project for both aggregate-level research and crime policy more generally. Figure 1: The Spatial Concentration of Robbery in Houston, TX and Columbus, OH. 12

26 CHAPTER TWO ANTICIPATING VARIATION IN CRIME S CONCENTRATION 2.1 Introduction While the unequal distribution of crime across space has long been of interest to social researchers, relatively little empirical research has been devoted to systematic assessments of the tendency for a small number of places to account for a very large proportion of the total crime problem. Of the research conducted to date, both cross-sectional and longitudinal studies of the distribution of crime at small levels of aggregation have found evidence that that crime is strongly coupled to a small number of micro-areas within a given city (Brantingham and Brantingham, 1993; Groff et al., 2010; Weisburd et al., 2004; Weisburd et al., 2012). The striking concentration of crime has been referenced several times in recent aggregate-level work. Importantly, however, research has not systematically evaluated whether variation in the concentration of crime exists across cities, nor have the measures utilized in past studies accounted for the spatial proximity of these high-crime areas to one another, referenced in this study as spatial concentration. As suggested in the introduction, there are compelling theoretical reasons to believe that variation in the concentration of crime (both spatial and aspatial) may exist once a large sample of cities is considered and that this variation may also contribute to the between-city differences in crime. This dissertation adds to the literature on the concentration of crime by examining two primary research questions. First, does the concentration and clustering of crime vary across cities? Second, is variation in the concentration of crime (either spatial or aspatial) associated with the between-city differences in criminal activity, net of other factors know to contribute to city crime rates? The current chapter lays out the theoretical reasons to believe variation in the concentration of crime may exist, while Chapter 3 discusses the potential impact of crime s concentration on the total volume of crime within a given city. As mentioned in the introduction, although evidence of crime s dramatic concentration is compelling, existing research suggests that meaningful variation in the degree to which crime is concentrated may exist across cities. To date, this is something that has not been examined empirically. In addition to the evidence from crude comparisons using strictly distributional measures from a handful of cities, there are also a number of theoretical reasons to believe that the concentration of crime varies significantly from one city to the next. Additionally, once the spatial 13

27 dimension of concentration (i.e. the clustering of high-crime places within space) is taken into account, different patterns may emerge. Furthermore, insights gleamed from past research also suggest that both the spatial and aspatial concentration of crime is likely to vary by according to crime type (Groff and McEwen, 2006; Grusbesic and Mack, 2008). A long tradition of research on aggregate crime rates has shown that the distribution of certain forms of crime are a result of distinct conditions or circumstances, with instrumental crimes being tied most closely to the distribution of criminal opportunities. To date, research on the concentration of crime has not investigated whether different forms of crime follow different patterns of concentration. By taking a broad look at the literature on aggregate crime rates, both at the neighborhoodand city-level, as well research on other major socioeconomic shifts which have occurred in cities across the U.S., a number avenues by which crime may become more highly concentrated in some cities than in others become apparent. First, a long line of criminological research has shown the distribution of structural characteristics such as economic disadvantage, ethnic heterogeneity and certain kinds of criminogenic places have the ability to impact the distribution of crime at the neighborhood-level, through their effect on the creation of motivated offenders and the degree to which those with higher propensities towards crime act on that propensity (Block and Block, 1995; Brantingham and Brantingham, 1984; 1993; Curtis, 1974; Rengert, 1980; Skogan, 1990). Accordingly, then, it may be anticipated that in cities where these conditions associated with crime are more unequally distributed (i.e. they are highly concentrated), higher concentrations of crime are more likely to result. Coupling this proposition with the fact that past research on racial and economic segregation has long established the segregation of poverty and different racial and ethnic groups exists in cities across America, suggests that the concentration of crime may vary accordingly. Research on residential segregation and the concentration of poverty suggests that broad social forces such as deindustrialization, suburbanization and immigration, coupled with local factors such as city-age and geography, and the availability of public transportation and housing market characteristics, have led to sizable between-city differences in the distribution of wealth and different racial and ethnic groups within American cities. The impact of deindustrialization and suburbanization on the distribution of conditions associated with crime can be explained in part by their effect on urban form. The traditional model of urban form is that of the monocentric city, pioneered by Alonso (1964), Muth (1969) and Mills (1972). This model includes a familiar image of a disc-shaped city in which the central business district is located in the center of the urban area surrounded by residential areas of varying characteristics. It has become apparent that over the past 14

28 several decades that the structure of many cities has departed from the monocentric model and that deindustrialization and suburbanization have led to clusters of economic activity dispersed throughout cities rather than predominately in the central business district. Urban planners have highlighted that no city is ever 100% monocentric, and that seldom is it 100% polycentric (i.e. with no discernible downtown ). Some cities are dominantly monocentric, while others are mainly polycentric and many are in between. Research also suggests that some city-specific conditions tend to accelerate the change toward polycentricity including: a historical business center with limited amenities, a high proportion of car ownership, cheap land outside the city center, a flat topography and a grid street design (Bertaud, 2004). Other conditions tend to keep cities more monocentric including: a city center with a large number of amenities, rail-based public transportation systems, a radial primary road network and difficult topography which impedes travel between suburbs. It is easy to imagine cities of each type here within the U.S. For example, compare the older, denser, predominately monocentric cities of the Northeast (i.e. Philadelphia, Pittsburgh, and Columbus) to the sprawling, more polycentric cities in the South and Southwest (i.e. Houston, Jacksonville, Phoenix and Los Angeles). Although the historical and topographical conditions listed above have an impact on the degree to which a city becomes polycentric, broad socioeconomic changes such as deindustrialization and suburbanization have also impacted urban form. Additionally, the availability of public transportation and housing market characteristics unique to each city dictate the impact of changes in urban form on the distribution of structural conditions related to crime. A tremendous body of research devoted to understanding the pronounced spatial isolation of certain groups suggests that the high degrees of racial and economic segregation present in urban areas across the U.S. are a result of several factors. First, broad social and economic forces such as the retreat from manufacturing and the increased suburbanization of large cities across the country have led to sizable between-city differences in the distribution of wealth and different racial and ethnic groups. Second, these large scale transformations have been coupled with city-specific conditions such as city-age and geography, the availability of public transportation, and housing market characteristics resulting in many cases in large divisions along racial and economic lines. Much of the remainder of this chapter focuses on how changes in urban form and other economic and political actions affect where certain groups of people live and how likely they are to succeed financially. As will become apparent, the transition from monocentric to polycentric urban design has led, in some areas, to large divisions based on race and social class. As cities transform from being predominately 15

29 monocentric to being varying degrees of polycentric, several between-city differences have translated into variation in the distribution of certain population groups. Specifically, variation in housing policy, the actions of economic and political elites, and the state of public transportation infrastructure have impacted the degree to which certain groups are isolated by race and social class. As will be suggested below, older, traditionally monocentric cities which have changed over time may see large clusters (i.e. spatially concentrated areas) of economically disadvantaged residents form as a large percentage of jobs move from the central business district to the outskirts of the city or nearby suburbs. Whereas in cities which are more polycentric, clusters of certain groups of people (i.e. impoverished or of a certain race or ethnicity) may emerge for a number of other reasons, yet may be dispersed throughout the city, resulting in high concentrations, yet lower degrees of clustering. Thus, there are reasons to believe that the both the concentration and clustering of criminogenic conditions (i.e. poverty, unemployment and racial heterogeneity) as well as crime generating places (i.e. bars, liquor stores, transit hubs and other risky facilities ) may vary from cityto-city. Section 2.3 and its subsections explore these pathways in greater detail, suggesting it is possible to anticipate variation in the concentration and clustering of crime once a larger, more diverse sample of cities is considered. Prior to discussing the past research and theory that supports the idea that the concentration of crime varies from city-to-city, it is important to again clarify some terminology making it possible to apply the results of past research to the current study. Much of the research discussed in the following sections comes from the literature surrounding racial and economic segregation. Over the past several decades, parallel bodies of research devoted to modeling the residential segregation of minority members and the concentration of poverty in urban areas have emerged (for example Massey and Denton, 1988; 1993; Jargowsky, 1994; 1997; Lee; 2000; Stretesky, Schuck and Hogan, 2004). High levels of segregation, as used in those bodies of research, often parallels what people have come to mean by the concentration of crime as used in the crime literature. For example, what has emerged as the most common measure referenced in discussions of the concentration of poverty (e.g. economic segregation), the total percentage of poor population which resides in highpoverty tracts, mirrors that of the concentration of crime used in prior literature. Other measures of segregation which have commonly been used in past research capture what Massey and Denton (1988) describe as evenness. Evenness refers to the degree to which members are over- or underrepresented in local areas in comparison to their proportion of the population in the larger area, that is, evenness is a measure of their distribution. For that reason, in the sections that follow, a 16

30 distribution of a given trait (i.e. poverty) and its concentration should be considered synonymous with one another. Specifically, if a given trait is more unevenly distributed, it would also be considered more highly concentrated. This is an important point as these two terms are used interchangeably in the sections that follow. Secondly, and perhaps more importantly, this dissertation highlights the potential importance of accounting for the proximity of certain local areas (i.e. high-crime or high-poverty) to one another within space. In the introduction this was termed clustering or spatial concentration, which again draws from the literature on racial and economic segregation. Massey and Denton (1988) define clustering as the extent to which predominately minority areas are in close proximity to one another within space and designate it as a separate dimension of segregation. For example, certain groups (i.e. blacks) might be highly segregated in a given city, but those neighborhoods with a high concentration of black residents may be dispersed throughout the city. This would represent a high level of concentration without a high degree of clustering. In contrast, under conditions of racial segregation it is also possible that most predominately black neighborhoods are constrained to a small number of spatially proximate neighborhoods, which would yield a city with high levels of racial segregation and racial clustering. During the 25 years since Massey and Denton s influential work, advances in GIS technology have opened up new avenues for the modeling of the distribution of groups in a spatial context. Recently Reardon and O Sullivan (2004) suggested the distinction between evenness and clustering made by Massey and Denton (1988) is an artifact of the reliance of spatial subareas (e.g. census tracts of block groups). Reardon and O Sullivan (2004) suggest that if a measure of segregation took into account the exact location and proximity of these groups to one another within space, the distinction between evenness and clustering would be irrelevant. That is, a single measure could account for both the distribution of a given characteristic (its evenness) as well as its degree of clustering within space. This measure, which accounts for both the (un)evenness of a distribution, as well as the degree of clustering present is what I have referred to as spatial concentration. Importantly, the vast majority of research on the determinants of racial and economic segregation discussed below has focused on the influence of factors such as deindustrialization and suburbanization within an aspatial context. In fact, only a handful of studies in this area have incorporated information on spatial proximity of these local areas to one another (Reardon and Firebaugh, 2002; Reardon and O Sullivan, 2004; Lee et al., 2008). For that reason, the connections drawn between the determinants of racial and economic segregation and the potential for variation 17

31 in the concentration of crime to exist between cities, is most relevant to aspatial measures of concentration. Due to the dearth of literature which has compared the outcomes of research utilizing spatial measures, it becomes difficult to distinguish those factors thought to impact one form of concentration (aspatial) for those which may impact both (spatial and aspatial). However, there are reasons to believe the processes described throughout this literature may actually be spatial in nature and therefore explain both the unequal distribution and clustering of certain phenomena. For example, the literature which examines the impact of the deindustrialization and suburbanization of cities across America has referenced the emptying of inner-cities (Wilson, 1987). In monocentric cities, those with a distinct central business district, the emptying of the city center may very well describe a large cluster of contiguous areas in the heart of the city which are disproportionately impacted by these processes. Considering this, I argue that the same list of factors thought to impact the distribution of the conditions associated with crime, may also influence the degree of clustering present in those conditions, especially in older, monocentric cites. Throughout section 2.3 I highlight that the processes referenced in the literature may actually be referencing a spatial process although researchers have failed to measure it in an explicitly spatial fashion. Before discussing the factors that are thought to impact the distribution and clustering of criminogenic conditions in cities across the country, Section 2.2 reviews the body literature on structural characteristics and crime generating or crime attracting places which have consistently been connected to rates of crime at the aggregate (i.e. neighborhood) level. Section 2.3 goes on to describe how deindustrialization, suburbanization, and immigration have collided with between-city differences in affordable housing, public infrastructure and local policy, giving rise to variation in urban form and the concentration and clustering these of conditions believed to impact rates of crime, suggesting variation in the concentration of crime exists across cities. 2.2 Ecological Conditions Known to Impact Crime In order to anticipate how the concentration of crime may vary from one place to another, it is necessary to consider how the uneven spatial distribution of criminal activity observed in past research is likely to have emerged. Crime is a relatively rare event, one in which an individual with some propensity towards crime comes into contact with a potential victim or target in a situation sufficient to allow them to engage in crime (Brantingham and Brantingham, 1993; Sherman, 1995). This suggests that crime is a result of a specific combination of conditions that may only be found in 18

32 select locations. Previous research linking crime hot spots to theories of social disorganization, social control and opportunity theories highlights that the distribution of a number of conditions are relevant to where criminal activity is likely occur. Specifically, research has shown the distribution structural characteristics such as economic disadvantage, ethnic heterogeneity and the existence of crime attracting or crime generating places, have the ability to impact rates of crime at the neighborhood-level, through their effect on the creation of motivated offenders and the degree to which those with higher propensities towards crime act on that propensity (Block and Block, 1995; Brantingham and Brantingham, 1984; 1993; Bursik and Grasmick, 1989; Curtis, 1974; Rengert, 1980; Skogan, 1990). Importantly, the distribution and spatial proximity of these conditions shown to be associated with crime, as well as the movement of offenders and victims, is not random. Historical as well as contemporary factors have shaped how cities across the country have developed over time, presumably leading to a great deal of variation in the concentration of conditions known to be associated with crime. The remainder of this section (2.2) reviews aggregate-level research on the structural characteristics which have been associated with the distribution of crime at the neighborhood-level. Section 2.3 goes on to suggest why the concentration and clustering of these conditions may vary across cities, contributing to variation in the concentration of crime The Impact of Economic Deprivation Central to a number of criminological theories is that disadvantage fosters high levels of crime. Tenants social disorganization theory, Wilson s isolation theory and Anderson s work on the subculture of violence, propose the detrimental consequences of poor economic conditions. Economic disadvantage may lead to increased levels of crime through the development of a population with a higher propensity towards crime, or by providing opportunities in which those with higher propensities towards crime are more likely to act. Salient to the distribution of motivated offenders, Wilson (1987) argues the effect of poverty on violent crime can be explained through its effects on social isolation and the disruption of interpersonal and organizational networks. He and others suggest that economic segregation, resulting from the decline in manufacturing and the concentration of poverty, has far reaching impacts on poor communities as it deprives them of both economic resources (police, schools, churches, and businesses) as well as depletes the image of conventional norms and values. As a result, residents of socially isolated and poverty-stricken neighborhoods may be more likely to develop strong propensities to engage in nonconventional behavior, leading ultimately to high rates of crime in these areas. 19

33 In addition to the ability of high levels of economic disadvantage to contribute to a population with a higher propensity towards crime, disadvantage can also contribute to higher rates of crime by making it more likely that those with a high propensity towards crime will act. Drawn from the long tradition of social disorganization research, it is known that economically disadvantaged areas are often plagued high levels of residential turnover, and the associated instability has been shown to inhibit the formation of social ties, thus reducing the ability to maintain effective levels of social control (Boggess and Hipp, 2010; Bursik, 1989; Sampson and Groves, 1989). Due to this lack of attachment, disadvantaged communities have a diminished capacity to control behavior through a number of channels including, the supervision of teenage groups, informal surveillance, and the minimization of physical disorder (Bellair, 1997; Sampson and Groves, 1989; Bursik and Grasmick, 1993; Cohen and Felson, 1979). These areas, plagued by low levels of informal social control, make it more likely that those with strong propensities towards crime and violence will act (Sampson et al., 1997). Studies devoted to this topic have generally found that neighborhoods with higher levels of poverty are victim to higher rates of crime (Crutchfield, 1989; Hipp, 2007; Krivo and Peterson, 1996; Warner and Rountree, 1997) The Impact of Racial and Ethnic Heterogeneity A second structural characteristic, racial heterogeneity has long been considered an important predictor of crime within the social disorganization paradigm (Bursik and Grasmick, 1993; Kornhauser, 1978; Sampson and Groves, 1989; Shaw and McKay, 1942). Racial heterogeneity is thought to contribute to diminished levels of social control through its effect on social ties (Warner and Rountree, 1997). Due to communication and identification barriers associated with different races, as well as high rates of population turnover, areas which are more heterogeneous in terms of racial composition are thought to be less cohesive and suffer from lower levels of social control. Weak ties resulting from high levels of heterogeneity limit the ability of residents to agree on common set of values or to solve community problems (Bursik, 1986; Kornhauser, 1978). The traditional measure used in research, the percentage of black residents, has been shown to be a consistent predictor of elevated rates of crime (Blau and Blau, 1982; Chamlin, 1989; Land et al., 1990; Messner, 1982; Warner and Pierce, 1993; Warner and Rountree, 1997). Importantly however, scholars have pointed out that a measure such as the percent black may be too simplistic due to the changing nature of the population composition of the U.S. Evidence from census data supports the notion that America is becoming increasingly diverse. More recently, researchers have 20

34 employed a more complete measure of population heterogeneity which includes information on white, black, Hispanic and Asian population characteristics (Kubrin, 2000; Smith, Frazee, and Davidson; 2000; Weisburd et al., 2012). Results from these studies highlight that several parallels between immigrant enclaves and minority ghettos exist. Both ethnic enclaves and traditional black ghettos exhibit a prevalence of cheap and densely populated housing stock, are commonly located within the inner city, and suffer high rates of poverty (Logan, Zhang and Alba, 2002). Accordingly, it has been argued that as Asians and Hispanics enter communities where recent immigrants are more numerous, the resulting social disorganization may, in some cases, lead to higher rates of crime in those areas. Overall, results from numerous suggest that racial heterogeneity has a significant impact on rates of crime at the neighborhood level (Bellair, 1997; Rountree and Warner, 1999; Sampson and Groves, 1989; Warner and Roundtree, 1997) The Impact of Unemployment A third structural condition which has been subject to a great deal of criminological research is unemployment. Although clearly tied to the economic well-being of a given area, the effect of unemployment on crime has generally been considered to be distinct from that of rates of poverty. Connections have been drawn between unemployment and crime using a number of criminological theories outside of social disorganization, including strain, anomie and routine activities theory. While several connections between unemployment and crime have been drawn in the past, research has failed to produce any clear consensus on the relationship between the two (Box, 1987; Chiricos, 1987). In an attempt to clarify these inconsistencies, Cantor and Land (1985) proposed a complex model describing the potential for unemployment to impact crime in a number of ways. Specifically, Cantor and Land suggest that unemployment may impact crime in two countervailing ways, namely through their effects on motivation and guardianship. These counteracting effects have been the subject of much debate within aggregate-level research (Arvanities and DeFina, 2006; Britt, 1997; Chiricos, 1987; Greenberg, 2001; Hale and Sabbagh, 1991; Phillips and Land, 2012). In relation to motivation, stemming from arguments most salient to strain and anomie theories, individuals who have been excluded by the formal labor market may be more likely to pursue illegitimate opportunities in order to meet their financial needs. Agnew (1999) suggested that community characteristics have the ability to affect levels of strain by affecting the likelihood of residents failing to achieve positively valued goals, losing positively valued stimuli, and experiencing negative stimuli. These elevated levels of strain are then expressed through anger and frustration. 21

35 Agnew suggests that in neighborhoods with greater proportions of strained residents, they are more likely to interact with one another in a way which leads to aggression and violence. It is the environment s impact on the distribution of opportunities which produces a high proportion of strained individuals who are more likely to engage in acts of crime and violence. Agnew does, however, suggest that the extent to which levels of strain leads to crime is moderated by several things, including levels of social control and social capital within the community. So although unemployment has the ability to influence crime directly, its effect is often contingent on a variety of factors. Conversely, related to the tenants of routine activities theory, people who are not employed and therefore remain around their residence, may represent capable guardians of an area as homes are no longer left unprotected when residents leave for work. Here, as Cantor and Land (1985) suggest the association between unemployment and crime might actually be negative. Routine activities theory itself, however, also suggests the potential for differing effects of employment/unemployment on crime. Specifically, it has been suggested that quantifying the number of people who work in a given area is an important element of the understanding criminal opportunities (Brantingham and Brantingham, 1995). On one hand, people who work in a given areas and the possessions they bring with them, may represent suitable targets for motivated offenders. One the other hand, however, this presumably positive effect may be counteracted due to the fact that more people in a given area may also represent higher levels of guardianship. Cantor and Land s formulation of the impact of unemployment on crime has stimulated a significant amount of subsequent research. Though most research on unemployment and crime has concluded that a positive relationship exists between unemployment and crime, and that this relationship is stronger for property than for violent crime, in a review of sixty-three empirical studies Chiricos (1987) found results were mixed due to the use of a wide range of statistical methods. Greenberg (2001) also laments issues of misspecification and the possibility of many past studies to suffer from serious methodological deficiencies. In perhaps one of the most comprehensive studies on the unemployment-crime relationship, using panel data at the neighborhood-level Andresen (2012) confirmed that in the long-run, neighborhoods with relatively higher rates of unemployment have higher rates of crime within their borders. Phillips and Land s (2012) recent analysis also provides strong support for the Cantor and Land (1985) model. Specifically they found that in 78 of 84 empirical tests, the direction of the relationship was as 22

36 predicted by theory and that the effects of motivation were strong for property crimes than for violent crimes, as would be expected. Although it is apparent that a highly complex relationship between unemployment and crime exists, there are reasons to believe that its distribution has important implications for the distribution of crime. First, high rates of unemployment have the potential to influence the development of strong propensities towards crime through status frustrate and increased levels of strain, and second, unemployment s distribution can also affect the likelihood that people with high propensities act on that propensity due to its effect on criminal opportunities. Thus, considering the city-specific factors which give rise to different degrees of concentration and clustering of unemployment should lead to some interesting insights on how the concentration of crime (both spatial and aspatial) may vary from one city to the next The Existence of Crime Generators and Crime Attractors In addition to the measures of economic disadvantage and racial heterogeneity discussed above, routine activities theory and variants of it such as crime pattern theory have stressed the impact of the physical environment on how potential victims and offenders move across space; highlighting the places they are likely to intersect. The tight coupling of criminal incidents to specific types of places parallels findings from previous research showing that crime hot spots are often not larger than a street segment (Weisburd et al., 2004), street corner (McCord and Ratcliffe, 2007), or even a single address (Sherman et al., 1989). Using insights from these ecological theories of crime causation it can be argued that the concentration or spatial clustering of places which attract or generate crime, have the ability to shape the patterning of crime across American cities. In their explanation of crime pattern theory, Brantingham and Brantingham (1995) discuss the existence of special types of places, which they label crime attractors and crime generators. The Brantinghams and others have noted that these pockets of crime are often located near nodes of activity such as bars, fast-food outlets, pawn shops and liquor stores (Block and Block, 1995; Bernasco and Block, 2011; Ford and Beveridge, 2004; LaGrange, 1999; McCord and Ratcliffe, 2007). It has been suggested that these places, in combination with the natural movement of individuals, and the underlying structural characteristics of the area, contribute to high rates of crime in the certain areas. Brantingham and Brantingham (1999) define crime generators as specific locations or land uses to which large numbers of people are attracted for reasons unrelated to crime (shopping 23

37 centers, entertainment districts, office plazas, large housing complexes, sports stadiums, etc.). They may also be locations where major paths of transit converge such as bus stations, major intersections or transit systems stops (Beavon, Brantingham and Brantingham, 1994; Bernasco and Block, 2011; Block and Block, 1999; Johnson and Bowers, 2010). The commonality here is that any of these places have a large number of people that converge or pass-through them on a given day, thus contributing to the availability of suitable targets and the existence of motivated offenders. An important distinction between crime generators and crime attractors is that crime generators represent locations where potential offenders find themselves, and although they did not have an explicit intent on participating in a crime, they exploit opportunities presented at that location. That is, crime generators are places which make it more likely that those with a strong propensity to commit crime will do so given the availability of suitable targets and the absence of capable guardians. Thus, these places may become hot spots for criminal activity simply because of the volume social activity in the area. Crime attractors on the other hand, are particular places which are well-known to create opportunities to commit crime. These places attract motivated offenders because they are known to be areas which are conducive to specific forms of crime. Crime attractors do not necessarily bring together large groups of people at the same time, but their function makes them prime locations for motivated offenders to find attractive and poorly guarded victims or targets (Bernasco and Block, 2011). It has been suggested that places such as entertainment or shopping districts may have a high concentration of criminal opportunities due to the fact that people who travel to them are likely to have cash on-hand, representing prime targets for motivated offenders to capitalize on (Cohen and Felson, 1979; Frisbie et al., 1978; Wright and Decker, 1997). Research on offenders decisions echo the idea that offenders often actively seek out locations which are likely to have concealable, removable, available, valuable, enjoyable and disposable goods which are easy to steal (Bernasco, Block, and Ruiter, 2013; Clarke, 1999; Wellsmith and Burrell, 2005). Places such as bar districts, grocery stores, drug markets, check cashing businesses, and gas stations also represent potential target locations. The described attraction is created by an ecological label, which signals to would-be offenders that a particular location is good place to go in order to commit a certain form of crime. Accordingly, ecological labels draw motivated offenders to the area because they are well-known to provide lucrative opportunities to engage in crime. A substantial amount of research has been devoted to the study of types of places which attract or generate crime. The majority of research in this area has focused on the effects of 24

38 particular types of place on crime in the surrounding community. For example, research has shown that public facilities such as parks, libraries, community centers and schools play a key role in attracting suitable targets (Cromwell, Alexander, and Dotson, 2008; Roman, 2002; 2005; Roncek and Faggiani, 1985; Wilcox, Quisenberry, Cabrera and Jones, 2004). Similarly, the existence entertainment districts, and shopping outlets have also been shown to shape the distribution of crime within a given city (Block and Block, 1995; Roncek and Maier, 1991; Wirth, 1938). Empirical work in this area has assessed the impact of crime generating places in a number of ways. Researchers have examined how social structure and the distribution of crime generating places shape the opportunity for crime to occur across geographic units such as city blocks (Rountree, Land and Meithe, 1994; Wilcox et al., 2004), census blocks (Bernasco and Block, 2011) and street segments (Weisburd et al., 2012). Others have focused on the location of facilities using threshold techniques to examine the distribution of crime without accounting from structural characteristics (Groff, 2011). Similarly, using data on a wide range of locations (i.e. bars, clubs, gas stations, pawn shops, laundromats, and grocery stores) to measure the attractiveness of a given location to robbers in the vicinity, Bernasco and colleagues (2013) showed that the presence of legal cash economies and small scale retail activities accounted for a sizeable proportion of the robbery incidents which occurred in the city of Chicago. In addition to the impact of specific types of places on crime, evidence from past research also suggests residential areas that include land devoted to nonresidential uses, regardless of the type, may experience higher rates of crime. The potential for land use decisions to be associated with rates of crime has a long history within criminological research (Jacobs, 1961; Newman, 1972; Shaw and McKay, 1942). The tenants of a number of criminological theories suggest that densely populated, highly-developed, mixed land use areas have the ability to generate higher rates of crime. From a routine activities perspective, areas of mixed land use represent places where there may be an increased number of suitable targets, therefore drawing motivated offenders to the area in order to commit crime (Felson, 2002; Gardiner, 1976; Cohen and Felson, 1979). Alternatively, the tenants of social disorganization theory suggest that because of the heterogeneous nature of mixed land use areas, residents are less likely to form strong ties and therefore are unable to maintain the level of social control necessary to prevent different forms of crime from occurring (Roncek, 2000; Kurts, Koons, and Taylor, 1998; Taylor, 1997; Wilcox et al., 2004). It has also been suggested that mixed land use reduces residents ability to differentiate between locals and outsiders, therefore inhibiting the use of informal social control (Gardiner, 1976; Taylor et al., 1995; Wilcox et al., 2004). Finally, 25

39 related to levels of disorder, nonresidents passing through areas for other reasons may litter, or otherwise contribute to the deterioration of high-traffic areas, has also been shown to impact rates of crime (Taylor et al., 1995). Each of these theoretical viewpoints would suggest that these types of areas may suffer from higher rates of criminal activity. On the other hand, Jacobs (1961) highlights the potential positives of concentrated, diverse neighborhoods. She argues that mixed land use promotes healthier blocks because they are constantly in use and thus have denser informal control networks. In her view, densely populated, mixed use neighborhoods, draw individuals onto the street creating higher levels of informal social control of public space due to the steady stream of eyes on the street (Jacobs, 1961). Accordingly, one may expect densely populated, walkable communities to experience lower levels of crime. The distribution of mixed land use has been shown in past research to influence crime rates in the surrounding communities (Fowler, 1987; Jacobs, 1961; Sampson and Groves, 1989). For example, Lockwood (2007) found that both commercial and public land uses were significantly associated with rates of assault and robbery across 145 census blocks in Savannah, Georgia. Other empirical research, however, has demonstrated the potential for densely settled, walkable communities in which residential and commercial properties are interspersed, to sustain higher levels of neighborhood cohesion and therefore resist crime (Browning et al., 2010; Calthorpe and Fulton, 2001; Sampson and Raudenbush, 1999). Specifically, Browning and colleagues (2010) found that at low levels, increasing commercial and residential density is positively associated with homicide and assault. Beyond a threshold, however, increased commercial and residential density was seen to reduce the likelihood of crime, suggesting a non-linear relationship may exist. Recent studies have also argued the effects of land uses on crime are likely to be conditioned by the socioeconomic characteristics of the area (Smith, Frazee, and Davison, 2000; Wilcox et al., 2004). Authors suggest that since economic disadvantage is commonly associated with lower levels of informal social control, then it is likely that the crime-generating potential of land uses will likely vary depending on the level of disadvantage in a given area. Consistent with this reasoning, Smith, Frazee and Davidson (2000) found that the influence of commercial outlets on robbery was greater in areas with a high proportion of single-parent households. Similarly, Wilcox et al. (2004) examined the associate between commercial, industrial, and residential land use and crime across 100 Seattle neighborhoods. The authors found that the effects of certain types of places were conditional upon the relative level of population instability. Finally, Stucky and Ottensmann (2009) found that several specific land uses were related to violent crime, whereas some others were not, 26

40 and that this effect was conditional on socioeconomic characteristics of the area. Specifically, authors found that the effect of busy roads, high-density residential units, and commercial outlets on crime was dependent on the level of disadvantage in the focal area. Overall, existing research suggests the distribution of particular kinds of land use and specific types of places have implications for the distribution of crime. Specifically, it could be anticipated that the distribution of commercial land use and other crime generating or crime attracting places may have the greatest impact on the distribution of instrumental crimes. As elaborated in section 2.3 the distribution of these types of places, and particular land use decisions are a product of a myriad of factors making it likely that their concentration varies between cities across the country thus translating into varying degrees of crime concentration Criminogenic Pathways and Crime In addition to crime attractors and crime generators, scholars in the area of environmental criminology discuss such ecological features as nodes and paths in order to tie individual s day-today movements to an understanding of where crime is most likely to occur. Accessibility and urban form have long been an interest of criminologists and city planners concerned with the distribution of criminal opportunities. Past research has shown that policies and city planning decisions have a significant impact on the movement patterns or paths of individuals. These movement patterns, which are dictated by street networks and transit systems, also put potential offenders in contact with suitable targets in places in predictable ways, and have the ability to significantly influence the where crime is likely to occur (Bevis and Nutter, 1977; Beavon, Brantingham and Brantingham, 1994; Loukatiou-Sideris, 2002). In this way paths allow those with a strong propensity towards criminal behavior to act out, taking advantage of the large number of suitable targets which can be found utilizing the same routes A moderate amount of research has identified the clustering of criminal events near major arterial roads, larger intersections, bus stops, transit centers, and subway or rail stations (Beavon et al., 1994; Bernasco and Block, 2011; Johnson and Bowers, 2010; Wilcox, 1973; Maguire, 1982; Loukatiou-Sideris, 1999; Loukaitou-Sideris, Ligget and Iseki, 2002; Weisburd et al., 2012). For example, Robinson s (1998) study of crime around nine SkyTrain stations in Vancouver also found direct evidence that public transportation facilities have the ability to attract higher-than-expected rates of crime. Specifically, she found that 49 percent of all calls for service in the city occurred within 750 meters of a SkyTrain station, although this area only accounted for 15 percent of the total 27

41 area of the city. Similarly, Block and Block (1999) found that there was a strong relationship between street robbery and the proximity to rapid transit stations in the Bronx and Chicago s Northeast Side. Other research has found that the relationship between public transit and crime is contingent of ridership, land use, and the characteristics of the surrounding neighborhood (Ihlandfeldt, 2003; Loukaitou-Sideris, 1999; Loukaitou-Sideris et al., 2002). Street networks also have the ability to impact how people move around a city (Brennan, 1948; Lee, 1970). The theories of environmental criminology (Brantingham and Brantingham, 1991) and situational crime prevention (Clarke, 1992) suggest that variation in traffic flow may also impact the distribution of opportunities and offenders and could have a substantial impact on crime patterns. Streets with high volumes of traffic may provide a large number of criminal opportunities because they represent a place where victims and offenders come into contact with one another. In addition to individuals who use arterial roadways to travel from one place to another, commercial outlets, attempting to attract customers, are commonly located along major routes of travel, thus providing additional opportunities for crime. A number of studies have shown street type to be related to levels of crime (Beavon, Brantingham and Brantingham, 1994; Duffala, 1976; Levine and Wachs, 1986; Loukaitou-Sideris, 1999). Dating back to the Chicago School, Burgess (1916) concluded that one of the most important factors in understanding delinquency was the proximity of the youth population to streets that were home to a large number of businesses. Bevis and Nutter (1977) examined the influence of six street types on the spatial distribution of crime in Minneapolis, concluding that streets with greater accessibility were associated with higher rates of crime. Other scholars have also noted the clustering of criminal events near large intersections and along arterial roads (Wilcox, 1973; Maguire, 1982; Loukatiou-Sideris et al., 2002; Weisburd et al., 2012). In New York, street segments with a larger number of lanes were shown to experienced higher rates of crime (Perkins et al., 1993). Relatedly, White (1990) found strong evidence that the number of access lanes leading from major traffic arteries into neighborhoods was associated with rates of burglary. Similarly, neighborhood accessibility or permeability has been shown to be related to rates of household burglary (Bernasco and Luykx, 2003). Overall, criminological research on urban form and road networks suggests that the accessibility and movement of motivated offenders plays a key role in the distribution of crime. The spatial breath of these transportation networks (both public and private), their ridership, and the structural conditions present in the areas which they service all have the ability to influence the 28

42 distribution of crime within cities across the country. As discussed in greater detail below, there are several reasons to believe that public transportation plays a significant role in shaping how crime is distributed within cities across America. The previous section (2.2) and its subsections provide a review of much of the existing research on the distribution of conditions and places well-known to be associated with higher rates of criminal activity. The following section (2.3) focuses in on the potential between-city differences in the concentration of these conditions, highlighting how unique cities are from one another and suggesting there are reasons to believe that both the distribution and spatial concentration of structural conditions (i.e. poverty) and places (i.e. crime attractors) known to influence crime are likely to vary from city-to-city, thus providing support for the argument that the concentration of crime is likely to vary accordingly. 2.3 Social Change and the Concentration and Clustering of Criminogenic Conditions As suggested in the introduction, there are several compelling arguments for why where various conditions well-known to be associated with higher levels of crime and violence are more highly concentrated, higher concentrations of crime may result. Importantly, however, there also reasons to believe that the concentration (both spatial and aspatial), of these criminogenic conditions (i.e. poverty, racial heterogeneity) and places (i.e. crime generators and crime attractors) may vary from city-to-city. Specifically, research on between-city differences in racial and economic segregation suggest that due to broad social changes such as deindustrialization, suburbanization and immigration, significant differences in the distribution and clustering of criminogenic conditions exists across cities. For example, through their impact on the availability of affordable housing and low-skill job opportunities both the suburbanization and deindustrialization of American cities during the latter half of the past century, has shaped where the most impoverished segments of the population are tend to live. Similarly, historic levels of immigration to the U.S. during the same period is believed to have contributed to the concentration of poverty for some racial and ethnic groups and has increased levels of racial and ethnic segregation in destination cities across the U.S. The remainder of section 2.3 reviews each of these phenomena, highlighting the between-city differences in city history and development, lending support to the idea that variation in the concentration of crime may exist once a representative sample of cities is considered. 29

43 It is also possible that city-specific approaches to crime and other social problems have led to variation in the concentration of crime. Programs designed to combat high rates of poverty, such as public housing and redevelopment projects may impact the distribution of a city s most disadvantaged populations leading to variation in the concentration and clustering of criminal activity. Furthermore, policing strategies unique to particular cities, such as the use of hot spot policing techniques and directed patrols, may have a direct impact on where crime is likely to occur in a given city, thus contributing to variation in the concentration and clustering of crime between cities from across the U.S. Unlike the shocks of deindustrialization and immigration, these policies and programs stem from local decision makers goal of reducing crime and social problems in their own community and are likely to vary from one city to the next. Section 2.4 discusses the cityspecific factors which may also impact the concentration and clustering of crime in cities across the county Deindustrialization The first process which is has been shown to impact the concentration of structural conditions known to impact rates of crime is deindustrialization. Deindustrialization, defined by Bluestone and Harrison (1982) as a widespread, systematic disinvestment in the nation s productive capacity, is not simply an economic process but a social and cultural one as well. It refers to the process of social and economic change ignited by the removal or reduction of industrial activity that was formerly supported by the manufacturing industry. During the 1960s, due to a rapid influx of foreign competitors into economic markets previously dominated by American corporations (e.g. steel, automobiles, and electronics), profit margins for U.S. manufacturing firms began to shrink. Consequently, American manufactures were forced to change their ways. Most outsourced their work from plants in highly-unionized cities in the North to rural areas in the South as well as to foreign countries where wages and standards were lower. The retreat from manufacturing in the U.S. during the last four decades has been profound (Jargowsky, 1997). In 1961 manufacturing accounted for 27.7 percent of the US nonfarm employment, by 2010, this share had fallen to just 8.9 percent (Kneebone, Nadeau, and Berube, 2011). During the last several decades, tens of millions of blue-collar workers have lost their livelihoods to the closing of manufacturing centers across the U.S. (Glaeser, Vigor, and Sanford, 2001). 30

44 The impact of deindustrialization, although profound, has had varying effects on cities across the U.S. Nowhere was this more pronounced than in what has been come to be known as the rust belt. As manufacturing industries were most heavily concentrated in the Northeast and Midwest regions of the nation, these areas were especially hard hit (Sassen, 1990). Once being associated with a high standard of living and a robust economy, these areas are today associated with a high concentrations of poverty, unemployment and noticeable racial isolation. During the period of the population of the U.S. has doubled, yet within the same period, the populations of the great manufacturing cities in the northeast have declined significantly. For example, the cities of Detroit, Cleveland, Pittsburgh, St. Louis and Buffalo have all lost half of their population base in the past half-century (Mallach, 2010). Other cities such as Baltimore and Philadelphia have also been hard hit, losing nearly a third of their population. At the same time, however, as Americans migrated away from manufacturing centers, they have formed sprawling suburbs with polycentric economic bases, leading to the explosive growth of many cities, such as Phoenix, Arizona, which has grown tremendously over the past 50 years (Logan et al., 2002). As cities experience a loss of manufacturing jobs, urban areas have shifted to economies which are more service sector oriented. This transformation from a manufacturing economic base to a service one has had a tremendous impact on the structural characteristics of cities across the US including the increasing concentration of the poor, contributing to high levels of economic and racial segregation, and leading to the formation of a distinct social underclass (Wilson, 1987). Given that blue-collar jobs pay considerably more than low-skill service jobs, the loss of manufacturing industries increased the number of jobs paying poverty or near poverty wages (Lichter, 1988). Research on the concentration of poverty has shown that while the total proportion of the population living in poverty has remained relatively stable between 1970 and 1990, the percentage of poor people living in high-poverty neighborhoods (a form of concentration) increased substantially (Jargowsky, 1994; 1997; 2003). This indicates that rather than a change in poverty rate, per se, there was a change in where the poor lived. As those who could afford to moved out of urban centers, the poorest of city residents remained stuck, leading to an increase in the number of high-poverty neighborhoods in city-centers of many major cities across the U.S. Furthermore, although past research has only begun to consider the importance of the degree to which high-poverty neighborhoods cluster within space (i.e. Stretesky et al., 2004), there are reasons to believe that this process may play out differently in cities across the country, with poverty becoming increasingly spatially concentrated in some cities. For example, in cities which have strong historical ties to the 31

45 manufacturing industry a large number of low-skilled jobs were traditionally located in a central business or manufacturing district of the city, that is they were predominately monocentric. When these plants close and jobs disappear, it is likely to have a dramatic impact on those who lived in nearby and traveled to the area for work. This implies not only a higher concentration of poor economic conditions (neighborhoods with higher levels of poverty), but also a high degree of clustering between those neighborhoods within the central district of a given city (a growing spatial concentration of poverty). As discussed above, criminological theory suggests that the characteristics of severely disadvantaged areas may influence levels of crime through the creation of motivated offenders, and by reducing levels of informal social control, making it more likely that people will act on their propensity to commit crime. Studies in this area have generally found that neighborhoods with higher levels of poverty have more crime within their boundaries (Crutchfield, 1989; Hipp, 2007b; 2011; Krivo and Peterson, 1996; Warner and Rountree, 1997). Therefore, unequal impact of deindustrialization on cities across the U.S. may yield important differences in the concentration and clustering of poverty and certain disadvantaged minority groups, which may in turn have an effect on the concentration of crime Suburbanization One of the most striking features of cities and metropolitan areas across the U.S. is the massive and continuous suburbanization of both people and jobs that occurred during the latter half of the 20 th century. In previous periods, U.S. cities were characterized by small and dense central business districts located close to transportation hubs (i.e. port of train station). Due to high transportation costs, residences were located in or in close proximity to these central areas (Mieszkowski and Mills, 1993). With the proliferation of private automobiles, it was possible for city residents to move away from the city center while retaining their jobs. This explosion in the suburban population has contributed to the spatial expansion of many cities across the country as well as the emergence of rich suburban areas with an increased share of the total number of employment opportunities. However, the observed growth in the suburban location has not had an equal impact on all groups. Suburbanization has translated into higher levels of racial and economic segregation in central cities across the country due to the differential access to suburbs which minority groups possess. At the same time that predominately white, affluent suburbs were constructed, residential stratification along racial and economic lines restricted minority members 32

46 access to suburban locations in a number of ways (Logan and Molotch, 1987). Specifically, scholars have described how the use of restrictive zoning and other dual housing market conditions such as the steering of racial minorities by real estate agents into certain minority neighborhoods and the reduction of lending capital available to minority buyers has contributed to racial segregation (Massey and Denton, 1993; Galster, 1987; Logan and Stearns, 1981). Furthermore, the exclusion of racial and ethnic minorities from particular areas has led to a disconnection between entry-level jobs and those who are willing to work them. This spatial mismatch has been compounded by the lack of transportation to those who cannot afford the costs associated with car ownership leading to increasing disparities in wealth between areas in the city center and those in the suburbs. As the neighborhoods in the city center literally empty out and those can afford to, head to the more pristine suburbs, pockets of disadvantaged households remain, contributing to the concentration of poverty and racial heterogeneity. In older, historically monocentric cities (those with a distinct city center), it is likely that these disadvantaged neighborhoods also cluster closely within space near the old center of economic activity. Thus, it is believed that the suburbanization of cities across the country has contributed to variation in the concentration and clustering of conditions known to impact rates of crime. The suburbanization of cities and metro areas across America over the last three decades has been rather extraordinary. Glaser and Kahn (2001) report that the proportion of central-city residents in the 10 largest metropolitan areas had declined by 11 percent between 1970 and 2000 with some cities reporting much larger declines and that the average resident now lives more than nine miles away from the city center. Other scholars have reported a similar decrease in the number of jobs located in central cities during the same period. The proportion of jobs located in central cities decreased from 57 per cent in 1980 to 51 percent in 1990 and 47 percent in 2000 (Gobillon, Selod, and Zenou, 2007). It has been argued that this relocation of people and jobs from the city center to the suburbs, in combination with the differential access available to groups of different races and economic classes, has contributed to the large increase in the concentration of poverty observed during this period (Farley, 1987; Immergluck, 1998; Jargowsky, 1994; 1997; 2003; Kasarda, 1993). In his seminal paper, Kain (1968) argued that a major source accounting for the adverse labor market conditions which emerged is the spatial disconnection between inner city areas (where low-skilled workers lived) and the suburbs (where low-skilled jobs were becoming available). This has become known as the spatial mismatch hypothesis in which the problems associated with innercity ghettos are attributed to the sharp decrease in the number of entry-level jobs located in the inner 33

47 cities, coupled with the fact residents from these neighborhoods are unable or unwilling to travel to the suburbs for work. Following Kain s argument a large of empirical studies have tried to link the spatial disconnection from jobs and adverse labor market conditions (for examples see Holzer, 1991; Inhlandfeldt and Sjoquist, 1998; Gobillon et al., 2007). The general approach used to answer this research question usually examines the impact of some measure of the distance between residents and job locations on a measure of employment or earnings. More recent research has examined the impact of urban form on the commuting demands placed on individuals from different social groups is a spatial context (Horner, 2002; 2007; Schleith and Horner, 2014). Using data on the distance traveled to and from work drawn from the Longitudinal Employer-Household Dynamics (LEHD) dataset, Scheith and Horner (2014) show that the relative commuting burden placed on individuals with lower incomes has increased in recent years. Results of this research in this area suggest that the spatial patterning of affordable housing and job opportunities has an unequal impact on those of lower economic classes. The bulk of evidence from past research suggests that the distance to jobs is indeed partly responsible for the concentration of adverse economic outcomes for residents in inner city neighborhoods leading to the documented increase in the concentration of poverty and joblessness in those areas (for a review see Holtzer, 1991). This concentration could be both spatial and aspatial in nature, as the job base in central city neighborhoods begins to erode, and those who can afford to move to the outskirts of town, the proportion of un- or underemployed residents within neighborhoods within the city center begins to grow, creating pockets of economic disadvantage. It has also been argued that the problems associated with the suburbanization of American cities have been compounded by the availability of public transportation (LeRoy and Sontelie 1983; Glaeser, Kahn and Rappaport, 2008). As suggested above, suburbanization not only shifted jobs away from the city center, as suggested above, it has also increased the distance between residents living in affordable areas to the areas where jobs are available. As the financial cost associated with owning a vehicle makes them unavailable or unattractive to the poor, public transportation offers an alternative that is sometimes more appealing to those with low incomes. Public transportation, however, relies on high densities to operate efficiently and therefore, is often unavailable in lowdensity suburb areas, creating a disconnect between the low-skilled population base and suburban jobs. Kain and Meyer (1970) identified the isolation of inner-city residents from suburban opportunities as the ghetto transport or poverty transport problem. As many of the low-skill entry-level jobs are often beyond the reach of public transportation, the relocation of jobs to the 34

48 suburbs put low-skilled workers without automobiles at a significant disadvantage in securing employment. There are also reasons to believe that the coverage and density of public transportation varies from city-to-city. Dependent on a city s needs, public transit systems are designed to serve specific segments of the population in a wide range of geographic areas, and are therefore highly distinct from one another. For example, in many cities like Washington D.C., Chicago and New York, large segments of the total population rely on public transportation while in other cities ridership is limited mainly to poor, inner-city residents. Furthermore, in many metropolitan areas, the construction or extension of public transportation systems has been advocated as a way to address the problem over dependence on the automobile for the journey to work (Ihlandfeldt, 2003). In 1964 the Urban Mass Transport Administration (UMTA) was established to address the transportation problem by awarding grants for transit system development in select cities across the country. Many of the UMTA projects were developed with specific goals in mind. For example, in Long Island, NY multiple bus routes were planned to provide service from low-income communities to job concentrations located on the outskirts of the city (Crain, 1970). However, many suburban communities in these same areas often resist the creation of train or bus stations in their communities because they believe that these facilities will deliver criminals to their doorsteps (Poister, 1996; Walmsley and Perret, 1992; Tandon, 1999). Numerous news stories highlight how suburban communities and neighborhood groups often perceive that public transportation is capable of transporting inner city crime, gangs and drugs into the suburbs (Carlson, 2000; Gustafson, 2008; Nussbaum, 2009). If these fears are justified and new transit stations do increase neighborhood crime as some research suggests (i.e. representing crime generators), this may counteract new transit proposals, limiting the spread of these projects from the inner city to the suburbs, thus increasing the spatial mismatch between affordable housing and available jobs. Therefore, one would expect that the density and penetration of public transportation to vary from city-to-city, leading to variation in the economic success of those living in inner city neighborhoods. Results from research in this area confirm that the availability of public transportation has an impact on the concentration of poor economic conditions in inner-city areas (Ihlandfeldt and Young, 1996; LeRoy and Sontelie, 1983; Ong and Miller, 2005; Glaeser et al., 2007). Sanchez (1999) found that access to bus transit had a positive effect on employment opportunities for inner-city residents in Atlanta, Georgia and Portland, Oregon. Using a large sample of metropolitan areas Glaeser, Kahn and Rappaport (2008) explored the impact of the availability of public transportation 35

49 on the centralization or concentration of the poor. Utilizing data from the 2001 National Household Transportation Survey to estimate the time costs of taking public transportation versus driving, Glaeser and colleagues (2007) found that the availability of public transportation plays an important role in explaining the concentration of the poor in inner city areas. Across cities, the poor are likely to live in cities with more public transportation and the poor are less centralized, or less concentrated, when the suburb-central city gap in public transit is not as great (Glaeser et al., 2007). Authors also suggest that the lower availability of public transportation in the West may explain why there are lower levels of economic segregation in that region. Overall, results from prior research suggest that variation in the availability of public transportation across cities in the U.S. has had an impact on the concentration of poor economic conditions within city centers through the mechanisms described by the spatial mismatch hypothesis. By limiting the distance low-skilled workers can travel for work, many remain stuck in select neighborhoods within the city center contributing to the spatial concentration of poverty, and large clusters of underemployed residents within those areas serviced by public transportation. In cities with more highly developed public transportation system, however, it may be less likely that these high-poverty areas cluster less dramatically within space, leading to lower spatial concentrations. Therefore, I argue that the between-city differences in public transportation infrastructure may contribute to between-city variation in the concentration and clustering of crime at the city level. A second mechanism by which the suburbanization of American cities has led to the concentration of poverty and an increased segregation of racial and ethnic groups is through its effect on the where people live. Scholars have contended that suburbanization has impacted economic and racial segregation as whites and those with higher incomes have moved from the city center to more distant suburban locations (Galster, 1991; Pendall, 2000). Although many reasons for the high levels of segregation have been suggested (i.e. discrimination and household preferences), past research has highlighted the role of the housing market, including the use of land use restrictions and their exclusionary effects, in increasing the spatial distance between economic and racial groups (Clark, 1986; 1991; Galster, 1988; Pendall, 2000; Nelson, Sanchez and Dawkins, 2004). The use of zoning and other types of land use controls to inflate the cost of suburban housing has been shown to contribute to higher levels of racial and economic segregation. Due to discrimination in housing and mortgage markets, lower average family incomes and limited credit histories, homeownership rates for African Americans and Latinos are significantly lower that 36

50 homeownership rates for whites. To the extent that local governments place regulations that encourage the construction of owner-occupied housing, they also limit the choices of minority and low- to moderate-income households. Even when rental housing is available, traditional land use controls tend to isolate these developments within space, apart from single-family homes, thus contributing the concentration of certain groups along racial and ethnic lines. Here again, although not systematically explored in a spatial context, there are reasons to believe that housing policy is capable of creating large clusters of housing with similar characteristics (i.e. expensive or affordable, high- or low-density) which in turn contributes to the spatial concentration of certain groups within space. For example, low-density zoning restrictions prevent residential development in a given area except as detached, single-family households. This form of zoning can restrict the supply of affordable housing in a given area in a number of ways. First, by preventing multi-family units, the stock of affordable housing in the area is reduced, because developers are forced to abide by the zoning requirements which require them to build larger, more expensive, detached housing units. Pendall (2000) also suggests that low-density zoning may reduce rental affordability because rentals in these areas are in greater demand because of the superior services associated with these areas. Other restrictions, such as permit caps, also reduce the supply of housing units and may have an especially large impact on the construction of affordable housing. It has been shown that permit caps give builders incentives to build a smaller number of larger, more expensive homes in order to increase profits, thus reducing housing affordability in a given area (Zorn, Hansen and Schwartz, 1986). Therefore, it is believed that low-density zoning and permit cap restrictions created by local municipalities may impact the supply of affordable housing and promote exclusion based on race and social class, leading to a greater concentration of criminogenic conditions outside of suburban areas. Here again there is a spatial component, specifically clusters of predominantly white, affluent neighborhoods are expected to exist as the distance from the city center increases, leaving racial and ethnic minorities and those who cannot afford the housing spatially concentrated in older, more densely populated neighborhoods within the city center. Again, the importance of urban form is clear in this discussion. For example, in cities such as Philadelphia and New York, which have remained predominantly monocentric, land area near the central business district is a premium, and therefore housing is cost prohibitive for the majority of individuals who may work in the city. However, in cities where a large percentage of jobs have moved from the city center to locations along the outer edges of the city, a different pattern of land 37

51 and housing prices is likely to emerge. Clusters of more affordable housing may exist in the city center near the former hub of economic activity because these areas are generally older and more densely populated, as well as associated with older infrastructure and less desirable conditions. Finally, in cities which include a mixture of monocentric and polycentric attributes, housing markets may be fragmented, with a mix of neighborhoods both expensive and inexpensive distributed between the central business district and the other hubs of economic activity. As discussed above, these patterns are likely to be dictated by housing policy. Here it is anticipated that concentration of economic disadvantage or affluence (neighborhoods made up of households of similar characteristics) would still exist, yet they are less likely to be surrounded by neighborhoods of the same makeup, leading to lower levels of spatial concentration or less clustering. On the other hand, there are other policies that local municipalities can adopt which may foster integration and prevent further segregation by race and class. One way the housing market could lead to desegregation is for suburban communities to open themselves up to low- and moderate-income households (Downs, 1973). This can be done through higher density zoning, the relaxing of building codes, and in other ways that promote mixed-income households such as the use of mandatory housing elements. Mandatory housing elements have a common goal of the provision of housing for all types of households projected to live in a given area, rather than being designed to promote select groups such as those individuals who can afford to purchase expensive homes. They require developers to set aside housing units for low- and moderate-income households or achieving a mix of housing units by type and density. The idea of requiring specific housing elements is relatively new, with the first mandatory statues arising in two states, California and Oregon) in the 1970s (American Planning Association, 2002). Portland Oregon, for example, requires that 20% of all housing be targeted to low- and moderate-income households (Knaap and Nelson, 1992). Urban containment policies have also been suggested as a way to reduce the impact of housing on racial and economic segregation (Downs, 1973). Broadly speaking, urban containment policies can be distinguished from other land use regulations by explicitly limiting the development of land outside a defined urban area, while encouraging new development and redevelopment within the existing boundaries (Nelson and Duncan, 1995). This prevents further exclusion of particular groups by requiring the redevelopment of already built areas, leading to more mixed-income communities, thus reducing racial and economic segregation. Scholars note that these programs are more subtle than mandatory housing policies and often do not include racial undertones in their 38

52 justification. They usually include arguments such as preserving open spaces, revitalizing urban areas, and expanding housing choice among other things (Nelson, 1999). During the 1970s, urban containment programs emerged in a handful of metropolitan areas such as Miami, Minneapolis, Boulder, Sarasota and Sacramento, California. Similarly during the 1990s, Washington and Oregon adopted urban containment policies and applied them to most urbanized counties. Presently, over 100 metropolitan areas use some form of containment policy, designed to restrict the physical space of the locality (Nelson, Dawkins and Sanchez, 2004). These policies are likely to reduce the division between groups within urban areas as the explicitly require the mixing of different housing types, which are likely to attract members of different social classes and racial or ethnic groups. This sort of pattern would imply lower levels of both concentration and clustering of economic disadvantage. A sizeable body of evidence from legal cases shows that municipal governments have used land use controls, such as exclusive low-density zoning and building caps to exclude low-income and minority households from certain areas (Keating, 1997). Alternatively, Nelson, Dawkins and Sanchez (2004) found that mandatory housing elements and the use of urban containment policies were associated with a reduction in racial segregation. Metropolitan areas which employed some form of containment or mandatory housing element policy saw greater reductions in residential racial segregation that those areas that did not during the years between 1980 and Research also suggests that there is significant variation in regulatory policy from one locality to the next. Pendall (1999) reported that the Northeast and Midwest regions are characterized by fragmented metropolitan areas where municipalities tend to use large-lot zoning requirements to control growth and rarely adopt affordable housing programs to reduce the price effects of their land use regulations. At the same time, excluding Florida, the South and the Great Plains region rarely use land controls of any kind as they continue to try to promote growth rather than restrict it. Pendall (2000) also suggests that the West, Florida and Maryland are all characterized by stronger urban containment programs (those policies which constrain growth to a certain geographic area). Results from several empirical studies have shown that trends in segregation mirror these regional differences in land use regulation (Nelson, Sanchez and Dawkins, 2004). Farley and Frey (1994) examine segregation trends in 1990 and find that after controlling for other determinants of segregation found in the literature, the highest levels of racial segregation are in the Northeast and Midwest, with much lower levels observed in the South and the West. Similarly, results presented by Nelson, Dawkins, and Sanchez (2004) suggest that urban containment policy may reduce racial segregation in the areas in which they are enacted in the decades prior. 39

53 Research in this area suggests that variation in housing policy has an impact on both racial and economic segregation through its impact on the availability of affordable housing. Specifically, the reliance on land use restrictions, urban containment policies, and building caps has a direct impact on the housing choices available to minorities and low- and moderate-income families, thus determining where they are able to live. As measures of racial and economic segregation are synonymous with the concentration of conditions known to impact the distribution of crime, it is believed that the different housing policies which exist in municipalities across the country have the ability to contribute to variation in the concentration and clustering of crime across American cities. Overall, demographic research on racial and economic segregation suggests that the dramatic increase in suburbanization over the last four decades has had an impact on the distribution of population groups along racial and economic lines. As those who could afford to leave moved to areas further away from the city center (predominately White, middle- to upper-class households), those who could not remained in the city. Compound varying degrees of suburbanization with variation in the availability and use of public transportation and differences in housing policy, it is possible to see how some cities have become significantly more segregated over time, while others have remained more integrated. There are also reasons to believe that due to urban structure (i.e. historically moncentric cities) and the spatial nature of housing markets, that suburbanization is likely to have contributed to the clustering of disadvantaged groups within neighborhoods in the city center in certain cities. These between-city differences in the concentration and clustering of criminogenic conditions (i.e. poverty and racial heterogeneity), I argue, are likely to have contributed to variation in both the concentration and clustering of crime Immigration At the same time as the consequences of deindustrialization and suburbanization were hitting cities across America, amendments to immigration policy implemented in 1968 repealed immigration quotas based on nationality, resulting in a significant influx in new immigrants from Latin America and Asia (Keely, 1974; 1979). Between 1970 and 2005, the proportion of U.S. residents born outside the U.S. increased from 4.8 to 12.4 percent (Raphael and Smolensky, 2009). This dramatic increase in the foreign born population has had an impact on the economic and demographic character of cities across the country. Specifically, there is evidence that high levels of immigration in many cities across the U.S. may have contributed to the concentration of poverty and increases in racial and ethnic segregation; two conditions known to be associated with higher rates of 40

54 crime. However, as will become apparent below, the relationship between immigration and conditions associated with crime is more complex than originally suggested. Specifically, when theorizing how high rates of immigration may impact the demographic and economic character of cities across the country, it is important to consider the origin of the immigrant population in question and what resources they bring with them. Traditionally, it has been argued that immigration is likely to increase levels of poverty in areas with a large number of immigrants in a number of ways. First, as poverty rates among foreign born populations are relatively high, immigrants may have a direct effect on the poverty rate by increasing the number of people who live in poverty within a given area. Research has also shown that due to the fact that many immigrants arrive with lower levels of education, they are more likely to live in poverty than their native-born counterparts (Borjas, 1995; 2003; Card, 2005). Second, immigration has an effect on the labor supply by increasing the number of low-skilled laborers is a given area, leading to underemployment in both the immigrant and native populations (Borjas, 1999; 2003). There is evidence of labor market competition between immigrants and natives for groups with the lowest levels of education. While immigrants composed only 13 percent of the workingage population in 2000, they made up a much larger proportion (28%) of the population who do not have a high school diploma, and over half of those with less than 8 years of schooling (Card, 2005). This influx in low-skilled laborers may contribute to economic strain by increasing the number of individuals (both immigrant and native) who are likely to make substandard wages. Additionally, past research suggests that recent immigrants are likely to settle in select areas of a given city contributing to levels of racial and ethnic segregation. A long-established line of research has shown that areas with a large proportion of immigrants arise because newcomers needs for affordable housing, family ties, familiar culture and connections to the job market (Stark, 1991; Thomas and Znaniecki, 1974). Beyond the unequal distribution of immigrants across tracts in cities from around the country, a striking feature of the residential pattern of many new immigrant groups is the extent to which these immigrant neighborhoods cluster within space (Allen and Turner, 1995; Logan et al., 2002; Massey, 1986). New immigrants come to areas where they have friends or family on which they can depend. After becoming established, they tend to settle in the same area, leading to further consolidation and growth of ethnic neighborhoods. While results from early research have suggested that high rates of immigration are likely to lead to the concentration of poverty and higher levels of racial and ethnic segregation, more recent research highlights that increased levels of immigration need not translate into poor economic 41

55 conditions, although they might for some racial and ethnic groups (Nee and Sanders, 2001; Nielsen and Martinez, 2003; Logan et al., 2002). While most immigrants a century ago were manual laborers who lacked the financial resources to have much choice regarding where they lived, more recent immigrants come from a diverse set of nations and many who have higher levels of human capital that find professional or other high-status positions in the U.S (Card, 2005; Nee and Sanders, 2001; Logan et al., 2002; Portes and Rumbaut, 1990). More specifically, certain groups such as immigrants from India and other Eastern counties possess high levels of human and social capital (i.e. education and economic resources), and while they may have preferences for living in neighborhoods which possess similar demographic characteristics, they are not necessarily impoverished. In contrast, the areas established by low-wage labor migrant groups, such as Mexicans, who are less likely to have large amounts of wealth, are more likely to remain economically depressed. Accordingly, Logan and colleagues (2002) hypothesized that the market resources that immigrant groups bring with them are the primary determinant of the kinds of neighborhoods they are likely to settle into. The results of their analysis support this idea. For example, Dominican immigrant neighborhoods in New York have a much higher poverty rate than other non-immigrant neighborhoods in the city. Similar findings were described for Chinese, Mexicans, Guatemalans, Filipinos and Koreans in Los Angeles (Logan et al., 2002). However, for the Afro-Caribbean in New York and the Vietnamese in Los Angeles, living in ethnic neighborhoods was associated with greater economic resources. Further, although there is evidence of discrimination against Hispanics and Asians within the housing market, it has been shown that both groups have substantially lower segregation from non-hispanic whites than do blacks (Massey and Denton, 1987; Logan, Stults, and Farley, 2004). Specifically, research has found that higher levels of education, and the according increase in income allows the most advantaged members of certain immigrant groups to live in neighborhoods that are comparable to non-hispanic whites (Massey and Denton, 1987; Alba, Logan and Stults, 2000; Logan, Alba and Leung, 1996; Logan et al., 2004). Results from empirical research suggest that while the expected association between racial and ethnic heterogeneity and the higher levels of disadvantage held for some groups, other groups did not follow this pattern. Thus, the depressed central-city enclave is not the only outcome which can be anticipated, suggesting that the relationship between immigration and the structural characteristics associated with crime is exceedingly complex. It is possible that this complexity may explain the mixed results of past research on the relationship between immigration and crime (Ousey and Kubrin, 2009; Stowell et al., 2009; Martinez et al., 2010; Kubrin and Ishizawa, 2012). 42

56 However, combining the well-documented tendency for immigrants to live closely within space with the fact that certain groups (i.e. Mexicans, Chinese and Koreans) are more likely to live in depressed neighborhoods, it is could be anticipated that high-levels of immigration in select cities across the U.S. has contributed to increases in the overall concentration and clustering of poverty and deepened patterns of residential segregation. This in turn has implications for variation in the concentration of crime (both spatial and aspatial) in cities across the U.S. Those cities which have taken in large number of recent immigrants overall (especially those from groups more likely to enter the lower rungs of the economy), such as Los Angeles, New York, and Miami, may be more likely to have higher concentrations and more substantial clustering of crime, while cities which did not receive a large number of immigrants, or attracted members of groups with higher levels of social capital, may have more diffuse patterns of criminal activity The Political Economy In addition to the historical processes of immigration, suburbanization and deindustrialization listed above, it has been argued that that the actions of political and economic elites have made certain neighborhoods susceptible to deleterious conditions (Bursik, 1989; Bursik and Grasmick, 1993; Massey and Denton, 1993; Velez and Richardson, 2012). Heavily grounded in conflict theory, the political economy perspective was developed to explain the concentration of poor economic conditions which appeared in the core of many American cities during the 60s and 70s. This perspective suggests that social problems arise from a city s pursuit to grown and accumulate wealth (Logan and Molotch, 1987). Neighborhoods compete for scare city resources, and strong ties to political and economic elites influence to which neighborhoods these resources are allocated. Because affluent neighborhoods are capable of furthering the city s interests of growth and wealth accumulation they are more likely to receive lucrative city resources and desirable public services. Logan and Molotch (1987) argue that wealthy neighborhoods are able to use their worth to leverage themselves to city elites and entrepreneurs. This leverage assures that they will be protected from external threats such as the construction of public housing or other undesirable city facilities like a new sewer plant or interstate. Poor neighborhoods, on the other hand, are of little worth to the city, and are therefore unable to resist initiatives leading to decisions that may further debilitate the area (Velez and Richardson, 2012). An emerging body of literature has examined how the decisions of outside economic elites, such as banks, have the ability to impact neighborhood conditions, including levels of poverty and 43

57 crime (Massey and Denton, 1993; Peterson and Krivo, 2009; Skogan, 1990; Squires and Kubrin, 2006; Velez, 2009; Velez and Richardson, 2012). One potential pathway for banks decisions to impact neighborhood conditions is through lending behavior. Specifically, it has been argued that home mortgage lending has the capability to dampen rates of crime at the neighborhood-level through its effects on, physical disorder, economic opportunities and public social control (Velez and Richardson, 2012). The infusion of monies, through residential and commercial lending, has the potential to negatively affect levels of physical disorder within a neighborhood. Residential bank loans may also contribute to the number of economic opportunities available in a given area, thereby reducing rates of crime. Loan dollars have the capability of expanding local economic opportunities through their impact on the maintenance of the population base, and the need for business. If consistent investment occurs, loans should create an environment that encourages existing businesses to stay, as well as attract new businesses to the area. This in turn will have an impact on the number of legitimate economic opportunities in a given area, a factor well-known to contribute to levels of economic deprivation and criminal activity. Accordingly, it is possible that the differential availability of loan dollars may buffer some neighborhoods from these conditions by creating economic activities whereby reducing levels of poverty. Without loans, neighborhoods are placed on a trajectory of physical decline and abandonment which has the potential to translate into higher rates of poverty and crime (Hagan, 1994; Massey and Denton, 1993; Skogan, 1990; Sampson and Raudenbush, 1999; Squires and Kubrin, 2006; Taylor, 2001). Hagan (1994) describes this process in his discussion of the transformation of one majority-black neighborhood in Chicago. Although poor, this once vibrant community declined substantially due to economic disinvestment and was later victim to a large number of abandoned buildings, broken windows, and graffiti. Therefore, it is thought that neighborhoods which receive an adequate amount of loan capital are protected from this spiral of decline. Considering the recent collapse of the U.S. mortgage market, the distribution of residential loans lending sticks out as an important factor having implications for the concentration of crime across U.S. cities. Research in this area has shown that between 2006 and 2008 conventional prime lending, declined substantially and even more drastically in communities of color (Woodstock Institute, 2010). Furthermore, evidence suggests that there is geographical concentration of subprime mortgages in low-income and minority neighborhoods (Calem, Gillen and Wachter, 2004; 44

58 Immergluck and Wiles, 1999). The Woodstock Institute (2010) warns that without major efforts to secure fair lending, we will see further shrinkages of lending capital to communities that have the greatest need. The drying-up of prime mortgage capital will likely result in the accelerating neighborhood decline, especially in already disadvantaged communities. Thus, the shifts which have occurred in the banking industry over the past several decades have implications for the levels of crime in neighborhoods within cities across the U.S. Empirical research suggests that a meaningful relationship between the distribution of lending and conditions associated with elevated levels of crime exists. Furthermore, data on the number of subprime loans at the neighborhood-level has illustrated that these loans, those with the greatest potential to harm vulnerable communities, tend to cluster within space. Results of prior research suggest that in cities where subprime loans are spatially clustered, poor economic conditions and criminal events may be more concentrated both spatially and aspatially. High concentrations of prime loans, on the other hand, may have the opposite effect on crime leading to a more diffuse distribution of economic deprivation, disorder, and criminal activity The Concentration and Clustering of Crime Generating Places Although a relatively sizeable literature on the determinants of poverty and racial segregation exists, much less is known about the factors associated with the concentration and clustering of crime generating places. Scholars have devoted, and rightfully so, most of their efforts on outcomes related to the concentration of certain kinds of places such as payday lenders and tobacco outlets, rather than to the determinants of their distribution. (i.e. Grusbesic et al. 2013; Pridemore, Grusbesic, 2013; Roncek and Bell, 1981). However, a small amount of literature from the health field sheds lights on the characteristics associated with higher densities of these crime generating places. For example regional studies have found that tobacco outlet density is higher in areas with lower median household incomes and those with larger Black and Latino populations (Hyland, et al., 2003; Peterson, Lowe and Reid, 2005; Rodriguez, Carlos, Adachi-Mejia et al., 2013; Schneider et al., 2005). Other research, on the distribution of alternative financial service providers reports that demographic factors such as income, race and education level are associated with the location of payday lenders, pawn shops, and check cashing locations. Although findings are somewhat mixed, past research suggests that payday lenders and other alternative financial services are more prevalent in areas with lower household incomes, lower levels of education, and larger Black and Latino populations (Burkey and Simkins, 2004; Gallmeyer and Roberts, 2009; Graves, 2003). Importantly, 45

59 however, most of these studies have been focused on the distribution of these places within a given city, and are limited in their generalizability. In a county-level study of alternative financial service providers, Prager (2009) confirms that certain demographic characteristics are associated with the number of check-cashing places within a given county, and that measures of the population s credit worthiness and state laws governing this type of financial institutions also played a role. Finally, research on the geographic density of alcohol retailers has come to similar conclusions. The density of alcohol retailers at the neighborhood-level has been shown to be associated with higher levels of poverty and areas with larger Black and Latino populations in urban areas (Livingston, 2011; Romley, Cohen, Ringle, and Sturm, 2007; Berke, Tanski, Demidenko et al., 2009). While there is much more to be learned regarding the determinants of the location of risky facilities and the factors associated with their concentration and clustering within space, the available evidence suggests that in cities which are more highly segregated and have high concentrations of poverty, dense pockets of these criminogenic places are likely to exist. As discussed above, trends in deindustrialization, suburbanization and immigration have had an impact on the concentration of poverty and the locations in which different racial groups live. Accordingly, it may be anticipated that these same places, which were hit hardest by deindustrialization, or took in a large number of immigrants may also have highly concentrated pockets of crime attractors which may translate into higher concentrations of crime. Section 2.3 and its subsections argues that in cities where criminogenic conditions are unequally distributed or cluster tightly within space (i.e. they are spatially concentrated) crime is likely to be more highly concentrated. After explicating the theoretical connection between structural conditions such as poverty, unemployment and racial heterogeneity and crime at the aggregate level, and reviewing the past research associated with each of these conditions, I have argued in section 2.3 that historical events such as wide scale deindustrialization, the transition of the population base from the city center to the suburbs and historic rates of immigration have had an unequal impact on cities across the U.S. Specifically, due to the abandonment of manufacturing as a source of blue collar jobs, and the shift of the population base to areas away from the city center, many cities across the Northeast and Midwest saw substantial increases in the concentration of poverty. Around the same time, historic numbers of immigrants from Latin America and Asia began to arrive in border cities across the U.S. The tendency of immigrants to settle in areas with like populations also contributed to the spatial clustering of poverty and strengthened trends in racial segregation or the concentration of racial heterogeneity. Finally, the theoretical perspective of the political economy 46

60 and the impact of lending decisions made by economic elites (i.e. banks) are believed to have had an effect on the concentration of delirious economic conditions by starving many areas of much needed capital investments to ward off many social conditions associated with crime. In combination, deindustrialization, suburbanization, immigration, and the political economy suggest that those conditions most closely associated with higher rates of crime are likely to be more highly concentrated in some cities than in others. Thus, it is reasonable to assume that crime may be more concentrated in those cities hit hardest by deindustrialization, cities which have taken in a large number of immigrants over the past four decades, and in those which experienced large scale disinvestment by economic elites. Although these assertions are not tested directly in this dissertation, the sections above provide a number of compelling theoretical reasons to believe that variation in the concentration and clustering of crime exists across cities. The impact of these described mechanisms is something which future research should explore in greater detail. 2.4 City-specific Approaches to Crime and Other Social Problems In addition to the effects of deindustrialization, suburbanization, immigration, and disinvestment by economic elites which were felt unequally by cities across the country, it is possible that city-specific approaches to crime and other social problem have led to variation in the concentration of crime. Specifically, programs designed to combat high rates of poverty, such as public housing and urban redevelopment projects may impact the distribution and clustering of a city s most disadvantaged populations leading to varying degrees of crime concentration. In addition, policing strategies unique to particular cities, such as order maintenance and hot spot policing techniques, may contribute to variation in the concentration of crime in cities across the U.S. more directly. Unlike the more global, or regional shocks of deindustrialization, suburbanization and immigration discussed above, these policies and programs stem from local decision makers goal of reducing crime and social problems in their own community and are likely to be highly variable from one city to the next. As will become apparent, the role of economic opportunity and the availability of affordable housing again play a prominent role in where crime is likely to occur. Section 2.4 reviews these city-level programs and policies, highlighting the potential for variation across cities, and suggesting that variation in the concentration of crime is likely to result. 47

61 2.4.1 Public Housing A city characteristic which has long been associated with rates of economic disadvantage is the existence of public housing. As alluded to above, the type of housing available in a given area has a large effect on who is likely to live there. Public housing, that which is subsidized by the government for the most disadvantaged segments of the population, is of particular interest because it has clear connections to where individuals living in poverty are able to live. Public housing was established to provide decent and safe rental housing for eligible low-income families, the elderly, and persons with disabilities. The 1.3 million units of public housing present in the U.S., which account for about 5 percent of all rental units in the country, are an important source of affordable low-income housing for those at the bottom rungs of society. These public housing units take on greater importance in large urban areas, where they represent a higher proportion of the affordable housing stock available to low-income individuals. In recent years, both local and federal governments have made significant changes to housing policy with implications for the distribution of those receiving public assistance both within- and between-cities, which could in-turn have implications for the concentration and clustering of crime. High concentrations of public assistance have long been connected to the processes of social disorganization (Kubrin and Weitzer, 2003) and have been seen to compound the effects of other neighborhood conditions (Bursik, 1989; Skogan, 1990; Wilson, 1987). Scholars have suggested that public housing projects, along with urban renewal policies and the decline in manufacturing in large U.S. cities lead to the creation of a permanent underclass by socially and spatially isolating poor, inner city minority residents (Bauman, Hummon, and Muller, 1991; Bickford and Massey, 1991; Goering, Kamely and Richardson, 1997; Wilson, 1987). The co-occurrence of high rates of poverty and the construction of large multi-family public housing projects act to concentrate the most disadvantaged segments of the population and have been shown to be especially relevant to the generation of crime in the surrounding area. For example, Goering, Kamely, and Richardson (1997) found that in 1990, nearly one-half of all public housing tenants lived in high-poverty neighborhoods. Hirsch (1983) also highlights that in Chicago during the 1950s, public housing projects were predominately constructed in poor, minority neighborhoods which may have been least able to resist them. Research illustrates how the construction of public housing projects acted to spatially anchor poverty in disadvantaged, predominately minority neighborhoods, strengthening patterns of 48

62 economic and racial segregation, and leading to higher rates of welfare dependency, sexual promiscuity and crime (MacDonald, 1997; Massey and Denton, 1993; Wilson, 1996). Public housing projects have been shown to be plagued high levels of residential turnover, and the associated instability has been shown to inhibit the formation of social ties, thus reducing the ability to maintain effective levels of social control (Bursik, 1989). This downward spiral of isolation and neglect emerges when poorly funded and maintained public housing projects are left uncared for, resulting in visible signs of disorder, abandoned structures, litter, graffiti and vandalism, all which have been associated with higher levels of criminal activity (Bickford and Massey, 1991; Holloway et al., 1998; Skogan, 1990; Wilson and Kelling, 1982). Indeed, a considerable body of research has found that public housing is related to higher rates of neighborhood crime (Popkin and Cunningham, 2005; Fagan and Davies, 2000; Farley, 1982; Harrell and Gouvis, 1994; McNulty and Holloway, 2000; Roncek, Bell and Francik, 1981). Evidence is consistent with the view that public housing is associated with crime due to its role in the concentration of poverty and other conditions related to social disorganization within neighborhoods (Sampson and Wilson, 1995). Bursik (1989) found that the construction of public housing in Chicago during the 1970s was associated with increases in rates of residential instability which, in turn, resulted in increases in crime. Similarly, McNulty and Holloway (2000) point out that those black communities with public housing projects tend to have higher crime rates than similar communities without the public housing. The idea that the concentration of poverty through the use of public housing is a major cause of social problems such as joblessness, poverty, and crime has led to sweeping changes in housing policy at both the local and federal levels (Popkin, et al., 2004). Housing authorities across the country have changed their tactics by developing programs such as Moving to Opportunity (MTO), the Housing Opportunities for People Everywhere (HOPE VI), or Section 8 voucher programs which are designed to allow participants to reach better and safer neighborhoods while retaining the support of housing programs (Freedman and Owens, 2011; Lens, Ellen, and O Regan, 2011). This new breed of housing assistance program was designed with a number of goals in mind. First, they were designed to spatially de-concentrate inner-city poverty by demolishing large, spatially concentrated high-rise developments and replacing them with mixed-income housing, as well as housing vouchers, allowing recipients of housing support to live in other areas throughout the city. Secondly, they hope to integrate the tenants into suburban, middle-class communities, providing greater opportunity, resources and choice for assisted housing residents (Galster and Zobel, 1998). 49

63 One prominent example of such a voucher program, the Gautreaux assisted housing program, was created in Chicago in 1976 as a result of a series of lawsuits against the Chicago Housing Authority and the U.S. Department of Housing and Urban Development (HUD). Between its inception and 1998, the program had granted over 6,500 Section 8 certificates to former public housing residents, relocating them in predominately white, low-poverty neighborhoods in the city and suburbs of Chicago. Given the relative success of the Gautreaux program, a number of other cities (i.e. Boston, Cincinnati, Dallas and Hartford) have redesigned their housing policies to model what was done in Chicago (Rosenbaume, 1995). Similarly, the MTO and Section 8 programs were launched at the federal level which provided funding private-market rehabilitation and new construction of new privately-owned mixed-income housing units. The MTO program was rolled out in five cities across the country, hoping to provide low-income families with better places to live. Between 1977 and 1997, the number of households receiving housing vouchers increased from 162,000 to over 1.4 million which comprised over one-third of all low-income renters served by HUD (Jacob, 2004). Between the late 1970s and early 90s a combination of large public housing projects and voucher subsidies were used. By 1989, however, the available evidence suggested that these initiatives had done little to deconcentrate poverty, and funding cuts to public housing programs had led to rapid deterioration of the existing public housing units still in use (Goetz, 2003). In 1992, the Housing Opportunities for People Everywhere (HOPE VI) program was created with the aim of dramatically redefining the look of public housing, requiring the demolition of the most severely distressed public housing projects and the relocation of residents using private-market subsidies (Clampet-Lundquist, 2004; Kleit and Manzo, 2006; Oakley and Burchfield, 2009). Since 1992, HUD has awarded HOPE VI grants in 166 cities across the U.S. As of 2002, 63,100 severely distressed units had been demolished and another 20,300 unites are slated for redevelopment (Holin et al., 2003; Popkin et al., 2004). This battery of new housing programs, which are generally designed to intersperse lowincome households throughout a variety of neighborhoods across the city, have been the subject of recent criminological research and have been shown by some to reduce poverty rates in the original areas as well reduce the potential of victimization for program participants (Goering, Feins and Richardson, 2002; Rubinowitz and Rosenbaum, 2000; Lens et al., 2012). The results of these studies have not been unanimous. While some researchers have found that participants have successfully moved to better neighborhoods, others have not (Patterson et al., 2004; Popkin and Cunningham, 50

64 2005). It has been argued that the disparate results across MTO sites may be partially explained by variation in housing availability among the different locations in question (Burdick-Will et al., 2010). Previous research has indicated that without special counseling, renter households receiving vouchers make short-distance moves, remaining in or near their original neighborhoods and consequently experience little improvement in their housing conditions (Varady and Walker, 2000). This is due to the fact that in some markets there are considerable obstacles to the successful placement of voucher holders outside poor neighborhoods (Oakley and Burchfield, 2009). Tight rental markets, such as those in major cities like Chicago and New York can fail to yield enough affordable housing, resulting in voucher holders being forced to relocate in high-poverty neighborhoods, thus failing to reduce the concentration of poverty and lessening the exposure to crime. Additionally, despite their popularity at the federal level, housing mobility programs such as MTO, HOPE VI or Section 8 have not gained unanimous approval by communities across the U.S. The main source of opposition has come from middle-class communities who fear an upsurge in social problems and erosion of the quality of life as a result of the relocation of poor families into their area. In some cities, such as Denver and Boston, the opposition to relocation programs has been strong enough to impose strict limitations on the maximum number of tenants that can be relocated to a given area (Ludwig and Stolzberg, 1995). Others have doubted the efficacy of mobility programs, citing the demolition of public housing and dispersal of low-income individuals as an excuse for a land grab by urban elites (Bennett and Reed, 1999). In addition to uprooting public-housing residents from their communities and disrupting existing social ties, there are questions concerning the quality of the housing and neighborhoods to which they move (Venkatesh, 2002; Venkatesh and Celimi, 2004). Studies describe regional variation in neighborhood conditions of voucher housing, with tight rental markets (i.e. New York, Chicago, and Boston) creating obstacles to obtaining housing outside of poor neighborhoods (Marr, 2005; Oakley and Burchfield, 2009). So while voucher housing tends, on average, to be less concentrated than traditional public housing, because of its reliance on the private market, it is likely that cities vary in terms of the degree to which these types of programs are being utilized and the success they have achieved (Goetz, 2003). As a result, some cities may continue to have high concentrations of public housing units within their borders while in others those individuals receiving public housing assistance may be more widely dispersed. 51

65 As research has shown, the existence of public housing is intricately related to neighborhood crime rates in a number of ways. With cities utilizing a variety voucher and relocation programs in conjunction with large housing projects during the current era, there is reason to believe that the distribution of these low-income households may impact the concentration and clustering of crime within American cities Urban Redevelopment In addition to public housing policy, there are other city-specific programs designed to alleviate social problems which have the ability to impact the concentration of crime within a given city. Specifically, targeted economic incentive programs, known as enterprise or Empowerment Zones (EZs), were first unveiled during the 1980s to create jobs in economically depressed urban areas across the country. Long before that, large scale urban renewal programs often targeted social problems, using the relocation of businesses, the demolition of structures, and the relocation of people to reshape the distribution of certain conditions within cities across the U.S. As elaborated below, these programs, introduced at the local-, state- and federal-level, have the potential to impact the concentration and clustering of crime through the alleviation or displacement of economic deprivation. This section reviews the development of these programs as well as existing research regarding their impact on communities in select cities across the U.S. Large scale urban renewal projects were first introduced in the U.S. sometime between World War I and World War II. New York, for example has undergone a number of dramatic changes spawned from urban renewal projects, beginning with the design and construction of Central Park in 1909 and the construction of new bridges, highways, parks and housing projects between 1930 and 1970 (Ballon and Jackson, 2007; Zipp, 2010). Other cities across the U.S. began to create redevelopment programs in the late 1930s and 1940s. Many of these projects focused on the redevelopment of economically disadvantaged areas by local public housing authorities and often lead to the demolition of impoverished neighborhoods and the construction of new public housing developments within cities such as Chicago, St. Louis and Los Angeles. Pittsburgh is also a city with a long history in urban renewal. In the 1950s, under the influence of powerful economic elites such as R.L. Mellon a large section of the downtown area of Pittsburg as demolished to make room for public parks, office buildings and a sports arena (Pritchett, 2003). Similarly, in Boston, almost one third of the city was demolished including the West End (a historically impoverished area) to make 52

66 room for a new highway, government and commercial buildings as well as new moderate-income housing developments. Importantly, however, redevelopment projects are not always as dramatic as those cited above. More recently, federal-, state- and local governments have utilized projects based much less on destruction and more on revitalization and investment through the use of tax incentives to bring new small and big businesses to certain areas within a given city. During the 1980 s many states rolled out spatially targeted economic development policies for addressing the concentration of poverty in urban areas. Known generally as enterprise zones, these programs provided economic incentives (usually through tax abatements) for companies to create jobs in economically depressed areas. Following implementation at the state-level, the Federal Government created a similar program, called Empowerment Zones, which coupled tax incentives and wage credits with large amounts of federal funding for community development. As of 2004, the EZs cover over 700 census tracts across 31 communities (Greenbaum and Bondonio, 2004). Research examining the effects of spatially targeted incentive programs, such EZs, remains relatively limited and has concentrated on various state-level programs with only a few studies examining the effect of the programs at the federal level (Spencer and Ong, 2004; McCarthy, 2003, O Keefe, 2004). In 2001, an assessment of the EZ program was prepared for HUD to assess the program s initial outcomes. In an evaluation of the six urban zones originally selected for inclusion in the program, the report concluded that although total employment increased in the zones as a whole, this growth could not be attributed to the EZ program itself (HUD, 2001b). Despite the mixed findings overall, there is evidence that some individual EZs saw significant gains in employment. For example, the EZ in Baltimore, MD saw a significant increase in jobs in the area designated for redevelopment. In spite of some noted successes, it is still relatively unclear whether the EZ program has had a significant and uniform impact on communities across the country. In one of the most comprehensive studies in the area, Oakley and Tsao (2006) used propensity score matching techniques to examine the effect of empowerment zones on a variety of socio-economic outcomes in neighborhoods across Chicago. While they find some a marginal effect on rates of poverty and unemployment, they find little evidence that the program impacted other socioeconomic outcomes such as education or housing prices (Oakley and Tsao, 2006). Unlike the broad socioeconomic changes discussed in the section above, the impact of urban renewal programs on the concentration of poor economic conditions is less clear. While disadvantaged areas may be singled out by policy makers as areas prime for redevelopment, the 53

67 unequal distribution of conditions such as poverty may remain relatively unchanged. That is, the concentration of poverty in a select number of areas within a given city would only change significantly if a sizeable number of these were targeted for redevelopment and saw each dramatic improvement in their economic standing. On the other hand, dramatic redevelopment or urban renew projects (sometimes referred to as gentrification), which displace disadvantaged populations by effectively pricing them out of the area which has been redeveloped may lead to an increase in the concentration of poverty as those individuals must relocate to other affordable areas which are also economically disadvantaged. Many of the large scale projects, such as the one in Boston mentioned above have been criticized for this very reason (Teaford, 2000). The theoretical impact of urban renewal and redevelopment programs on the clustering of economic disadvantage is perhaps more straightforward. If specific areas within a large cluster of high-poverty neighborhoods were targeted and the project successful, redevelopment projects may effectively reduce the spatial concentration of poverty by breaking these large clusters into smaller ones which are more dispersed throughout the city. For example, large scale urban renewal projects such as design and construction of Central Park in New York, the development of the Riverwalk in San Antonio and the redevelopment of the West End in Boston almost certainly had an impact on the spatial concentration of poverty as large swathes of each city were completely redeveloped to make room for public services, highways and new development. Thus, it is believed that in cities where these types of projects have been undertaken, the clustering of poor economic conditions may have been impacted. Similarly, the use of EZs to revitalize specific areas within a given city may impact the spatial concentration of poor economic conditions, although they may do so in a less dramatic fashion. Overall, research on geographically targeted economic development initiatives, such as the EZ program, and large urban redevelopment projects such as those in the cities of New York, Pittsburgh and Boston, have had an effect on disadvantaged communities in select cities across the country. Although existing research in this area remains limited in a number of ways, there are reasons to believe that cities which have undertaken urban redevelopment may have seen changes in the concentration and clustering of conditions known to be associated with higher rates of crime (i.e. poverty and unemployment). Due to its limited application in select cities across the country, and variation in the scale of redevelopment, urban redevelopment is a clear candidate for contributing to the suggested variation in the concentration of crime across cities in America. 54

68 2.4.3 Policing An additional factor with clear implications for the concentration of crime in a given area is the police s response to it. Stemming from research which suggests that undirected patrol has little effect on reducing crime, several new approaches to dealing with crime in a given city have emerged in recent years (Kelling et al., 1974; Sherman, 1990). Specifically, research on hot-spot, or problem oriented policing suggests that these practices, if implemented effectively, can have an impact on rates of crime in cities across the country. These forms of policing remain relatively new, and are a significant change from previous policing efforts and traditional patrol techniques. As elaborated below, it is believed that directed patrol (whether directed towards offenders, places, victims, or offenses) may have a significant impact on the concentration and clustering of crime within cities across the U.S. and thus, contribute to its variation. Stemming from the research that suggests a significant clustering of crime in a small number of places, or hot spots, a number of researchers have argued that crime can be reduced more efficiently if the police focused their attention to these deviant places (Eck, 1997; Sherman and Weisburd, 1995; Braga, 2001). Unlike most innovations in policing, the emergence of hot spot policing can be traced back to the research showing that the risk of crime is extremely localized, even within high-crime neighborhoods (Sherman et al., 1989; Weisburd et al., 2012). Indeed, police officers have long recognized that certain locations within their beats tend to contribute significantly to the crime problem in their area. Until recently, however, police strategies did not focus systematically on crime hot spots, instead using both saturated and random patrols to combat crime across American cities. In recent years, with the advancement in crime tracking and mapping technology, hot spot policing has become a popular way for police departments to fight crime. Many police departments now have the capability to analyze crime data in more sophisticated ways, and using programs such as Compstat and STAC are able to implement problem-solving strategies to control hot spot locations (Weisburd et al., 2003). A recent report found that 7 in 10 departments with more than 100 sworn police officers reporting using crime mapping to identify crime hotspots (Weisburd et al., 2003). A growing body of evidence suggests that focused police interventions, such as directed patrols, proactive arrests, and problem-oriented policing, can significantly reduce crime in highcrime areas (Braga, 2002; Eck, 1997, 2002; Weisburd and Eck, 2004; Braga and Bond, 2008). To 55

69 date, the most systematic review of research on the impact of hotspot policing was conducted by Anthony Braga (2001). Results from a series of quasi-experimental research studies suggest a statistically significant reduction in total calls for service and total crime incidents as well as varying reductions for different types of crime (Braga, 2001). Of the nine studies included in the review, seven studies reported statistically significant reductions in crime for treatment sites compared to the control sites. Furthermore, the bulk of evidence suggests that these reductions in crime are not merely a result of crime displacement, but rather real reductions in criminal activity stemming from police intervention (Braga, 2001). None of the studies reviewed reported a substantial spatial displacement of crime into the areas surrounding the locations targeted by police (Braga, 2001). This is important because if crime simply moved around the corner, the concentration of crime would remain unchanged. Importantly, however, the results of four studies suggested a possible diffusion effect of focused police interventions, where crime in nearby areas declined as well. One of the studies reviewed reported that in Kansas City, none of the tracts surrounding the target location saw a significant increase in crime post-intervention, and that two of the contiguous areas saw significant decreases in crime as well (Sherman and Rogan, 1995). Similar results were reported in Houston and St. Louis where nearby tracts were seen to reap the benefits of targeted interventions in neighboring areas (Caeti, 1999; Hope, 1994). Although the phenomenon of displacement and or diffusion are notoriously difficult to measure with any certainty, the results of these studies suggest that the selection and targeting of crime hot spots has a significant effect on the distribution of crime in a given area (Weisburd and Green, 2004; Short, Brantingham, Bertozzi and Tita, 2009). However, much like the urban redevelopment and revitalization projects discussed above, their impact is not as clear as some of the other factors discussed. For example, directed policing may be able to reduce crime in a select number of hotspots, thus reducing the spatial concentration of crime at the city level. Past research has shown that that not only do these strategies have the ability to impact crime in the target neighborhood, the benefits of increased police activity may to spill over into the surrounding areas, thus diffusing crime even further which may break up large clusters of criminal activity. Research which has focused on the impact of hotspot policing and the potential displacement of criminal activity also suggests that by combating crime in select areas may actually contribute to higher concentrations of crime because a larger proportion of the total volume of crime would then occur in a smaller number of areas (those which were not targeted). Finally, if the effect of policing tactics 56

70 had a uniform effect on all high-crime areas within a given city, the concentration of crime would remain unchanged. Therefore, although it is anticipated that in cities which have adopted some form of hot spot policing approach it crime may be less spatially concentrated than in cities where police have clung to more traditional approaches to crime control, the impact on the traditional measure of concentration of crime is less clear. In section 2.4 I have argued that a number of city-specific programs and policies exist which have the potential to contribute to variation in the concentration of crime at the city-level. Specifically, programs designed to combat high rates of poverty, such as public housing and redevelopment projects may impact the distribution of a city s most disadvantaged populations leading to varying degrees of crime concentration and varying degrees of clustering. In addition, policing strategies unique to particular cities, such as hot spot policing techniques and directed patrols, may contribute to variation in the concentration or clustering of crime in cities across the U.S. Unlike the shocks of deindustrialization and immigration discussed above, these policies and programs stem from local decision maker s goal of reducing crime and social problems in their community. Because these programs have been shown to have an impact on conditions closely associated with crime and are likely to vary significantly from one locale to the next, they have the potential to increase the between-city variation in the concentration of crime. Chapter 2 and its subsections takes a broad look at the literature on aggregate crime rates, suggesting a number of potential avenues by which crime may be more highly concentrated in some cities than in others. Highlighted is the importance of a number of large socioeconomic changes which have occurred over the past several decades. Specifically, changes in the labor and housing markets resulting from the wide scale retreat from manufacturing and the increased suburbanization of cities which occurred since the 1970s has had profound effects on the urban form and distribution of conditions well-known to be associated with rates of crime. Additionally, the impact of these broad social changes is likely to be contingent on a number of other city-specific conditions such as public transportation infrastructure and housing policy, leading to a large amount of variation between cities. Finally, local and state governments across the country have taken different approaches to dealing with certain social problems, using a wide variety of policies and programs to combat the largest issues facing their communities. Taken as a whole, the theories and the research reviewed above cast doubt the claims that the concentration of crime is unlikely to vary from one city to another, especially once the spatial dimension of concentration is considered. The first goal of this dissertation is to evaluate the possibility that the concentration of crime varies from city-to- 57

71 city using two measures that represent the distribution and clustering of crime within cities across the country. After establishing the extent to which the concentration of crime varies across cities, the second goal of this dissertation is determine that variation has implications for the total volume of crime observed at the city-level. Chapter 3 reviews aggregate-level research on city-level crime rates, and goes on to suggest that there are theoretical reasons to believe that the degree to which crime is concentrated (both spatially and aspatially) within a city s boundaries may have impact the overall rates of crime in a given city. 58

72 CHAPTER THREE THE CONCENTRATION OF CRIME AND CITY CRIME RATES As Chapter 2 argues, both theory and existing empirical research suggests that the concentration and clustering of crime may vary significantly from city-to-city. In addition to advancing general knowledge on the distribution of crime both within cities across the country by exploring the potential for the concentration and clustering of crime to vary across cities, this dissertation contributes to research on aggregate crime rates by suggesting that the degree to which crime is concentrated within a city s boundaries may have an impact on overall city crime rates. That is, the degree to which crime is concentrated and/clustered (i.e. aspatially or spatially concentrated) within a given city may have an effect on the total volume of crime observed at the city-level, independent of the known predictors of city crime rates. Drawing corollaries from epidemiological research on the spread and containment of disease, as well as reviewing past research on the potential contagious nature of violence, Chapter 3 outlines a series of arguments suggesting that an examination of the concentration and clustering of criminal events may add to our understanding of between-city differences in crime. 3.1 Existing Knowledge on City Crime Rates Why do some cities have higher rates of crime than others? This question has been the subject of a large amount of empirical research over the past several decades. Sociologists and criminologists have attempted to explain between-city differences in crime using a number of criminological theories, most of which have stemmed from the pioneering work of the Chicago School. Theories regarding the effects of social disorganization, economic deprivation and strain as well as the criminal propensity of certain groups have long been used to explain the variation in crime rates seen across cities, counties, and metropolitan areas. The goal of research in this area is to isolate the characteristics of geographic areas that lead to high rates of crime (Schuessler, 1962; Short, 1985). Prior research devoted to city-level variation in crime has drawn on measures of a wide range of social, demographic, ecological and economic conditions present in cities and other large aggregates across the U.S. The current section reviews existing theory and research on the 59

73 factors associated with city-crime rates with an eye to how adding information on the concentration and clustering of crime may contribute to literature in this area. Many of the commonly considered covariates have their roots in the classical Chicago school studies of urbanization and criminal activity (Wirth, 1938; Shaw and McKay, 1942). In their description of social disorganization theory, Shaw and McKay (1942) suggested elevated rates of social disorganization exist in large, densely populated and racially diverse areas as well as places characterized by higher levels of population instability and economic disadvantage. They argued that these structural conditions lead to the weakening of social control, ultimately translating into higher rates of crime. Variants of this argument have been made by Kornhauser (1978) and Sampson (1985) which have suggested that community characteristics such as those listed above have an impact on family structure, informal social control networks, community attachment, anonymity, and the capacity for guardianship. An additional consequence of economic disadvantage and ethnic heterogeneity suggested in the literature is the formation and transmission of deviant and violent subcultures. Accordingly, a major component of Shaw and McKay s theory is that heterogeneous, low-income, urban communities spawned the formation of delinquent groups (e.g. gangs) contributing to higher rates of crime. Much of the research which was spawned from Shaw and McKay s (1942) initial work was focused on the impact of poor economic conditions on rates of criminal behavior. Various strain, social control, and subcultural explanations have been offered to account for the high rates of crime observed in disadvantaged areas (Merton, 1938; Miller, 1958). For example, Blau and Blau (1982) draw a theoretical connection between economic disadvantage and rates of crime using insights from Merton s (1938) strain theory. They suggest that as a consequence of high levels of inequality, those living in poverty are more likely to feel resentment, frustration, hopelessness and alienation, which in turn produces a sense of injustice, discontent and distrust (Blau and Blau, 1982, p.119). They argue that economic inequality, or the unequal distribution of wealth, between groups within society, is more salient to the explanation of crime rates than the absolute level of disadvantage. Shaw and McKay s (1942), on the other hand, argued that high rates of poverty were just one of many conditions present in disadvantaged communities which contributed to social disorganization, low levels of social cohesion, and diminished levels of social control. Finally, poverty and economic disadvantage have also been associated with the creation of subcultures valuing toughness, and the use of violence in the resolution of disputes (Miller, 1958; Wolfgang and Ferracuti, 1967; Anderson, 1994; 1999). While the causal role that economic hardship plays in promoting criminal behavior 60

74 differs between each of these perspectives, most explanations suggest that the existence of poverty within a stratified society increases levels of strain, weakens institutional legitimacy and undermines the social bonds between these institutions and the poor. Empirical results from studies of crime generally support a positive relationship between both absolute and relative levels of deprivation and rates of crime at the aggregate level (Jacobs, 1961; Blau and Blau, 1982; DeFronzo, 1983; Parker and McCall, 1999; Sampson, 1985; Wadsworth and Kubrin, 2007; Sampson and Wilson, 1995). A central claim made by Shaw and McKay (1942) is that population change and turnover has negative consequences for levels of social control within a community. The systemic model of community structure argues that residential stability is strongly related to community attachment and involvement, and that residential stability leads to the formation of social networks, community cohesion, and informal social control (Bursik and Grasmick, 1993; Sampson and Groves, 1989). Areas with more stability, therefore, should have lower rates of crime compared to those with a more transitory population. Aggregate-level research conducted to date generally suggests that high rates of population mobility are indeed associated with higher rates of crime although findings from contemporary research are less consistent (Stark, 1987; Morenoff and Sampson, 1997; Ousey and Kubrin, 2009). Also related to levels of informal social control, population size and density and heterogeneity have also long been associated with the prevalence of criminal activity. Racial heterogeneity is thought to contribute to diminished levels of social control through its effect on social ties (Warner and Rountree, 1997). Due to communication and identification barriers associated with different races, as well as high rates of population turnover, areas which are more heterogeneous in terms of racial composition are thought to be less cohesive and suffer from lower levels of social control. Weak ties resulting from high levels of heterogeneity limit the ability of residents to agree on common set of values or to solve community problems (Bursik, 1989; Kornhauser, 1978). Population and housing density have also been linked to rates of crime through the concept of anonymity (Roncek, 1981). High population density makes it less likely that residents are able to recognize their neighbors, or more importantly outsiders, and may be less likely to engage with members of their neighborhood or to provide adequate guardianship. Several studies report a significant association between population size, heterogeneity and density and crime rates at the aggregate level (Blau and Blau, 1982; Messner, 1982; Roncek, 1981; Roncek and Faggiani, 1986; Warner and Rountree, 1997). 61

75 Although largely unexplored in early ecological research, an increasing number of studies have considered the potential for family structure to impact rates of crime at the aggregate level. High levels of family disruption (e.g. divorce or unwed mothers) may lead to higher rates of crime by decreasing levels of informal social control (Sampson and Groves, 1989; Beaulieu and Messner, 2010). Examples of informal social control include neighbors general surveillance, supervision of youth activities and intervening for the good of the community (Sampson, 1986; Sampson and Groves, 1989). Felson and Cohen (1980) note the potential influence of family structure not just on social control of offenders, but also on the ability of family structure to shape the patterning of criminal targets and opportunities. As articulated in their routine activities theory, predatory crime requires the convergence of offenders, suitable targets and the absence of effective guardianship within time and space. Compared to married couples, single and divorced persons may be especially vulnerable to crime as a result of decreased guardianship both at home and in public. The role of family structure in the generation of crime has been largely supported in existing research. Prior empirical research has reported a positive association between measures of family disruption (e.g. percentage female-headed households, or divorces rates) and rates of crime (Beaulieu and Messner, 2010; Block, 1979; Messner and Tardiff, 1986; Roncek, 1981; Rountree and Warner, 1999). An additional community characteristic which has generated a significant amount of empirical research on between-city differences in crime is focused on the effects of the age structure of the population. Age structure is tied to higher rates of criminal offending through a number of theoretical perspectives which focus on both the criminal propensity of the young adult population and their heightened risk of victimization due to risky lifestyles. First, it has been long observed that crime is disproportionately committed by teenagers and young adults (e.g., those 15 to 29). Two criminological schools of thought are commonly used to explain the age-crime connection, namely elements of strain theory and social control theory (Greenberg, 1985; O Brien et al., 1999). Some argue that the more tenuous position of youth within the labor market relative to older people, coupled with peer pressure to meet and achieve certain expectations and goals, may draw young people disproportionately into lucrative but illegal activities (Greenberg 1985; Phillips, 2006). Secondly, social control theorists note that during the formative years of adolescence and young adulthood, familial attachments and social connections are loosened, social control is weakened, and the young are free to violate group norms (O Brien and Stockard 2002; Tittle 1988). Thus, both the strain and social control perspectives suggests that where there are greater number of these individuals in their crime-prone years, higher crime rates are likely to result. Additionally, Cantor 62

76 and Land (1987) point out that teenagers and young adults not only commit crimes at a higher rate than those in other age groups but are more likely to become victims. This routine activities/lifestyle theory of victimization argues that an increase in the participation of work and leisure activities outside the home is associated with an increase in crime rates (Cohen and Felson, 1979; Cohen, Felson and Land, 1980). Accordingly, it is believed that the proportion of young adults within a given city can have an effect on crime through both number of motivated offenders and the supply of potential victims in a given area. As described above, the list of structural characteristics believed to impact aggregate crime rates is substantial. Furthermore, although past research has found support for the relationships described, results from the multitude of studies in this area are far from unanimous. In perhaps one of the most widely cited papers written in this area, Land, McCall and Cohen (1990) examined several of the structural characteristics most commonly associated with higher rates of crime and developed a well-supported empirical model to explain the between-area variation in crime over a period of three decades. In a detailed review of 21 studies, the authors noted that while the methods employed across studies were relatively similar, they generated highly-inconsistent results, leading to questions regarding the most salient predictors of crime at the aggregate level. What emerged from this paper were two important contributions to research on aggregate crime rates. First, Land his colleagues contributed to the field by applying statistical procedures such as principle component analysis and index construction to correct for problems of collinearity between many of the structural characteristics associated with rates of crime. Secondly, authors expanded the list of structural characteristics associated with aggregate crime rates, drawing theoretical connections between resource deprivation, racial composition, family structure and various other social and economic characteristics and rates of crime. The creation of indices minimized the problems of associated with including a large number of convariates that are strongly related to one another by combining several variables into a single measure. For example, the resource deprivation index, as operationalized by Land and colleagues in 1990, is composed of the percentage Black, percentage of families living below poverty, Gini index of family income inequality, median family income (logged), and the percentage of children not living with both parents. What resulted from this work was robust baseline statistical model which included an index of resource deprivation, a population structure index, percent divorced, unemployment rates, percentage of the population aged 15 to 29 and a southern region dummy variable. 63

77 Ultimately, the work done by Land and colleagues provided a solid platform on which to expand empirical knowledge regarding the structural conditions associated with crime at the aggregate level. Recent replications and extensions of their work suggest that variation in crime rates among U.S. cities are a function a number of structural characteristics (McCall, Land and Parker, 2010). Research over the past 40 years has found remarkably consistent evidence that city crime rates are affected by the social demographic, and economic structural features unique to a given city. Specifically, the following five generalizations have emerged from the existing research. First, net of other factors, city crime rates have consistently been shown to be related to the population structure of cities. Larger and more densely populated cities have been shown to have higher crime rates than those with smaller, less dense population bases. This finding is consistent with routine activities theory and other opportunity theories of crime which contend that all other things equal, increases in the convergence of attractive targets with motivated offenders within time and space in the absence of capable guardians will lead to increases in crime (Cohen and Felon, 1979). Second, high levels of unemployment, economic deprivation and social inequality have consistently been shown to be associated with higher crime rates at the city-level (Land, McCall, and Cohen, 1990; Hipp, 2011). This finding is consistent with classical strain theory s emphasis on structurally-induced frustrations which stem from the discrepancy between aspirations and expectations (Merton, 1938). In addition to the strain perspective, routine activities theory suggests that a combination of potential targets (the rich) and motivated offenders (the poor), along with the absence of capable guardians will lead to elevated rates of crime (Cohen and Felson, 1979; Hipp, 2007). Finally, the impact of poor economic conditions on crime rates has been explained using tenants of social control theory (Hirschi, 1969). Social control theory suggests that high levels of economic deprivation are associated with weaker bonds to society and mainstream institutions such as the economy and the family, resulting in higher rates of crime. Third, net of other predictors, city-level crime rates have been shown to be associated with to prevalence of divorce (Blau and Blau, 1982; Beaulieu and Messner, 2010; Pratt and Cullen, 2005; Sampson and Groves, 1989). Social disorganization theory suggests that divorce may lead to a greater family disruption, resulting in lower levels of participation in community organizations and a diminished capacity for social control. Sampson (1987) cites research suggesting that communities with high rates of family disruption are characterized by low rates of community participation thus weakening levels of formal social control. Sampson and Groves (1989) also highlight how family 64

78 disruption may impact the development of informal social control, specifically linking divorce rates to the supervision of street-corner teenage peer groups. Finally, control theory suggests that divorce weakens or breaks the bonds associated with familial institutions, resulting in higher propensities for crime. Forth, the prevalence of individuals aged has been shown to be associated with higher city-level rates through a number of theoretical perspectives which focus on both the criminal propensity of the young adult population and their heightened risk of victimization due to risky lifestyle choices. Finally, net of other factors, the geographical location of an ecological unit in the south has been linked to higher rates of crime at the aggregate level. Recent research, however, suggests that the regional distinctiveness of the South, with respect to homicide rates in particular, has declined in recent decades (McCall, Land, and Parker, 2010). In sum, city crime rates have been shown to be a product of several structural characteristics that are believed to work through their impact on levels of strain, social control and the abundance of criminal opportunities. The relationship between the structural factors listed above (population structure, economic deprivation, unemployment, family disruption, age structure, and geographic location) and crime has been well established in prior empirical research and can be asserted with a relatively high level of confidence (McCall, Land, and Parker, 2010; Pratt and Cullen, 2005). Land and colleagues (1990) conceptual model and methodological procedures have provided a strong foundation on which to build and refine models of city-level crime rates. A recent meta-analysis suggests that these structural characteristics are robust predictors of crime and future studies that fail to control for the effect of these run the risk of being misspecified (Pratt and Cullen, 2005). Since 1990, using the insights from the work of Land et al.(1990), scholars have continued to add to the list of social and structural characteristics thought to contribute to aggregate-level crime rates. Compelling arguments have been made for the potential of several additional factors to impact rates of crime including, the concentration of poverty (Krivo and Petterson, 1996; Lee, 2000), residential segregation (Sampson, 1985; Peterson and Krivo, 1993), police force size (Kubrin et al., 2010; MacDonald, 2002; Sampson and Cohen, 1988), immigration (Butcher and Piehl, 1998; Martinez and Lee, 1999; Ousey and Kubrin, 2009; Stowell et al., 2009) drug market activity (Baumer, Lauritsen, Rosenfeld, and Wright, 1998; Cork; 1999; Corman and Mocan, 2000; Grogger and Willis, 2000; Ousey and Lee, 2004; Fryer et al., 2013;), local political cultures (Stuckey, 2003) and even the prevalence of lead poisoning (Nevin, 2000). 65

79 Of those city-level factors mentioned directly above, the concentration of poverty and racial and ethnic residential segregation have been also linked to aggregate crime rates through the mechanisms outlined by social disorganization theory and its contemporary variants. The theoretical arguments connecting both of these city characteristics to rates of crime have relied on the relatively large body of empirical research which suggests that higher levels of racial and or socioeconomic inequality are associated with higher rates of crime (see for example Blau and Blau, 1982; Land, McCall and Cohen, 1990). Specifically, it has been argued that highly disadvantaged communities lack the levels of social control necessary to control crime and may spawn cultural adaptations which are criminogenic in nature. This perspective contends that residents of highly disadvantaged communities may have difficulties in coming together to prevent crime and disorder through both informal and formal control mechanisms (Bursik and Grasmick, 1993; Shaw and McKay, 1942; Sampson et al., 1997). It has also been suggested that the isolation caused by economic and racial or ethnic segregation may generate subcultures conducive to higher rates of crime (Anderson, 1999; Wilson, 1987). The segregation of residents along racial and/or ethnic lines, as well as the concentration of poverty and social isolation that comes along with that stratification has been shown to be associated with elevated rates of crime and violence at the city-level (Peterson and Krivo; 1993;1999; Shihadah and Flynn, 1996). For example Peterson and Krivo (1993) found that levels of black/white segregation were significantly and positively associated with rates of black homicide in a sample of large cities in Similarly, Shihadeh and Flynn (1996) found that segregation was associated with rates black homicide and robbery in a sample of cities in Peterson and Krivo (1999) went on to show that levels racial residential segregation had an effect to city black homicide rates and that this impact was partially or fully mediated by the concentration of deleterious economic conditions. The found no connection, however, between segregation and white homicide rates and argued that this could have been expected as segregation does not increase the concentration of white disadvantage (Krivo, Peterson, Rizzo and Reynolds, 1998). Taken as a whole, it has been demonstrated that racial segregation affects the concentration of disadvantage across neighborhoods within cities and both have the ability to impact levels of crime and violence at the city-level through a number of mechanisms described above. An additional city-level characteristic associated with rates of crime which has received a great deal of attention in recent years is immigration. Although originally believed to contribute to higher rates of crime through the same mechanisms described by social disorganization theory, the 66

80 results of empirical research on the relationship between immigration and rates of crime at the aggregate level is much more mixed. While social disorganization theory suggests that a large influx of immigrants into a given area will lead to increased levels of disorder and crime through its impact on informal networks and levels of social control, other research on the effects of immigration suggests the possibility that immigration may be inversely related to crime (Palloni and Morenoff, 2001; Zhang and Sanders, 1999). A leading explanation for this inverse relationship is selectivity theory, which argues that immigrants who come to the U.S. do so for the opportunity to improve their life changes and are therefore less likely to fall into the cycle of disadvantage implied by social disorganization theory. It has also been suggested that immigrants are more likely to have access to strong social networks which provide connections to the job market and additional support to those in need (Palloni and Morenoff, 2001; Hagan, Levi and Dinovitzer, 2008). Finally, research conducted by Logan and colleagues (2002) highlights that members of certain recent immigrant groups may not be considered disadvantaged at all. Authors note that members of many immigrant groups which have come to the U.S. in recent decades possess higher levels of social capital (i.e. education and economic resources) and although they may settle in ethnically diverse neighborhoods, this may not necessarily translate into higher rates of crime. Recent macro-level research suggests a negative or null effect of immigration on rates of crime. For example, Lee, Martinez and Rosenfeld (2002) find that the size of the recently arrived immigrant population was associated with lower rates of homicide in the city of Miami and that in El Paso and San Diego immigration did not have a significant impact on rates of homicide. Similarly, using a sample of large cities from across the U.S., Butcher and Piehl (1998) found that changes in the size of the immigrant population between 1980 and 1990 were unrelated to changes in overall or violent crime rates. Most recently, immigration has been found to be negatively associated with, or unrelated to levels of crime using a sample of metropolitan areas from across the U.S. (Stowell et al., 2009; Reid et al, 2005). Overall, the growing body of empirical literature on immigration and crime suggests that a complex relationship between the two exists and that any aggregate-level analysis of crime should consider including a measure of immigration. In addition to city demographic characteristics such as racial segregation and levels of immigration, a common perception is that police play a major role in preventing crime. Consistent with a deterrence perspective, it is assumed that a greater police presence (i.e. a larger police force) should reduce crime through its impact on the perceptions of would-be offenders. Specifically, larger police forces may deter criminal activity because would-be offenders believe that they are 67

81 more likely to be apprehended and charged if they choose to commit a crime. Additional empirical research has investigated the deterrent effect of arrest certainty using measures such as the proportion of arrests to the total number of crimes for a given offense within a given city (i.e. Sampson and Cohen, 1988; Kubrin et al., 2010). Although results of empirical work devoted to the impact of arrest certainty and police force size are somewhat mixed, the majority of prior research suggests a negative relationship exists between these measures and aggregate rates of crime (Kubrin et al, 2010; Levitt, 1997; Marvell and Moody, 1996; Sampson and Cohen, 1988). For example, in his panel study of 59 U.S. cities, Levitt (1997) used an instrumental variable approach to isolate the impact of police force size on rates of crime, finding evidence of a deterrent effect. Similarly, Marvell and Moody (1996) found that a significant negative effect of police force size on most of the crime types considered. Finally, using a sample of large cities from across the U.S., Kubrin and colleagues (2010) found that the use of proactive policing (as measured as arrest certainty) was associated with lower robbery rates. As a whole, research devoted to the effect of the police on crime suggests that accounting their presence and/or behavior in some fashion is important to the study of city crime rates. Finally, the impact of illicit drug market activity, especially activity associated the sale of crack cocaine, has received a significant amount of attention within prior research devoted to city crime rates. Both quantitative and ethnographical research suggests that the culture surrounding the illicit drug trade is criminogenic in nature (Baumer, 1994; Blumstein, 1995; Anderson, 1990). As these illicit markets are not subject to formal regulation, high rates of violence may exist as individuals take responsibility for their own protection (Black, 1983). The traditional measure used to estimate the prevalence of crack cocaine included in prior research is the cocaine arrest rate. Results of empirical research in this area suggest that the prevalence of drug market activity has an impact on rates of crime at the aggregate level. For example, Baumer (1994) found the prevalence of crack cocaine was associated with higher rates of homicide across a sample of 24 cities, independent of many of the structural characteristics known to be related to rates of crime. Similarly, Blumstein (1995) concludes that the rise in youth homicide during the late 1980s was a result of the proliferation of firearms and gun violence which erupted in inner-city neighborhoods during the crack cocaine epidemic which occurred during this period. Also, given the fact that illicit drug use fuels the need for quick cash, Baumer and colleagues (1998) found that cities with higher levels of crack cocaine use experienced larger increases in robbery during the late 1980s and early 1990s and that this was coupled with a decrease in burglary during the same period. Authors 68

82 argue that the proliferation of crack cocaine altered the reward structure associated with these two forms of crime, making it more likely that offenders commit robbery rather than use the profits of burglary to generate the cash they need to purchase the addictive drug. Finally, the emergence of crack cocaine has been associated with the rise in black youth homicide during the same time period (Fryer, Heaton, Levitt, and Murphy, 2013). Although the crack cocaine epidemic of the late 1980s has since subsided, the existing evidence suggests that the volume of drug activity in a given city may impact rates of crime. While we know significantly more today than we did 40 years ago regarding the factors most closely associated with the between-city differences in crime, there is still much to be learned regarding the processes that unfold within cities, translating into higher rates of crime for some. As the following sections of this chapter argue, there are reasons to believe that the variation in the concentration and clustering of crime within cities may also add to our understanding of why some cities have higher rates of crime than others. Specifically, net of the commonly included structural characteristics associated with higher rates of crime at the city-level, I argue that the degree to which crime is concentrated and/or spatially clustered within a city s boundaries may have an independent impact on the rates of crime at the city-level. Before discussing the specifics of the models presented in this dissertation in Chapter 4, the remainder of the current chapter discusses the theoretical connections between the concentration and clustering of crime and variation in city crime rates. 3.2 Adding to our Understanding of Between-City Differences in Crime As reviewed above, a long list of factors have been suggested for why some cities have higher rates of crime than others. In the following section, I suggest that including information on crime s concentration may add to our explanation of city-level crime rates. Specifically, I suggest that there are two mechanisms by which it could be anticipated that the concentration and/or clustering of crime with cities may impact total rates of crime at the city level. First, the concentration and/or clustering of crime may impact city crime rates is through its ability to facilitate a process of social contagion. Drawing from prior literature which argues that behaviors conducive to crime and violence have the ability to spread through the population much like a contagious disease (i.e. Loftin, 1986), I argue that it is crime s distribution (i.e. degree of concentration and /or clustering) which makes this process more or less likely to occur. Second, in relation to levels of social control, I suggest that the degree to which crime is concentrated and/or 69

83 clustered within a given city may impact the ability of police agencies to combat the crime problem, contributing to the between-city variation in crime. Section and its subsections discuss the contagious nature of criminal activity, highlighting the potential for preventative and retaliatory actions to contribute to higher rates of crime in cities. Following the discussion of the contagious nature of criminal activity, the relationship between the concentration of crime and these contagious processes is expanded upon. Section discusses whether it should be expected that the processes of contagion should be expected to be more prominent in areas where crime is more highly concentrated, or if in fact the opposite may be true. Here the potential for crime s concentration and clustering to impact the likelihood that individuals adopt these approaches in response to the threat of criminal victimization is discussed, suggesting I could be anticipated that when crime is less concentrated and/or less clustered within a city s boundaries there is a greater potential for a contagious process to occur. Although this assertion goes against observations made in prior research that suggest it is the spatial clustering of crime, particularly crime which is violent in nature, which facilitates contagion (see Loftin, 1986), there are theoretical reasons to believe that a more diffuse patterning of crime could yield higher overall levels of crime and violence. Specifically, lower levels of crime concentration and/or clustering may signal to a larger proportion of the population that the crime problem is not under control, leading to the spread of preventative and retaliatory actions which may be conducive to the spread and escalation of violence. Under these conditions, a larger number of individuals are exposed to crime in their local environment and may be more likely to take action to prevent their victimization. Accordingly, drawing insights from prior research on the perceived threat of victimization, as well as research on the processes of social contagion, Black s theory of self-help and subcultural theories on crime causation, I argue that when crime is less concentrated and/or clustered within a city s boundaries there is a greater potential for a contagious process to occur, resulting in elevated rates of crime and violence. Additionally, as elaborated on below, in relation to social control I suggest that the degree to which crime is concentrated within a given city may impact the ability of police agencies to combat the crime problem. Taking a practitioner-oriented approach, crime is in many respects analogous to a form of disease which needs to be controlled and treated. As such, the concentration and clustering of crime may have an impact on the prevalence of crime at the city-level by affecting efforts to regulate criminal behavior. Thinking about epidemics in a clinical sense, the spread of disease may be easier to combat when it is contained in a relatively small amount of space. In cities 70

84 where crime is highly concentrated (either spatially or aspatially), police may be more effective in identifying problematic areas and allocating their resources more efficiently to contain criminal activity, translating into lower rates of crime overall. A growing body of literature on criminal hotspots suggests that the use of targeted or saturated policing has the ability to reduce crime in focal areas without displacement, thus translating into lower rates of crime. Therefore, it is anticipated that the concentration and clustering of violent (i.e. homicide, robbery and assault), and non-violent (i.e. burglary) crime may be negatively related to crime rates at the city-level. Section of this dissertation reviews research on hot-spot policing techniques, highlighting how the concentration and clustering of crime in a given city may contribute to the effectiveness of police actions, resulting lower in rates of crime and violence The Contagious Nature of Crime Terms such as epidemic or contagion, most closely related to literature on the spread of infectious disease, have made their way into criminology s vernacular. Authors have used the term epidemic to describe the wave of youth violence which hit U.S. cities during the latter half of the 1980s along with the proliferation of crack cocaine use (Baumer et al., 1998; Blumstein and Wallman, 2006; Cook and Laub, 2002; Fryer et al., 2013). Similarly, contagion has been used to describe the growth and spread of international terrorism (Schlesinger, Murdock and Elliott, 1984), social conflict (Buhaug and Gleditsch, 2008), domestic disputes (Mills, 1999), as well as neighborhood violence (Fagan, Wilkinson and Davies, 2000; Loftin, 1986; Topalli, Wright and Fornango, 2002). Most closely related to this dissertation, authors have suggested that aggregate crime rates may be a function of social contagion. Drawing from the literature on the spread of disease, it has been suggested that criminal activity in one s neighborhood may lead to behaviors and values which are conductive to elevated rates of crime, especially violence. The rapid spread and dramatic escalation in crime during the late 1980s has lead some scholars to draw a connection between diffusion of criminal behavior, especially that which is violent in nature, and the spread of disease. Loftin (1986) as well as Fagan and colleagues have written a series of papers which argue that violent behavior has the ability to spread through a population similar to the transmission of a contagious disease. Citing the clustering, escalation and reciprocal nature of violence from numerous studies at both the individual- and aggregate-level, Loftin (1986) argues that violent crime has the ability to draw new people into the conflict, therefore spreading throughout social networks much like a disease would. Similarly, Fagan and Wilkinson 71

85 (1988) as well as Fagan and Davies (2004) discuss the transmission of social norms and behaviors which result in higher rates of crime in the context of social contagion. Research in this area has drawn heavily from the literature on the adaptations to the threat of victimization (Black, 1983) and development of violent subcultures discussed by Anderson (1994). The theory of self-help and the generation of subcultures conducive to the use of violence both share a common theme: Those exposed to crime may react to it in ways which lead to escalation of violence. Specifically, exposure to crime within one s neighborhood may signal to residents that formal mechanisms of control have failed, leading them to take measures into their own hands. First, in order to prevent their victimization, people within high-crime neighborhoods may take steps to prevent their victimization thereby changing their behavior either by arming themselves, joining gangs, or by adopting a subculture which promotes the appearance of toughness and supports the use of interpersonal violence in the resolution of disputes (Anderson, 1999). Secondly, if victimized, those that feel that no formal recourse is available are more likely to retaliate, thus contributing to the escalation of crime described by Loftin and others. The following subsections discuss each one of these pathways in greater detail Adversary Effects and the Code of the Street. One way in which crime has been considered analogous to a contagious disease is through the impact which crime has on the actions of individuals who are exposed to it. As individuals become exposed to crime within their immediate surroundings they may be more likely to feel as if they need to take action in order to protect themselves. Just as people take precautions against becoming infected with the common cold, H1N1 or a sexually transmitted disease, they often take precautions against being victims of crime. These actions can take many forms, some of which are likely to contribute to higher rates or crime, especially violence. Specifically, as a response to the threat posed by criminal activity within their community, individuals may arm themselves, join a gang, or adopt a behavioral code which they believe makes it less likely they will be victimized, ultimately contributing to higher rates of crime. Felson (2009) describes this response as an adversary effect suggesting that what people do in response to the exposure to crime may in fact translate to higher rates of crime, especially violence, overall. Below, each of these responses to criminal threat is discussed in greater detail. One way in which the fear of crime could contribute to high rates of crime within a given city is through the proliferation of gun ownership. Loftin (1986) portrays this process in his exploration of the surge in homicides which occurred in Detroit in the late 60 s and early 70 s. He describes an arms race, of sorts, where people strove to arm themselves in order to protect 72

86 themselves from being victimized, ultimately leading to higher rates of homicide in subsequent periods. He concludes that the prevalence of assaultive violence within neighborhoods across the city lead to the increased use of weapons and violence, and had the ability to spread across space and escalate out of control, displaying epidemic-like pattern. Similarly, Mc Dowall and Loftin (1983) suggest that when people doubt the ability of formal security measures (i.e. the police) they turn to protective gun ownership as a means of self-help to prevent victimization. Individual-level research suggests that protective firearm ownership is inversely related to measures of confidence in the criminal justice system (Kleck; 2003; Jiobu and Curry, 2001). Additionally, aggregate-level research suggests that gun ownership rises as violence and civil disorder increase, and falls when more resources are allocated to crime control (Kleck; 1979; Mc Dowall and Loftin, 1983). Overall, although research on gun ownership is far from unanimous, most reports suggests that elevated levels of fear play a role gun ownership (Newton and Zimring, 1969; Lizotte et al., 1994; Smith and Uchida, 1988). Importantly, the proliferation of gun ownership, in general may not translate into elevated rates of crime. The literature on the relationship between guns and crime is vast, the findings mixed, and the results highly contested (see Moody and Marvell, 2005 or Donohoe and Ayers, 2009 for a review). However, there are compelling reasons and empirical evidence to believe that the prevalence of guns may lead to elevate rates of lethal violence (Loftin, 1986; Cook and Ludwig, 2006; Ayres and Donohue, 2003). For example, using a number of proxies of gun ownership rates, Duggan (2001) found that increases in gun ownership lead to significant increases in homicide at the county-level. Accordingly, I argue that the knowledge of crime within one s own neighborhood may contribute to perceptions regarding the effectiveness of the criminal justice system and lead a larger number of residents to arm themselves in response to the threat of victimization. In those cities where a larger proportion of the population has armed themselves in response to this threat, higher rates of violence may be more likely to result. A second response to the fear of crime which may contribute to higher rates of violence is the creation and perpetuation of gangs. Research has shown that conflict and the threat of violence is central to both the formation and continuation of youth gangs (Thrasher, 1927; Klein, 1971; Decker and VanWinkle, 1996). Results from a 5-year longitudinal study of adolescents in six U.S. cities, suggest that a large percentage of self-identified gang members joined their gangs for protection (Peterson, Taylor and Esbensen, 2004). Similarly, Decker and Van Winkle s (1996) study of St. Louis gang members revealed that 86 percent of their sample indicated that protection was 73

87 one of their motives for joining a gang. Other ethnographic work conducted by Padilla (1992) and Vigil (1988) further suggests that the fear of victimization led individuals to join gangs for their own protection. Furthermore, gangs offer a social context in which violence is more normative. The willingness to use violence is a key characteristic distinguishing gangs from other peer groups (Horowitz, 1983). Klein (1971) discusses mythic violence as a good example of how gang members are socialized into a group that values the use of violence against rival gangs. Decker (1996) also describes violence as a central feature of gang life. Accordingly, the existence of gangs has been associated with higher rates of violence within cities across the country (Miller, 1958; 1992; Block and Block, 1992; 1993; Decker, 1996; Howell and Decker, 1999). Much of the research on gang behavior has been attributed a larger proportion of their criminal behavior to their involvement in the drug trade and the proliferation of weapons during the crack epidemic of the late 1980s (Fagan, 1990; Klein, 1995; Spergel, 1995; Taylor, 1989). Although fear of victimization isn t the only reason people join gangs and gang activities are numerous (see Howell, 1998 for a review), prior research has shown that the fear of crime may lead some to seek protection in gang membership and the existence of gangs has been shown to contribute to elevated rates of crime in cities across the U.S. A final adaptation to crime within ones neighborhood which may translate into additional violence in certain cities is the adoption of a subcultural value system which promotes the use of violence. Because individuals which perceive elevated rates of crime in their neighborhood are likely to have less faith that police are able to prevent their victimization, and therefore believe they are effectively on their own, they may be more likely to ascribe to what has come to be known as the code of the street (Anderson, 1994). In this context, the street code represents a cultural adaptation to perceived threat and provides guidance on how individuals should act to prevent victimization. Knowledge of the code is thus largely defensive, and becomes necessary for navigating public life. Anderson (1999) discusses that the code of the street both influences and governs behaviors within predominately African American inner-city neighborhoods. He argues that individuals from impoverished inner-city neighborhoods feel alienated from mainstream society and are less likely to believe that the police are capable of providing adequate protection (Black, 1983; Brunson, 2007). The vast majority of the street code deals with the process of campaigning for self-respect and the need to be in control of one s environment in order to guarantee one s safety. Specifically, the 74

88 majority of the street code deals with achieving and holding respect, or juice, but also emphasizes toughness, retribution and violence. Not only does the use of violence build respect, but it is also believed to prevent future victimization (Anderson, 1994; 1999; Stewart, Schreck and Simons, 2006). To date, there has only been a small amount of empirical research on the adoption of the street code. Matsueda, Drakulich, and Kubrin (2006) found that street codes are disproportionately found in Black and Hispanic neighborhoods and in neighborhoods with high rates of violence. Additionally, individuals who perceived discrimination at the hands of police were more likely to adopt the street code, and further this relationship was conditioned by levels of violence within the neighborhood (Intravia et al; 2014). Overall, this small body of research suggests that the code of the street may be more prominent in areas where crime occurs, as individuals in these areas feel they must adopt the code of the street for their own protection. A larger body of research has investigated the impact of the code of the street of criminal behavior. Using data on 867 African-American adolescents, Stewart, Simons and Conger (2002) found that individuals who adopt the street code are more likely to report violent behavior. Similarly, Stewart and Simons (2010) found that violence is higher in neighborhoods where the street culture is more prevalent. In a more general sample, however, street code values failed to predict offending (Piquero et al., 2012). Using a random sample, Piquero and colleagues found that the scope of street code attitudes across a wide variety of individuals and not just inner-city African Americans and that individual s demographic characteristics mediated the effect of the street code on criminal behavior. Overall, past research suggests that the fear of being victimized may lead to the adoption of a set of cultural norms which are more conducive to the use of violence (Brezina et al., 2004; Stewart et al., 2010). Prior research has documented that individuals who reside in neighborhoods which suffer from elevated rates of crime are more likely to adopt the code of the street, and that adherence to the street code is associated with greater involvement in violent behavior. Importantly, however, in addition to the adversary effects described above, which are believed to contribute to higher crime rates at the aggregate level, individuals who perceive crime as a problem may also react to this fear by taking measures that should not contribute to higher rates of crime such as securing their homes, locking their cars, or changing their routine activities to prevent victimization (i.e. avoiding walking alone at night) (Rengifo and Bolton, 2012; Rountree and Land, 1996; Tewksbury and Mustaine, 2003). These actions may be particularly common in response to crimes such as burglary or minor theft, where the fortification of one s home or car is likely to 75

89 reduce the risk of being victimized. However, in line with past research on the contagious nature of crime, I argue that in response to more serious violent crime, that which has also been shown to induce greater amounts of fear (Skogan and Maxfield, 1981), it is perhaps more likely that individuals exposed to these crimes take react in ways conducive to additional violence Retaliation. In addition to the adversary effects described above, retaliatory crime also corresponds with the idea of contagion outlined by Loftin (1986) as well as the principals of self-help described by Black (1983). In order to seek redemption for their victimization, individuals that feel they have no formal recourse may be forced to exercise social control themselves, which commonly takes the form of a violent act (Black, 1983; Reuter, 1983; Rosenfeld, 2009). In retaliatory attacks, or what Felson (2009) labels as dispute-related aggression, the primary goal of offenders is retribution for previous harm done. One criminological theory which lends support to the idea that crime may be contagious in nature is Black s (1983) theory of self-help. He and others have argued that a many behaviors, considered criminal by modern standards, actually represent modes of conflict management, and in that way represent forms of social control (Black, 1983). Black points out that Hobbesian theory would anticipate higher levels of violence and other crime in areas or settings where sources of formal social control (i.e. policing) is underdeveloped or incapable of providing viable control (Black, 1983, 41). The notion of self-help also highlights a number of mechanisms by which the spatial distribution of criminal events may result in higher levels of violence. As crime has been allowed to flourish in certain areas, residents may feel they must express social control in the form of violent behavior because they feel like they do not have any other options. Scholars have called on this argument in their explanation of aggregate-level crime rates and trends (Rosenfeld, 2009; Blumstein and Rosenfeld, 1998; Baumer et al., 1998). Much of the work on retaliatory crime has come from the study of illicit drug markets. Criminal retaliation has been linked to the sale and distribution of crack cocaine and has been implicated as a major factor in the historic levels of violence experienced in the late 80s and early 90s (Blumstein and Rosenfeld, 1998; Jacobs et al., 2000; Harries, 1997; Topalli, Wright and Fornango, 2002). It has been argued that illicit drug markets constitute a virtually stateless society (Black, 1983; Horwitz, 1990; Phillips, 2003). By definition, underground markets, such as those for associated with drugs or stolen property, are not subject to formal regulation. Although individuals involved in the drug market can be punished by the law, they are often unwilling or unable to call upon the law for help when they themselves are victimized (Baumer et al., 1998; Jacobs, 2000; Rosenfeld, 2009; Topalli, Wright and 76

90 Fornango, 2002). Prior research suggests that individuals involved in illicit activities are reluctant to report victimization to the police because they fear being implicated in a crime (Topalli et al. 2002; Wright and Decker, 1997), or are less likely to want to come into contact with the law to begin with (Hindelang, Hirschi, and Weis, 1981). Recent research also suggests that the relationship between an individual s own criminal behavior and the likelihood that they would report victimization to the police is contingent on neighborhood conditions. Using a sample of 832 Pittsburg youth, Berg, Slocum, and Loeber (2013) found that individuals involved in criminal activity were less likely to report their own victimization to the police and that this effect of offending on reporting was stronger for individuals living in disadvantaged or high-crime neighborhoods. Since individuals who engage in illicit activities are unable or unwilling to call on the police for help, retaliation may become a primary way to deal with conflicts (Jacobs and Wright, 2006). Overall, prior qualitative and empirical research has demonstrated a meaningful connection between retaliation and illicit drug markets (Jacobs and Wright, 2006; Kubrin and Weitzer, 2003; Levitt and Venkatesh, 2000; Ousey and Lee, 2004). The code of the street further supports the use of retaliation; to be exploited without fighting back is to be a punk, and to be a punk makes repeat victimization more likely (Jacobs, Topalli, and Wright, 2000). In order to prevent future attacks, these individuals may be forced to exercise their own kind of social control which is commonly violent in nature (Black, 1983; Reuter, 1983; Rosenfeld, 2009; Tedeschi and Felson, 1994). Building on the typologies of Black (1998), Jacques and Wright (2008) suggest that there are two broad types of retaliation: violent and nonviolent. Violent retaliation involves the use of threats or physical force as a form of punishment for previous wrongdoing (Black, 2004). Examples of violent retaliation include retaliatory robberies, kidnappings, and murders (Jacobs, 2000). Non-violent retaliation is a form of revenge which does not use threats or physical force such as burglaries or frauds (Wright and Decker, 1994). Within violent retaliation, the form of retaliation most pertinent to this dissertation, Jacques and Wright (2008) suggest there are two conceptually distinct typologies. The first, violent confiscation, is defined as a retaliatory act that involves both violence and the taking of the wrongdoer s wealth (Jacques and Wright, 2008). An example of this would be a retaliatory robbery, in which an individual uses force in order to steal valuables from the original offender. A drive by shooting on the other hand represents a pure fight response, which has involves violence but does not involve the taking of wealth (Jacobs and Wright, 2006). Each of these retaliatory responses to victimization is thought to contribute to elevated rates of crime surrounding illicit drug markets. 77

91 In addition to the retaliation which is prevalent within illicit drug markets the nature of gang activity is well suited to the social contagion of violence. Empirical work suggests that the retaliatory nature of gang violence is responsible for the rapid escalation and maintenance of high levels of homicide and other forms violence within those groups (Block and Block, 1993; Decker, 1996; Decker and Van Winkle, 1996). Indeed, past research has shown that gang homicides make up a large proportion of all homicide incidents within a number of cities. For example, in percent of the homicides in Los Angeles and Chicago were gang related. By 1994, nearly 45 percent of all homicides in Los Angeles County were gang-motivated (Maxson, 1999). Finally, Decker and Curry (2002) showed that from 1994 to 1996, gang-related homicides accounted for approximately one-fourth of all homicides in St Louis. Additionally, there is evidence that the steep increase in Chicago s homicide rate between 1987 and 1994 was mainly due to a tremendous increase in the number of gang-motivated homicides (Block, Christakos, Jacob and Przybylski, 1996). More recent research suggests that the increase in homicide seen in California between 1999 and 2001 was entirely due to an increase in gang homicides in Los Angeles County (Tita and Abrahamse, 2004). Additionally, and also important to the comparative nature of this study, Maxon (1999) found that gang homicides are not confined to large cities and that 40 percent of the cities who reported the presence of gangs also reported gang related homicides accounting for a large number of deaths. This suggests that gang-violence and its retaliatory nature is not isolated to large cities and may contribute to high rates of homicide in other areas. Also relevant to the idea of social contagion, in contrast to much of the traditional research which sought to explain the prevalence of homicide using the tenants of social disorganization theory, Decker (1996) emphasized the role of the collective behavior in gang violence, suggesting that gang the etiology of gang violence was a product of group processes. In particular, it has been argued that it is the collective behavior of a gang which may lead to the extraordinary rates of violence which have been observed within these groups. Decker (1996) describes a seven-step process in which threats translate into high levels of violence and retaliation. Drawing from structural and cultural theories of violence, scholars have argued that certain types of violent encounters, particularly retaliatory homicides, can be understood as a result of a subcultural value system that develops in response to social disadvantage and mandates deferential treatment of others and aggressive sanctions against those who show disrespect (Kubrin and Weitzer, 2003; 158). Additionally, Papachristos (2009) argues that it is the group context of gang behavior which allows murders to spread through an endemic-like process as gangs take act in order to seek 78

92 vengeance for previous wrongdoing or to prevent future victimization. In this context (conflict between street gangs), individual murders may be viewed as a threat to the group itself and elicit a violent response (Decker, 1996; Papachristos, 2009). The back-and-forth retaliation between gangs may continue indefinitely, thus producing enduring conflicts and elevated rates of violence. Accordingly, I argue the reciprocal nature of gang violence is consistent with a contagion hypothesis in that prevalence of gang-motivated homicides may reflect intrinsic features of the crime itself, not simply the presence of facilitating structural characteristics (Rosenfeld, Bray, and Egley, 1999). In Sections and I have suggested adversary effects and retaliation are two of the mechanisms by which crime rates may be a function of social contagion. The above summary of research on adversary effects (i.e. the proliferation of gun ownership or gang membership) and the retaliatory violence fit well within Loftin s description of contagion and the resulting spikes in violent crime. Section goes on to discuss the relationship between the concentration and clustering of crime and the processes of contagion just discussed, suggesting that there are a number of reasons why it could be anticipated that varying degrees of crime concentration and/or clustering within a city s boundaries may contribute to between city differences in criminal activity Crime Concentration and Clustering and Social Contagion As discussed above much of the research which describes the contagion of crime hypothesis is centered on the violent behavior which surrounds illicit drug markets and gang activities. The rapid spread and dramatic escalation in violence during the late 1980s (at the height of the crack epidemic) has lead some scholars to draw a connection between escalation and spread of criminal behavior, especially that which is violent in nature, and the spread of disease (Baumer et al., 1998; Blumstein and Wallman, 2006; Cook and Laub, 2002). Specifically, Loftin (1986) argues that violent crime (he uses the term serious assaultive violence) has the ability to draw new people into the conflict, therefore spreading throughout social networks much like a disease would. Similarly, Fagan and Wilkinson (1988) as well as Fagan and Davies (2004) discuss the transmission of social norms and protective or retaliatory behaviors which result in higher rates of crime. Importantly, I argue that the spatial distribution of crime (i.e. its concentration and clustering) play an important role in the transmission of attitudes and behaviors associated with higher levels of crime and violence. 79

93 As noted above, within the literature which references the contagion of crime, it is generally assumed that high degrees of concentration and/or clustering of crime are one of the elements that make the social transmission of these criminogenic conditions possible. In fact, Loftin (1986) points to the spatial clustering as evidence of contagion. This assumption is certainly plausible, and is logical given the scholars focus on the spike in violence that occurred during height of the crack epidemic which was generally isolated to disadvantaged inner-city neighborhoods in large urban areas. However, I argue that it is important to consider the possibility that the spatial distribution of crime within cities may impact city crime rates through a contagious process more generally. Specifically, I suggest it possible that when crime is less concentrated and particularly when it is less clustered it may lead to the adoption of behaviors such as adversary effects and retaliatory actions among a larger proportion of city residents, translating into higher levels of crime, especially that which is violent in nature. As mentioned numerous times in the sections above, one of the primary mechanisms by which the distribution of crime may contribute to generation of adversary effects or the use of retaliation is the perception that crime poses a threat. Below I briefly review the literature on the fear of crime, suggesting that the level of concentration and clustering of crime within a given city may have an important impact on residents perceptions of the crime problem. Over the past several decades the fear of crime has become an increasingly significant area of interest within the field of criminology. As a result a large number of empirical studies have been devoted to the factors which are most salient to an individual s fear of crime. For example, it has been well-documented that certain groups may have a greater vulnerability due to certain sociodemographic characteristics such as age, gender and social class and therefore tend to report a greater fear of crime (see Hale (1996) for a review). Accordingly, it has been suggested that women and the elderly report a greater fear of crime as these groups tend to be more physically vulnerable. Racial and ethnic minorities as well as individuals from lower socioeconomic groups also tend to report levels of fear due to the fact that they often have fewer financial resources to protect themselves or their homes against crime such as purchasing a weapon or installing an alarm system. Other empirical findings suggest that individuals who have had direct or indirect experience with criminal victimization report higher levels of fear (Box, Hale and Pack, 1987; Skogan, 1987). It shouldn t come as a surprise that being a victim of a crime, or having direct contact with someone who has been victimized, has also been shown to lead to a greater fear of crime. Individuals who have been recently victimized typically report that they are more fearful than individuals who have not been victimized or those whose victimization was in the more distant past (Hale, 1996). 80

94 The social, economic, and structural characteristics which make up neighborhoods have also been shown to be important determinants of levels of fear. Neighborhood characteristics are believed to affect fear both indirectly though their influence on the prevalence of crime and disorder in the neighborhood and directly as they may signal to residents that the community is unable to control residents behavior due to a lack of social control. Consistent with these arguments, prior research provides support for the effect of neighborhood conditions on levels of fear, reporting that higher levels of fear are reported in areas which are more economically disadvantaged (Covington and Taylor, 1991); densely populated (Bankston et al., 1987); ethnically diverse (Kershaw and Tseloni, 2005); and consist of a larger youth population (Hale, Pack and Salked, 1994). Similarly, prior research has demonstrated that visual signs of disorder within one s neighborhood exert a direct influence on the fear of crime (Taylor, 2001; Wyant, 2008). For example, using neighborhood-level data in Philadelphia Wyant (2008) found a significant relationship between the fear of crime and perceptions of low-level disorder, net of the commonly considered individual- and neighborhood-level characteristics known to influence perceptions of risk. Finally, and most central to the arguments made in this dissertation, researchers have examined the relationship between neighborhood crime rates and levels of fear. For example, Skogan and Maxfield (1981) found that people who reported feeling unsafe outside after dark were more likely to live in neighborhoods with higher rates of crime. Similarly, individuals who reported specific forms of crime as a big problem in their neighborhood were more likely to live in areas with higher rates of those types of incidents. Skogan and Maxfield (1981) also reported that the relationship between both of these measures and neighborhood crime rates was stronger for robbery and aggravated assault than for burglary rates, suggesting that violent crime may have a larger impact on perceptions of risk than property crimes. Similarly, Taylor (2001) identified a significant relationship between fear of crime and neighborhood burglary rates while also controlling for pertinent structural characteristics and the prevalence of disorder. In addition, a handful of prior studies conducted at the neighborhood level report a direct link between residents fear of crime and neighborhood crime rates while controlling for individual differences known to impact perceptions of crime (Wilcox-Rountree and Land, 1996; Wyant, 2008). Finally, Brunton-Smith and Sturgis (2011) found that neighborhood structural characteristics, visual signs of disorder, and the incidence of crime all have direct and independent effects on resident s fear of crime. As a whole, the results of prior research suggest that the prevalence of crime in one s neighborhood, especially that which is violent in nature, is significantly associated with higher levels of fear. 81

95 Furthermore, and central to one of the main propositions of this chapter, Skogan and Maxfield (1981) found that since burglaries tend to be more widely dispersed (i.e. less concentrated), they stimulate fear in more places and are responsible for spreading it (fear) around city and suburban areas. This finding in particular suggests that the concentration and clustering of crime may play a role in the generation of fear among city residents. In this case, while respondents tended to report lower levels of fear in response to burglary, its distribution and proximity did have an impact on the number of people who reported they were threatened by this type of crime. Accordingly, it could be argued that low concentrations of any crime type may be associated with a greater prevalence of fear and that this may be especially true when serious violent crime such as homicide and robbery are highly dispersed. Prior empirical research also suggests that additional information regarding the prevalence and proximity of crime comes from a variety of sources and that this information may be reliant on the broader conditions of disorder within the community (Garofalo and Laub, 1978). Survey data suggests that neighbors, coworkers and friends talk frequently about crime and are more likely to talk about serious violent crimes than more prevalent property offenses. As a result, a large proportion of city residents may become aware of serious crimes which have occurred within their vicinity (Skogan and Maxfield, 1981). Like visual signs of neighborhood disorder, these secondary sources of information may lead many residents to conclude that the community cannot manage the crime problem or that external agencies are unwilling or unable to deal with them (Hunter, 1978). These findings highlight that the fear of crime is not only a result of criminal activity in the immediate area, but also its distribution across the city more generally. This again suggests that lower concentrations of crime may be associated with elevated levels of fear for a larger proportion of city residents because a larger number of them have been exposed to crime in one way or another. Based on these findings, it is plausible that lower concentrations of crime (either spatial of aspatial) may lead to a larger proportion of a city s population who perceive crime as not wellcontained within certain neighborhoods across the city and therefore experience a heightening risk of victimization. In cities where crime is highly concentrated and/or clustered it is possible that a larger portion of the population has relatively low exposure to it, either directly or indirectly, and may therefore be unlikely to perceive it as a large problem. A handful of bad neighborhoods in a city may not signal to residents of no-crime or low-crime neighborhoods that there is anything to worry about, especially if they are clustered in a certain area of the city and spatially removed from 82

96 the areas in question. On the other hand, when crime s distribution is more diffuse and elevated rates of crime are spread across neighborhoods within the city (i.e. less concentrated or less clustered) this may signal to a larger proportion of city residents that crime poses a threat to their safety. The threat of crime, stemming from the perceived proximity of criminal events, may lead to an increase in adversary effects, which could create a situation conducive to the contagion of violence (Cork, 1999; Loftin, 1986; Sah, 1991). Accordingly, I argue that the concentration and clustering of crime will be inversely related to city crime rates. Importantly, much of the research on the contagion of crime discussed in section has focused more directly on violent crime. Additionally, it has been shown that violent crimes such as robbery and assault and homicide tend to generate higher levels of fear. Therefore, it is anticipated that a diffuse concentration of violent crime (i.e. assault, robbery and homicide) poses the biggest threat to city residents and is more likely signal to community members that they are unsafe and they must do something to prevent their own victimization. For example, the encroachment of gang activity (i.e. homicide and assault) may lead city youth to join a gang or exhibit behaviors consistent with the code of the street in order to prevent being victimized themselves. Other residents may take additional measures to assure their own safety (i.e. arming themselves) if they perceive crime knocking at their doorstep. Ultimately, as a larger proportion of city residents take action to prevent their own victimization it becomes more likely that additional acts of violence will occur. It is for these reasons that I argue in cities where violent crime (i.e. homicide, robbery and assault) is less concentrated within space (either spatially or aspatially) it may be expected that higher rates of violence will result. The implications for the concentration and clustering of non-violent property crime (i.e. burglary) are less clear. While it has been shown that a diffuse patterning of these crimes may lead to elevated levels of perceived risk within a large portion of the population (Skogan and Maxfield, 1981), it is perhaps less likely that individuals will join gangs, or adopt the code of the street in response to that threat. It is also possible that in response to the threat of burglary individuals are more likely to take measures which may inhibit rates of crime such as purchasing home or car security systems or participating in neighborhood watch programs (Rengifo and Bolton, 2012). Accordingly, I suggest it is possible that the relationship between the concentration of crime and city crime rates may be more pronounced for violent crimes than for property offenses such as burglary. In section I suggest review the literature on the contagious nature of crime. In Section I went on to discuss the relationship between the concentration and clustering of crime and the 83

97 levels of fear which are thought to contribute to the prevalence of adversary effects and retaliatory responses. Accordingly, I argue that there are compelling theoretical reasons to anticipate that where crime is less concentrated and/or less clustered within a city s boundaries higher rates of crime may result. Specifically, low levels of concentration may lead to a greater prevalence of fear which may result in an increase in adversary effects and the use of retaliation by a larger proportion of a city s residents, creating a situation which is conducive to the contagion of crime (Cork, 1999; Loftin, 1986; Sah, 1991). As a whole, these two sections suggest that the concentration and clustering of crime at the city-level will be inversely associated with city crime rates and that this relationship may be the most pronounced for crimes which are violent in nature The Concentration of Crime and Effective Policing As suggested above, it is possible that higher degrees of crime concentration and or clustering may be associated with lower levels of crime at the city-level because crime s concentration allows police and other criminal justice actors to more efficiently dedicate crime prevention resources. Drawing again from literature which has described the proliferation of crime seen during the latter half of the 1980s as an epidemic, crime may become easier to contain or combat when it is constrained to a relatively small number of places within a given city. In cities where criminal activity is prevalent in only a small number of areas (i.e. it is highly concentrated), police may be able to target those areas more effectively and allocate resources in a more efficient manner, thus translating to lower rates of crime. Additionally, when high-crime areas are clustered within space, it is likely that any intervention may lead to larger declines in crime because of diffusion in crime control benefits from the target location into surrounding areas. The current section reviews research on hot-spot policing techniques, highlighting how the concentration and clustering of crime in a given city may contribute to the effectiveness of police actions, resulting in lower rates of crime at the city-level. Police and researchers have long recognized that crime is unequally distributed across neighborhoods and other administrative areas (such as police beats) within a city (e.g. Shaw and McKay, 1942). Advancements in information technology and geographic information systems have enabled police and researchers to identify and focus more precisely on areas which generate a large proportion of crime within their jurisdiction. Hot spot policing has become an increasingly common strategy used by local police agencies across the country. Furthermore, the adoption of hot spot policing is especially pronounced in jurisdictions with populations of 500,000 or more. 84

98 According to the Law Enforcement Management and Administrative Statistics (LEMAS) survey, in % of city and county agencies serving populations between 100,000 and 500,000 used hot spot identification, while virtually all (92%-100%) police agencies in jurisdictions larger than 500,000 reported using hot spot policing techniques (Burch, 2012; Koper, 2014; Reaves, 2010). Numerous studies have demonstrated the effectiveness of police interventions focused on crime hot spots. Braga and colleagues (2012) recently reviewed the results of 19 experimental and quasi-experimental studies devoted to the impact of hot spot-oriented policing interventions. Authors found that in 80% of the studies reviewed, a variety of policing actions including targeted vehicle and foot patrols, order maintenance policing, and intensive crackdowns significantly reduced crime in the treatment areas in comparison to the control sites. Results from a review conducted by the National Academy of Sciences also suggest a significant impact of hot spot policing on levels of crime and disorder (Short et al., 2009; Weisburd and Eck, 2004). Furthermore, results suggest that rather than displacing crime to nearby areas, it is more likely that the crime reducing effects of hot spot policing extend to areas outside the targeted location. That is that in addition to the areas targeted by police, the areas in close proximity to the hot spots were likely to see decreases in crime as well. This has been come to be known as the diffusion of crime control benefits (Clarke and Weisburd, 1994; Weisburd et al., 2006; Weisburd and Telep, 2012). Importantly, it is crime s concentration which allows for the identification of hot spots. If crime were distributed equally across areas within the city, police would have to assume that all areas within the city had an equal potential of generating crime, thus requiring them to distribute their resources across all areas within a given city. In practice, of course, this is not the case. However, it is possible that the degree of concentration or clustering of crime within a given city may impact how effectively police can combat the crime problem within their jurisdiction. Specifically, a high concentration of crime within a given city may aid police in correctly identifying problem areas, as well as allowing for the efficient allocation of police resources to areas where they are likely to have the largest impact. A high concentration of crime would allow departments to identify and target a select number of areas within their jurisdiction assuring that the use of their resources are likely to lead to the largest reduction in crime. In cities where crime is less concentrated, however, police may have a more difficult time identifying specific areas which are in the greatest need of their services and therefore be less effective in the allocation of their resources as they may decide it is necessary to focus on a larger number of areas across the city. 85

99 Additionally, it is possible that the degree to which crime is spatially concentrated (i.e. clustered) may have an especially large impact the effectiveness of hot spot interventions. In cities where high-crime areas are tightly clustered, any action by police may lead to a larger decrease in crime due to the diffusion of crime control benefits discussed above. For example, an intervention, such as a major crackdown or targeted patrol in one high-crime neighborhood may have an impact on the adjacent high-crime neighborhoods, thus translating into larger reductions in crime than may have been expected if the high-crime neighborhoods were not in close proximity to one another. On the other hand, however, low levels of spatial concentration (i.e. clustering) would theoretically decrease the effectiveness of police interventions because policing activity in an isolated high-crime neighborhood is less likely to translate into large reductions in crime in the surrounding, presumably low-crime areas. In summary, I argue that the concentration of crime in a select number of places and the clustering of these high-crime places within space may impact the total volume of crime within a city through its impact on the police s ability to effectively identify those places with the greatest need. As elevated levels of concentration and clustering make it more likely that particular areas will be identified as a hot spot, and therefore subject to additional scrutiny by the police, it could be anticipated that lower rates of crime may result. It is important to acknowledge, however, that the total volume of crime which occurs in a given city may also impact police effectiveness, translating into varying degrees of crime concentration and/or clustering. For example, low rates of crime may allow police agencies to allocate their resources more widely across the city preventing the spread of crime from a select number of high-crime areas, thus yielding a high degree of concentration. On the other hand, high rates of crime within a given city may require police to choose their battles and focus more directly on criminal hotspots in order to maximize their effectiveness. Having to focus on a select number of hotspots may reduce the police presence in other areas of the city, allowing for crime to occur in these locations, resulting in a more diffuse patterning of crime overall. This suggests that the concentration of crime may be endogenous. This possibility has implications for the conclusions drawn in this dissertation which assumes that the concentration of crime is exogenous to city crime rates. Any significant effect of the concentration of crime on city crime rates may be an artifact of a relationship in the opposite direction. To mitigate these concerns, albeit only slightly, this dissertation examines the effect of crime s concentration and clustering during the years of on city crime rates in Future research should evaluate this possibility in greater detail, 86

100 using methods which are designed to address issues of endogeniety more directly (i.e. instrumental variable regression models). Although no clear instrumental variables are immediately apparent, Chapter 2 suggests a number of city characteristics (i.e. the concentration of public housing or alcohol outlets) which could serve this function. This provides an avenue which future research should explore in greater detail. To conclude, while the unequal distribution of crime across space has long been of interest to social researchers, relatively little empirical research has been devoted to systematic assessments of the tendency for a small number of places to account for a very large proportion of the total crime problem. Of the research conducted to date, both cross-sectional and longitudinal studies of the distribution of crime at small levels of aggregation have found evidence that that crime is strongly coupled to a small number of micro-areas within a given city (Brantingham and Brantingham, 1999; Groff et al., 2010; Weisburd et al., 2004; Weisburd et al., 2012). The striking concentration of crime has been referenced several times in recent aggregate-level work. Importantly, however, research has not systematically evaluated whether variation in the concentration of crime exists across cities, nor have the measures utilized in past studies accounted for the spatial proximity of these high-crime areas to one another. This dissertation contributes to aggregate-level research by examining two primary research questions. First, does the concentration and clustering of crime vary across cities? Secondly, is variation in the concentration of crime (either spatial or aspatial) associated with between-city differences in criminal activity, net of other factors know to contribute to city crime rates? As suggested in Chapter 2, there are compelling theoretical reasons to believe that variation in the concentration of crime (both spatial and aspatial) may exist once a large sample of cities is considered. The current chapter argues that this variation may also contribute to city-level variation in crime, especially for crimes which are violent in nature (i.e. homicide, robbery and assault). Before discussing the results designed to address these two research questions as well as the implications of this research for aggregate-level research, Chapter 4 elaborates on the data and methods used in this dissertation. 87

101 CHAPTER FOUR MEASURING THE CONCENTRATION AND CLUSTERING OF CRIME As discussed in the previous chapters, this dissertation is designed around two primary research questions. First, does the concentration and/or clustering of crime vary across cities? Secondly, is variation in the concentration of crime (either spatial or aspatial) associated with the between-city differences in criminal activity, net of other factors known to contribute to city crime rates? The current chapter discusses data and methods used to assess these questions. After revealing the sources of the data included in this dissertation, the measures and empirical methods employed are discussed in detail. 4.1 Data Sources To accomplish the goals stated above, this dissertation relies heavily on data from the National Neighborhood Crime Study (NNCS). The NNCS represents the most comprehensive source of neighborhood (defined as a census tract) crime data available for large sample of cities throughout the U.S. It includes central cities and large suburbs, places in all regions of the country, those with declining manufacturing bases and healthy economies, and of particular interest here, cities that vary significantly in the total volume of crime reported to the police. Assembled by Peterson and Krivo (2000), the NNCS is a compilation of data for both violent and property crimes at the census-tract level for a representative sample of 91 cities with populations larger than 100,000. The sample was designed to represent the regional distribution, population size, racial/ethnic composition, and poverty status of urban neighborhoods in the United States in the year The final data set includes the count of reported homicide (murder and non-negligent manslaughter), forcible rape, robbery, aggravated assault, burglary, larceny and motor vehicle theft for a total of 9,563 neighborhoods within the 91 cities for the years 1999, 2000, and Although for the current project it may have been preferable to focus on smaller units of aggregation such as those used in prior research to measure the concentration of crime (i.e. individual addresses, census blocks, or street segments), data available at these micro-levels are unavailable for a large sample of cities at present. Accordingly, in order to facilitate one of the first 88

102 comparative studies of the concentration of crime, it is the census tract-level crime data available from the NNCS which was used to create the city-level measures of crime concentration and clustering discussed in detail in section 4.2. These data will be used to answer the first question addressed in this dissertation (Are there important city level differences in the concentration and clustering of crime?) in Chapter 5. To answer the second research question addressed in the dissertation, the city-level measures of the concentration and clustering of crime created from the neighborhood-level crime data will represent the key independent variables in a city-level analysis of city crime rates, results of which are presented in Chapter 6. Section 4.2 elaborates on the measures of crime concentration and clustering used to accomplish the primary research goals of this dissertation. In addition to the data from the NNCS, this dissertation incorporates city-level data on a wide range of socioeconomic conditions drawn from the 2000 Census as well as city-level crime data from the FBI s Uniform Crime Reports (UCR). The 2000 Census ST3-Table includes data on social, economic and housing characteristics compiled from a sample of approximately 19 million housing units (U.S. Census Bureau, 1997). A series of measures, (i.e. percentage of families living below poverty, percentage of female headed households, percentage of non-latino black, and percentage Hispanic) were used to create a number of commonly included city-level control variables known to be associated with rates of crime at the aggregate level. These control variables are discussed in greater detail in section 4.3. Finally, data on criminal offenses known to the police at the city-level were drawn from the FBI s Uniform Crime Reports. As elaborated below, city-level homicide, robbery, assault and burglary rates computed using the count of crimes and population estimates provided by the UCR are the outcome of interest in the second part of this analysis. The remainder of this chapter details the additional measures and analytic strategies utilized. 4.2 Measuring the Concentration and Clustering of Crime As discussed in the previous chapters, the clustering of social phenomena has long intrigued researchers, luring them with the prospect of obtaining clues about the causal explanations of the patterns observed. Research devoted to the idea of concentration and/or the clustering of certain types of events or individuals has been central to the study of crime (Brantingham and Brantingham, 1993; Eck et al., 2005; Spelman, 1995; Weisburd et al., 2012), residential segregation (Ducan and Ducan, 1955; Grannis, 2002; Massey and Denton, 1988; Reardon and Firebaugh, 2002) poverty (Krivo and Peterson, 1996; Lee, 2000; Stretesky et al., 2004) and disease (Mantel, 1967; Kulldorff 89

103 and Nagarwalla, 1995; Turnbull et al., 1990; Wilson et. al., 1999). In response, a large battery of methods designed to estimate how things are distributed across space have been developed in the fields of criminology, demography and epidemiology, each with their own set of questions, data structures, and statistical procedures. The current section discusses the two measures used to represent the concentration (i.e. aspatial concentration) and clustering (i.e. spatial concentration) of crime across American cities in this dissertation The Traditional Measure of Crime s Concentration The traditional measure of the concentration of crime referenced in prior empirical research represents the proportion of micro areas which account for the majority of crime within a given jurisdiction. For example, Sherman and colleagues (1989) stated that 3 percent of the total number of addresses accounted for just over half of all the calls to police in Minneapolis, Minnesota. Similarly, Weisburd and colleagues note that between 4 and 6 percent of street segments in Seattle accounted for 50 percent of the incidents reported to the police (Weisburd et al., 2004; Weisburd et al., 2012). Finally, largely parallel measures using the proportion of micro areas which account for a large proportion of the total crime reported have been observed for the cities of Boston (Pierce, Spaar and Brigg, 1988; Braga, Papchristos and Hureau, 2010) and Tel Aviv, Israel (Weisburd and Amram, 2014). Similar to past research, the measure of crime concentration used in this dissertation represents the percent of the total number of neighborhoods (defined here as census tracts) within a given city that account for 50 percent of the total volume of crime observed at the city-level. This measure was created by first summing the tract-level counts of homicide, robbery, assault, and burglary provided by the NNCS to derive city-level totals. Within each city, the tract-level data was then placed in descending order so that running totals could be computed. Using this, the number of tracts which accounted for 50 percent of the total volume of each crime was generated. Finally, a measure which represents proportion of tracts which account for over half of the crime observed within the city as a whole was created to represent the traditional (aspatial) measure of crime concentration. While comparable to estimates of the concentration of crime referenced in past research, this measure is not without limitations. For example, this measure is unable to account for the intraneighborhood heterogeneity in crime that has been identified in past research using data on micro areas (i.e. Groff and LaVigne, 2001; Weisburd et al., 2012). Though relationships examined at different levels of aggregation often yield substantively similar results (Land et al., 1990), it is 90

104 possible that this project s reliance on tract-level data masks the heterogeneity present at smaller levels of analysis such as addresses, street segments or census blocks. Accordingly, the results obtained from future analyses which utilize these smaller levels of aggregation may differ from the results of this dissertation. However, since the primary goal of this project is to assess the concentration of crime across a large sample of cities, for which micro-level data on crime has not been assembled, this dissertation draws on the tract-level data from the NNCS in order to create the measure of concentration described above. This shortcoming highlights the need for additional data collection at lower levels of aggregation as well as the development of a data infrastructure which allows for the replication and expansion of the work done here. A crude comparison between the results of past research on the cities of Seattle, Minneapolis and Boston, and the methods used in the dissertation is presented in Chapter 5. Also important, in relation to the second question addressed in this dissertation, higher values on a measure which represents the proportion of tracts which account for 50 percent of the crime within a city actually represent a greater diffusion of criminal activity. For this reason, in the regression models presented below, this measure was transformed by subtracting the obtained value from 100 so that larger values would represent higher degrees of crime concentration. This streamlines the discussion of the results when comparing this measure to the second measure of crime s concentration used in this study, the spatial version of Information H, for which higher values also represent a greater degree of concentration/clustering. The next section discusses the second measure utilized in this study in greater detail A Measure of the Spatial Concentration of Crime Several approaches have been applied to test for the general clustering of crime in prior empirical research. One popular approach to studying the spatial distribution of certain events is designed to identify the existence of clustering more generally. Basic tests for the existence of clustering include the nearest neighbor index, and tests for spatial autocorrelation such as Moran s I or Geary s C statistic (Eck, 2005; Moran, 1950; Weisburd et al., 2012). These tests are geared toward determining whether or not the phenomena under investigation clusters across space, but are not well-designed to specify, empirically, the location of these clusters relative to one another and are most useful when the location of a cluster is not of primary interest, for example the investigation of whether or not a disease is infectious (Mantel, 1967; Kulldorff and Nararwalla, 1995). Within the crime prevention literature, testing for clustering is often the first step in identifying crime hot 91

105 spots (Sherman et al., 1989; Eck, 2005). These approaches seek to identify the areas within a city with the highest density of criminal activity in order to inform policy makers and criminal justice procedures. Most of the empirical methods used in prior research incorporate some form of a hypothesis test in which the null hypothesis is that crime is randomly distributed across space. The assertion that crime is randomly distributed is then compared to its observed distribution using classical statistics to determine whether the null hypothesis is supported (Waller and Jaquez, 1995). As mentioned above, statistical approaches used to test for the existence of clustering include tests such as Moran s I and Geary s C, which test for the presence of spatial autocorrelation (Eck, 2005; Moran, 1950; Weisburd et al., 2012). More advanced methods, designed to examine the clustering of specific events or types of places include local indicators of spatial association (LISA) statistics (Anselin, 1995; Getis and Ord, 1996). LISA statistics assess the spatial association of data by comparing local averages to global averages, identifying those local areas with the highest incidence rates (Ratcliffe and McCullagh, 1999). These methods have been used in prior research to identify the location and clustering of high-crime street segments in the city of Seattle (Weisburd et al., 2012). In other situations, researchers are interested in the location of crime clusters relative to the location of certain types of places. In this instance, researchers examine the effect that a particular type of place (e.g. liquor store, check cashing location, or bus stop) may have on crime, or alternative outcomes in the surrounding area. Examples of this are seen throughout the literature on crime hot spots, where researchers investigate the effect of a specific type of place, such as a bar or tavern (Block and Block, 1995; Roncek and Bell, 1981), public housing project (Roncek et al., 1981), or a high school (Kautt and Roncek, 2007; Roncek and Lobosco, 1983) on surrounding levels of crime. A well-known statistical test of this kind is Ripley s (1977) K function. In this form of analysis, the prevalence of crime within a given radius of a certain type of place (bus stop, alcohol outlet, or high school) is compared to the prevalence of crime in the area as a whole. Using this approach, it is possible to determine if the area surrounding a given place is associated with a higher density of criminal activity than would be expected given the rate of crime for the entire area. Although useful under a variety of contexts within the fields of criminology and epidemiology, the methods listed above are not well-suited for the current multi-city study of crime concentration. Unfortunately, while they are capable of identifying hot spots of crime, or assessing the prevalence of crime in the proximity of a given place, they do not provide a measure of 92

106 on how tightly crime is concentrated or clustered within the larger area in a way that can be systematically compared across cities. For this reason, a systematic comparison of the spatial concentration of crime using these measures of clustering is infeasible. Therefore, in order to model the spatial concentration or clustering of crime, the current project relies on a measurement technique which is most closely related to research on residential segregation. Specifically, the current project will focus on the use of the spatial version of the information theory, H index. This index provides a way to systematically evaluate the spatial concentration or clustering of crime across cities. As one of the most central and enduring dimensions of racial inequality, residential segregation has been the topic of a tremendous volume of scholarly research (Bell, 1954; Clark, 1991; 1992; Ducan and Duncan, 1955; Grannis, 2002; Massey and Denton, 1987; 1988; Reardon and Firebaugh, 2002; White, 1986). At its most basic level, segregation is the degree to which two or more groups live separately from one another in different parts of the environment (Massey and Denton, 1988). In the case of residential segregation, it is the degree to which minority members live apart from members of the majority. Although a fairly simple concept when taken at face value, this description hides many of the complexities regarding the most appropriate way to model the distribution of certain groups across space (Massey and Denton, 1988; James and Taeuber, 1985; White, 1986). As a result, several measures have been developed over the years, each involving a slightly different definition of segregation. Furthermore, advances in geographic information systems (GIS) have made it feasible to better account for the distribution of distinct groups within space, leading to more complete measurement strategies which prove valuable to the current project. More than 25 years ago, Massey and Denton (1988) expressed discontent with the disagreement seen in the literature regarding which measures of segregation are superior and lamented the lack of a systematic research agenda designed to assess this issue. Their response was to describe and evaluate measures of five conceptually distinct dimensions of residential segregation: 1) evenness, 2) exposure, 3) clustering, 4) centralization and 5) concentration. Evenness is the degree to which the proportion of minority group members in local areas approaches the proportion of the entire area under consideration; as areas diverge from equal levels, segregation increases. Exposure reflects the extent to which minority and majority groups are exposed to one another by sharing common local areas. Concentration is the relative amount of physical space occupied by minority groups; as segregation increases, minority group members are confined to smaller geographic areas. Centralization is the degree to which group members are situated in or 93

107 around the center of an urban area. Finally, clustering is the extent to which predominately minority areas are in close proximity to one another in space. This dimension is maximized when these areas are contiguous, forming one large area, and minimized when they are scattered throughout space, as the squares on a checkerboard are (Massey and Denton, 1988). A group that is highly centralized, tightly clustered, unevenly distributed and minimally exposed to the majority members is said to be highly segregated. In the 25 years since Massey and Denton s influential work, advances in GIS technology have led to significant advancements in the modeling of the distribution of groups in a spatial context. Recently Reardon and O Sullivan (2004) have suggested the distinction between evenness and clustering made by Massey and Denton (1988) is an artifact of the reliance on aspatial measures. In Massey and Denton s (1988) formulation, evenness refers to the degree to which members are over- or under-represented in local areas in comparisons to their proportion of the population in the larger area. Clustering refers to the spatial proximity of these smaller areas to one another within the larger area. Reardon and O Sullivan (2004) suggest that if a measure of segregation took into account the exact location, and proximity of groups to one another within space, the distinction between evenness and clustering would be irrelevant. As a result, they suggest an alternative to Massey and Denton s (1988) dimensions of segregation consisting of only two dimensions, spatial exposure (or isolation) and spatial evenness (or spatial clustering). These two dimensions are illustrated in Figure 2, adapted from Reardon and O Sullivan (2004). Spatial evenness is the dimension which most closely relates to the idea of clustering discussed in this dissertation, as it accounts for both the uneven distribution of criminal activity as well as the degree to which these high-crime neighborhoods cluster within space. A recently developed statistic used in prior research on residential segregation to measure spatial evenness is the spatial version of the information theory index, symbolized by H (Reardon and Firebaugh, 2002; Reardon and O Sullivan, 2004; Lee et al., 2008). This measure has recently be shown to be conceptually and mathematically superior to a second commonly used measure of evenness, the index of dissimilarity D (Reardon and Firebaugh, 2002; Reardon and O Sullivan, 2004). Additionally, spatial H has been shown by Reardon and O Sullivan (2004) to possess all of the necessary characteristics of a spatial index of segregation. Like D, spatial H taps into the evenness dimension of segregation, but does so by comparing the proximity-weighted composition of an individual s local environment with the composition of the larger area as a whole. Reardon and O Sullivan (2004) conclude that of the four spatial evenness measures considered (the entropy-based 94

108 information theory index (H), the diversity index, the relative diversity index, and White s (1983) spatial proximity index), the spatial H is the most satisfactory. Specifically, Reardon and O Sullivan (2004) found that spatial H possesses a number of the desirable properties which have been identified in past research that pertain to measures of spatial evenness (i.e. scale interpretability, arbitrary boundary independence, and composition invariance) (see Schwatz and Winship, 1980; Reardon and Firebaugh, 2002). The next several paragraphs discuss each of these criteria briefly, highlighting how the spatial H is believed to provide a better estimate of the proximity of certain groups or events to one another within space. The first criteria by which to evaluate spatial segregation indices described by Reardon and Firebaugh (2002) is scale interpretability. This is also a desirable property of any segregation index more generally. Authors suggest any spatial segregation index should be equal to zero when group proportions are the same in each local environment (indicating complete evenness) and reach a maximum (generally 1) if the local environment is composed of only one group (indicating maximum unevenness). Arbitrary boundary independence suggests that a spatial segregation measure should be independent of the boundaries present the underlying data (i.e. census tracts or block groups). They argue that a major limitation of many indices of segregation (spatial and aspatial) is that the estimates derived are a function of the defined boundaries, opening them up to the issues associated with the modifiable areal unit problem (MAUP). MAUP draws attention to the fact that conclusions drawn regarding the distribution of certain groups (i.e. segregation), may differ due to the scale or the partitioning of space in regards to the units used. In general the MAUP consists of two subproblems: the scale effect and the zoning effect (Wong, 1997; 2004). The scale effect refers to how changing the size and number of the areal units (i.e. using tracts instead of block groups) can result in different estimates of segregation. The zoning effect refers to the fact that measures of segregation rely on individual-level data (on households) which has been aggregated to form counts for specified geographic areas and therefore will be sensitive to how the boundaries of these areas are drawn. In order to mitigate the impact of the scale and zoning effects, Reardon and O Sullivan rely on measures of segregation which are calculated using what they call local environments, described in more detail below. It is believed that the use of these local environments make the estimates of segregation derived using Reardon and O Sullivan s spatial measure less prone the problems associated with MAUP. 95

109 A final criterion suggested by Reardon and O Sullivan (2004) which is especially relevant to estimating the clustering of crime is composition invariance. In general, the authors argue a measure of spatial evenness should be independent of the larger areas population composition (i.e. the proportion of black residents) and should depend only on the distribution of the groups considered within space. This assures that any comparisons made across areas (e.g. cities) are valid and not impacted by the composition of the areas considered. This is an especially important requirement considering the goals of this dissertation. If the estimate of the clustering of crime (spatial H measure of evenness) was impacted by the total volume of crime which occurred in a given city, a multicity comparison would be inapplicable. To this effect, Reardon and O Sullivan (2004) demonstrate that a commonly used measure of segregation, the diversity index, is susceptible to this issue and the estimates obtained may be impacted by the relative size of the groups being considered. However, the authors show that spatial H is not plagued by this problem and conclude that of the four spatial evenness measures considered, spatial H is the most satisfactory. Based on this list of criteria Reardon and Firebaugh (2002) and Reardon and O Sullivan (2004) demonstrate that out of the measures assessed, spatial H is the most acceptable spatial measure of the evenness dimension of spatial segregation. H is capable of accounting for the distribution of various population groups within space and is also has been shown to mitigate a number of the limitations that impact aspatial measures of segregation which are reliant on arbitrary administrative boundaries (i.e. census tracts) and do not account for their proximity to one another within space. Further, while the spatial version of the dissimilarity index possesses a number of these favorable properties, it is sensitive to the composition of the population within each area and therefore may yield misleading results if applied to the measurement of crime s concentration. For these reasons, it is believed that spatial H represents an appropriate measure to adapt for use in this dissertation in order to measure the spatial concentration of clustering of crime. H, as described in detail below, can capture the proximity of high-crime places to one another within space, providing an estimate of crime s spatial concentration or clustering. In a series of papers, Reardon and colleagues (2004; 2008) describe at length the steps to compute spatial H. Here, I summarize the methods used to create the measure of the spatial concentration of crime, which is estimated in addition to the traditional measure of concentration described above, to answer both of research questions central to this dissertation. In prior research devoted to racial residential segregation, the typical strategy uses is to create an index using the count of two or more population groups in a given subarea of a larger 96

110 region. In this dissertation, however, the two groups included in the computation of the index are defined as: (1) the count of criminal incidents (homicide, robbery, aggravated assault and burglary); and (2) the total population. In the case of burglary, the second group is defined as the total number of households within a given tract. By substituting these measures for the group counts traditionally used, the spatial H index is a measure of how different crime rates are in individual s local environments, on average, than the region R (city) as a whole. As described below, rather than relying on census tract boundaries in the computation of the segregation index, Reardon and O Sullivan use the area within a specified distance (e.g. 1000m) to define an individual s local environment. This approach assumes that each individual inhabits a local environment in which the population composition are made up of the spatially weighted average of the population densities of each group at each point in the city of interest. These local environments are conceived as overlapping egocentric environments, rather than discrete spatially bounded neighborhoods, such as census tracts (Reardon, Matthews, O Sullivan, and Lee, 2008, p. 496). A visual representation of a local environment is presented in Appendix A. These same procedures are applied in this dissertation to compute spatial H in order to represent the spatial concentration or clustering of crime within cities. Below I draw from the procedures outlined by Reardon and colleagues (2008), summarizing the steps taken to create the measure of the spatial concentration of crime which is used to answer the two research questions central to this dissertation. All calculations, including the estimation of point crime densities and the computation of spatial H were done in ArcGIS 9.3 using the SpatialSeg macro available from Reardon s project s website Converting Crime Data to a Grid of Crime Densities. The first step in generating a measure of the spatial concentration of crime, defined here as the clustering of high-crime environments, is to generate the crime density at each point in a given city. Reardon and O Sullivan (2004) developed their measure of spatial H assuming the information on population density was available for every point within a region. In practice, however, these estimates must be derived from census data (in this case tract-level crime and population data). The initial steps to creating a spatial H involve (1) superimposing a grid of 100m X 100m cells on a tract-level map of a given city, (2) using data on the count of criminal events and tract population to estimate the counts of each for every cell in each tract, and (3) smoothing the grid with Tobler s (1979) pycnophylactic ( mass preserving ) method, which softens the sharp changes in counts at the boundaries of each tract. This method estimates the counts in each cell by assigning each cell the average count of the focal 97

111 cell and its eight neighbors, while retaining the total and group-specific counts within each tract. From this grid-level data, local environments can then be defined using these smoothed population and crime counts, which have been adjusted for the makeup of nearby cells as well as a proximity weighting function which accounts for the distance between each cell in the region (Reardon et al., 2008) Accounting for the Spatial Proximity of Subareas. In order to account for the degree of clustering present in the data, a spatial index must account for the spatial proximity of each cell to one another. For that reason, it becomes necessary to include a spatial proximity weighting function within the calculation of the crime rate in each local environment. Although as originally developed, Reardon and O Sullivan s (2004) spatial segregation measures did not reference a specific measure of spatial proximity, more recent research has relied on a distance-decay function specified by White (1983) which assures that nearby locations are weighted more heavily than more distant ones in the estimation of each local environment included in the computation of the index (Reardon et al., 2008). This dissertation uses the proximity function specified by Reardon and colleagues (2008). Specifically, biweight kernel proximity functions with a radius of 500m, 1000m, or 2000m are used to compute the crime rate in each local environment included in the computation of spatial H. The biweighted proximity function is defined below where d(p, q)is the Euclidean distance between points p and q, and r is the radius of the kernel to be estimated (i.e. 1000m). Using this particular weighting scheme, the crime at nearby locations will contribute more to the estimate of the crime rate in a given local environment than will the crime in spatially detached areas. [ ( ) ] The size of the radii used in these proximity functions has been subject to a significant amount of debate within the literature (i.e. Suttles, 1972; Chaskin, 1994) and has also substantive meaning for the current project. Prior research on residential segregation which has employed spatial H has used cutoffs at 500m, 1,000m, 2000m, and 4,000m (Lee et al., 2008). The smallest of which (500m), is thought to represent a pedestrian neighborhood in which most walking activities are thought to take place and the largest (4,000m) which translates to an area of nearly 20 square miles, an area larger than many suburban municipalities and is considered to be as large (or larger than) what most residents consider a neighborhood or local community (Lee et al., 2008). Research 98

112 on the distance traveled for personal reasons such as socialization and shopping suggest that these activities typically occur within such a radius (Hu and Reuscher, 2004). The two intermediate radii generate local environments which correspond roughly with many institutional jurisdictions (i.e. a police substation patrol area or an elementary school s zone). Lee and colleagues (2008) use these radii (500m, 1000m, 2000m and 4000m) to assess the determinants of racial segregation at multiple geographic scales which they label micro-segregation and macro-segregation. They use the smaller radii to capture the heterogeneity in racial composition present within short distances and the larger distances to capture the racial composition in larger local areas contained in the metropolitan areas examined in their study. Authors illustrate that these different scales result in different conclusions regarding the extent of segregation in their sample and therefore may impact the results presented here. Specifically, Lee and colleagues (2008) found that as the size of the radius used increased, the estimate of segregation decreased in magnitude. They also found that the rank-order correlation between the measures calculated using different measures was quite high (r>.90), suggesting that from a measure of segregation calculated at one geographic scale (i.e. 500m) it is possible to infer its ranking using the larger scales (i.e. 1,000m-4,000m). For the purposes of this study, it is posited that a distance of 1000m more accurately captures the local environment in which the concentration of crime should be measured. While crime in one s immediate, pedestrian, neighborhood is almost certainly relevant to their perceptions and resulting behavior, crime in the surrounding area where people are likely to shop, work, socialize or attend school is also likely to play a role. In this way, a local environment defined using a radius of 1000m represents an area for which residents may perceive the crime problem as being nearby. Local environments defined using a radius of 1,000m also approximate the size of the census tracts included in the NNCS sample (1.21mi 2 compared to a mean of 1.0mi 2 and a median of.54 mi 2 ). Also important to the choice of radius is the fact that the crime data used in the computation of this index is reliant on the tract-level data available in the NNCS. Specifically, the census tracts which make up the 91 cities included in the current analysis vary dramatically in terms of size (i.e. land area). For example, the biggest tracts in a number of large cities (i.e. Jacksonville, FL and Houston, TX) are they themselves as large (in terms of land area) as the smallest cities included in this dissertation (i.e. Alexandria, VA and Inglewood, CA). Therefore, the use of a smaller radius (i.e. 500m) may be inappropriate as the land area included in such a radius is often smaller than census 99

113 tracts themselves, making the estimate of crime within a local environment reliant on the pycnophylactic smoothing process described above. Similarly, the use of a larger radius (i.e. 2,000m) would create a local environment which in a few atypical cases (e.g. Inglewood, CA and Alexandria, VA) makes up nearly one-half of total land area within a given city, limiting its ability to capture the true degree of clustering among high-crime areas within, as estimates would be skewed towards zero. This reality highlights the complexities associated with conducting research which is comparative in nature. I argue that the use of a radius of 1,000m most accurately represents a local environment which is theoretically meaningful, and can also be used to measure the proximity of high-crime areas to one another within space (i.e. clustering) considering the variation in city size present in the NNCS sample. However, as this study represents the first computation of spatial H for crime, it is instructive to consider plausible alternatives (e.g., 500m and 2000m) as well. In order to assess the robustness of the results presented in this dissertation, I examine measures of spatial H defined using 3 different radii (500m, 1000m, and 2000m) Calculating the Spatial H Index. Given the estimated crime and population counts in each 100-by-100m cell and the proximity function specified above, the spatial H index is computed following the formulas presented in Reardon and O Sullivan (2004). Throughout their paper, Reardon and O Sullivan (2004) use the following notation in the formulas presented. The definitions have been adapted here to reflect the outcome measure used in this dissertation, the spatial concentration or clustering of crime. In this study, and represent the total population count and crime count, respectively, in cell p, such that the sum of total count of the population and crime within each cell add up to the total in the region R. The crime rate in each local environment p is defined as: Where is the weighted proximity function described above. From this we can see that is a proximity-weighted sum of all the cell-level crime and population counts within the local environment. Following Reardon and O Sullivan s (2004) interpretation, can be thought of as the crime rate that a person living in cell p would experience in his or her local environment, where local environment is defined by the proximity function. 100

114 Using this information it is then possible to use the crime rate from each local environment ( to compute a measure of spatial evenness. Spatial H measures the variation in the spatially weighted entropy (a measure of diversity) across the region R. Entropy at each point p is computed as: ( ) ( ) In the equation above, M indicates the number of groups in the population (n=2). The derived entropy of the local environment,, is analogous to the entropy of an individual tract, which is commonly used in the aspatial segregation information theory index. Following Theil (1972), Reardon and O Sullivan (2004) define spatial H as: In this final equation, T represents the total population of the region, and E is the overall regional entropy. Effectively, spatial H represents a measure of the estimated crime rate in each local environment, defined as the weighted average of all cells within 1000m, compared to the crime rate in the city as a whole. Spatial H would be at its maximum of 1.0 (indicating maximum concentration/unevenness) only when all crimes in a given city occur in one local environment. Conversely, if the crime rate in each local area were equal to that of the city as a whole, spatial H would be equal to zero (indicating complete evenness). In practice, however, the use of the smoothing and weighting techniques described above make these maximum and minimum values unlikely to occur. As the count of crimes in each tract are distributed across the grid-cells contained within, and then local environments are created using cell-level data and the distance-decay weighting scheme described, even if all crimes were contained in a single census tract, spatial H, would be diverge from 1.0 as multiple local environments contain a non-zero estimate of crime. As demonstrated above, spatial H is capable of accounting for the proximity of high-crime areas to one another. The more highly clustered high-crime neighborhoods are within space, the higher the values on this measure will be. On the other hand, if high-crime neighborhoods are dispersed throughout the city, measures of spatial H will approach zero. In this way, increases in the 101

115 spatial H correspond with higher levels of spatial concentration or clustering as discussed in this dissertation. In order to illustrate this application of the spatial H index, Figure 3 displays tract-level robbery rates in select cities along with their computed spatial H values. As can been seen in Figure 3, higher scores on the spatial H index, computed using the counts of tract population and robberies which occurred within each tract s boundaries, correspond with an increase in the spatial clustering of crime across the city. In the cities of Norfolk, VA (H=.0251) and Houston, TX (H=.0412) (panel 3a) it can be seen that the high-crime neighborhoods, defined as those tracts which are highest in crime and account for over 50% of robbery incidents which occurred in the city, are relatively dispersed throughout the two cities. In St. Louis, MO (H=.0641) and Dallas, TX (H=.0783) (panel 3b), clustering of these high-crime tracts begins to appear in a number of areas across the city but are not centralized in a single area. Finally in Plano, TX (H=.1014) and Aurora, IL (H=.1084) (panel 3c), while the majority of the tracts in the city experienced low rates of robbery, there is a cluster of tracts which have relatively high rates of robbery in specific regions of the city. Figure 3 also illustrates the capacity of H to capture an additional measure of concentration (i.e. the spatial proximity of these places to one another) when compared to the values of the aspatial measure of concentration (i.e. the percentage of tracts which account for 50 percent of the total volume of crime within the city), described above. Specifically, the rank-ordering of the cities illustrated in Figure 3 is dependent on measure of concentration used. For example, in reference to the spatial measure of concentration, Houston (H=.0412) has the second lowest estimate of spatial concentration of the cities shown above, while in St. Louis (H=.0641) robbery is more spatially concentrated. However, the aspatial estimate of concentration suggests that crime is more concentrated in Houston where it takes 20.6 percent of the tracts to account for 50 percent of the robberies than in St. Louis where it requires 23.85% of the total tracts of tracts (remember here that higher estimates on the untransformed aspatial measure are associated with a lower of concentration of crime). This highlights the fact that the measure of spatial H is capable of accounting for the fact that crime is more highly clustered in St. Louis than in Houston, even though it takes a larger number of proportion of tracts to account for 50 percent of the total number of robberies. The relationship between these two measures will be expanded upon in Chapter 5. Figure 3 demonstrates the utility of applying a spatial version of the H index to the study of crime s spatial concentration or clustering. It also illustrates that accounting for the spatial proximity of high-crime 102

116 areas to one another may yield additional information about the concentration of crime that is unaccounted for by the more typical aspatial measure. In the examination of Figure 3, the relatively low values on the measures of spatial H become apparent. This is presumably a result of two things which bear highlighting. First, although crime rates tend to vary significantly across neighborhoods within cities, the vast majority of census tracts contain some crime. This suggests that crime as a whole is not nearly as segregated as other things, such as racial groups, which would lead to lower estimates of spatial H. Secondly, the small values for H are also a function of the reliance on tract-level crime data and the use of local environments in the calculation of the spatial index. Due to the mass preserving smoothing procedures utilized, the building blocks of the local environments (defined here as the 100m X 100m cells which fall within a given a given radius) are assigned an estimated crime and population count which have been adjusted for the makeup of nearby cells as well as the proximity weighting function described in (Reardon et al., 2008). While this smoothing method maintains the count of crimes and resident population within each tract, effectively distributing a tract s count across all cells within that tract making each different from zero (if a crime occurred within that tract). So while mathematically the range of a given the entropy index is 0 to 1 (with 1 indicating complete unevenness if all crimes occurred within a single local environment), large values are highly improbable given that the count of crime within a single tract are distributed across the various cells which make up that tract. This interpolation results in the observed low absolute values on the spatial H measure. The use of tract-level data presumably pushes the H towards zero as it overestimates the evenness of the underlying data because the exact location of the crime is unknown. This highlights the limitations of the tract-level data utilized in this dissertation. The use of point-level crime data would arguably provide a more accurate estimate of H, however, as stated before this type of data is unavailable for a large number of cities at the current time. Future research should examine the impact of these limitations for the results presented in this dissertation. Figure 3 demonstrates the utility of applying a spatial version of the spatial H index to the study of crime s spatial concentration or clustering. This measure of crime s spatial concentration is used in conjunction with the aspatial measure of crime concentration defined earlier. These two measures, the inverse of the proportion of tracts within a given city which account for over half of the total crimes observed at the city level and the spatial H index, referred to respectively as the concentration and the clustering of crime, are used to answer the two research questions central to this project. To answer the first question (Does the concentration and clustering of crime vary 103

117 across cities?), variation in the aspatial and spatial measures of crime concentration is assessed in Chapter 5. Before presenting results which address this question, section 4.3 discusses the multivariate regression models used to answer the second research question posed in this dissertation (Is variation in the concentration and clustering of crime associated with the betweencity differences in criminal activity, net of other factors known to contribute to city crime rates?), results of which are presented in Chapter Isolating the Impact of Crime s Concentration In the second portion of the analysis presented, the measures of crime s concentration and clustering become the primary explanatory variables in a series of regression models designed to assess city-level variation in crime rates. The outcome measures used to answer the second question posed by this research are drawn from the FBI s Uniform Crime Report and represent the betweencity differences in criminal activity. Using the number of homicides, robberies, aggravated assaults and burglaries reported to the police in each jurisdiction, crime rates per 100,000 city inhabitants for the year 2002 were constructed using the population estimates provided by the FBI. Stemming from the fact that official crime data are restricted to offenses known to the police, the limitations of UCR data have been highlighted in past research (Gove, Hughes, and Geerken, 1985; Biderman and Lynch, 1991). Specifically, comparisons between official crime data and crime estimates from victimization surveys suggest that a substantial portion of certain crimes are not reported to the police. Nevertheless, unless there is reason to suspect that the percentage of crime reported to police varies significantly from city-to-city, the between-city variation in crimes reported to police represents an appropriate outcome measure for this study. Although research on this issue is extremely limited, prior research conducted at the neighborhood level found that rates of socioeconomic disadvantage do not significantly affect the likelihood of police notification among robbery and aggravated assault victims, suggesting that for serious crimes, persons from cities characterized by varying structural conditions are no more or less likely to notify the police when they are victimized (Baumer, 2002). Despite known limitations, UCR data is commonly used in aggregate analyses of crime, as it represents a standardized and easily accessible source of data available for a large number of geographies. A second limitation of the analyses presented here, which are reliant on the crime data available from the NNCS, is the inability to consider the concentration and clustering of specific sub-types of homicide. For example, there are a number of theoretical reasons to believe that 104

118 certain sub-types of homicide (i.e. gang homicides) better parallel the processes of retaliation and contagion outlined in the third chapter. There are also theoretical reasons, as well as prior empirical evidence, which suggest that the distribution of intimate partner homicides may be distinct from homicides which are gang or drug related (Dugan, Nagin and Rosenfeld, 1999; 2003). Unfortunately, however, the data available from the NNCS represents the total number of homicides within a given tract and are not disaggregated by sub-type and therefore, this dissertation is unable to assess these differences. This is something that future research on the concentration of crime should consider more carefully as it may impact the conclusions drawn in this dissertation. Although all requisite control measures were available for the full sample of cities considered, with the exception of police force size which was unavailable for one city, the tract-level crime data included in the NNCS was not available for all crime types for the full sample of cities. For example, the cities of San Antonio and Philadelphia did not provide tract-level data on homicide. Additionally, 2 cities had zero homicides during the three-year study period, making it impossible to assess the degree of crime concentration within. Finally, a number of cities were removed from the final samples as they were identified as outliers on either the dependent of independent variables of interest. Therefore, the number of cities included in the regression models presented in Chapter 6 varies slightly depending on the crime type considered. The analyses presented include a maximum of 85 cities for robbery and burglary, to 81 for homicide and 73 for aggravated assault. A final list of the cities included in the multivariate analyses discussed in Chapter 6 appears in Appendix B. In order to estimate the influence of the concentration and clustering of crime on city crime rates the analysis presented includes a series of city-level ordinary least squares (OLS) regression models. Since the specification used to predict aggregate crime rates is complex and generally includes a large number of covariates, it is possible that any or all of them may contribute to heteroskedasticity in the error term. Indeed, in a number of the models shown below, White s tests indicate evidence of significant heteroskedasticity. Under conditions of heteroskedasticity, OLS estimates remain unbiased, yet become inefficient, which could possibly produce misleading results. For that reason all results presented in Chapter 6 incorporate robust standard errors. As mentioned in Chapter 3, there is also the potential for the concentration of crime to be endogenous to city crime rates. To mitigate the concerns that any effect observed is due to reverse causality, 2002 city crime rates are regressed on measures of concentration and clustering created from neighborhood crime incidents which occurred during This alone, however, does 105

119 not assure that any effects observed are not subject to scrutiny. Future research should consider using more advanced methods, such as instrumental variable regression models, which are better suited for assessing the potential for endogenous relationship to exist. In order to isolate the impact of the concentration and clustering on city-crime rates, I control for a variety of measures found in prior research to be related to between-city difference in crime (Baumer et al., 1998; Land et al., 1990; Liska and Bellair, 1995; Kubrin et al., 2010; McCall, Land and Parker, 2010). As reviewed in Chapter 3, prior research has shown that that crime rates tend to be higher in cities with higher levels of resource deprivation and family instability, larger populations, larger proportions of Black residents and a larger number of young persons. Accordingly, in addition to the independent variables central to this dissertation (the concentration and clustering of crime), several city-level measures are included as controls in the regression models presented. Prior to the construction of the indices described below, a principal components analysis was conducted to identify those measures which are most closely related to one another and are therefore believed to represent a single construct (i.e. resource deprivation). Consistent with prior research, the information garnered from the principal component analysis was used to create a number of indices thereby mitigating the problems associated with collinearity. Table 4.1 presents the summary statistics for the city-level measures included in the current study for the full sample of cities considered in the analyses presented below (n=85). This sample does not include information on the 5 cities which were identified as outliers, described in greater detail in Chapter 5. It is these measures that were used to create the indices which are included in the multivariate regression models presented in Chapter 6, described below. As can be seen in Table 4.1, the measures of city population and population change between 1990 and 2000 are highly skewed. This holds true for each crime-specific subsample of cities analyzed, shown in the first table of Appendix C (C.1). Therefore, consistent with prior research, all calculations presented in the chapters that follow include the natural log of these measures. In the case of population change, due to negative values, a constant of 1.0 was added before the log transformation was completed. The first index created using the measures shown in Table 1, resource deprivation, is an index composed of the average summed z-scores for six variables: percent of the population which is non- Latino black, the percentage of families living in poverty, the percentage of the working age (16-64) population who is unemployed, the Gini index of income inequality, median family income (reverse coded), the percentage of female headed households. The inter-item correlation between these 8 measures for each sample considered is discussed in the crime-specific results sections presented in 106

120 Chapter 6. Versions of this index have been used to measure disadvantage in several prior empirical studies (i.e. Land et al., 1990) and consistently has been shown to be associated with rates of crime at the aggregate level. As mentioned above, the results of a principal components analysis indicated that these measures tend to vary together along with the other measures included in the index and represent a common factor (i.e. levels of disadvantage). The resource deprivation index as operationalized here differs slightly from that which Krivo and Peterson (1996) and others have constructed in prior city-or multi-level analyses using data from the NNCS (Krivo, Peterson, and Kuhl, 2009; Peterson and Krivo, 2010; Ramey, 2013). Importantly, however, the two indices of disadvantage are highly and significantly correlated with each other for the sample of cities used in this dissertation (r=.964; p<.01). Conclusions drawn from subsequent models including this alternate specification, not presented here, do not differ substantively from those included in the Chapter 6. To capture the size of the immigrant population across cities, an additional index, labeled immigrant concentration was created by combining two measures: the percentage of the population who is Latino and the percent of the city population who is foreign born. As shown in Table 4.1, the percentage of the population who are foreign born is modertately-to-highly skewed. A visual examination of this measure revealed that this was due to a small number of cities with a large foreign born population (Miami (59.8%) and Hialeah (72%)). So that the distribution of this measure resembles that of a normal curve more closely, the natural log of this measure was taken before the computation of the index. Again, Chronbach s Alpha for each crime-specific subsample is presented in Chapter 6. As results of past research suggest that crime rates tend to be higher in cities with larger populations I control for population size using a measure of the logged city population. I also include a measure of population density, operationalized as the number of residents per square mile. Contrary to past research, the measures of city population and population density were seen to load on separate factors and therefore, were each included in the multivariate regression models. In addition to the measures of population size and population density, I include a measure of the change in the population between 1990 and 2000 (logged to correct for skew) to capture the expansion of, or retreat from, each city included in the sample. Before taking the natural log, a constant of 1.0 was added so that this measure would represent the proportion of a city s population in 2000 as compared to its population in 1990 (e.g (-.13)=.87 of the 1990 population). 107

121 Additional standard control variables include the percentage of the adult population (15 and older) who are divorced, the percentage of the population which are in the crime-prone age group and dummy variable denoting southern location (South). In subsequent, more fully-specified models, the decision was made to also include a measure of residential segregation as it has been shown to contribute to crime rates at the aggregate-level and may also be related to the concentration of crime (Danziger and Gottschalk, 1993; Logan and Messner, 1987; Shihadeh and Flynn, 1997). Finally, as the size of a city s police force may also be related to rates of crime and crime s concentration and/or clustering, a measure of the number of sworn police officers per 100,000 city residents is included. Consistent with prior aggregate-level, several of the control variables are highly correlated with one another, raising the possibility of problems associated with multicollinearity. However, tests of variance inflation and sensitivity suggest that multicollinearity is not a major issue within the regression models specified, with all variables having a variance inflation factor of less than 10 (O Brien, 2007). Chapter 5 opens with a description of the concentration and clustering of crime designed to address the first research question put forward by this dissertation. Does the concentration of crime vary across American cities? Following this, the results of the multivariate regression models just described are presented in Chapter 6. Lastly, this dissertation closes with a discussion of the results and acknowledging the limitations of the current study, setting up an agenda for future research on the concentration of crime. 108

122 Figure 2: Reardon and O Sullivan s (2004) Dimensions of Spatial Segregation Figure 3: Varying Degrees of Crime Clustering Across U.S. Cities 109

123 Table 4.1: Summary Statistics for City Characteristics used in Analysis of City-Crime Rates; Max Sample (n=86) Source and Description n Mean Median Min Max SD Skewness Kurtosis Independent Variables City Population Total City Population; 2000 Census , , ,414 3,694, , Population Density Population per square Mile, Thousands; 2000 Census Population Change % Population Change between ; 1990/2000 Census Median Family Income Median Family Income in Thousands); 2000 Census % Percent Family Poverty % Of Families Living Below the Poverty Line; 2000 Census % Non-Black Population % of City Population Non-Latino Black; 2000 Census Gini Index of Inequality Gini Index of Inequality; 2000 Census % Unemployment % of Population aged in Labor Market Who are Unemployed; 2000 Census % Female-Headed Households % Female Headed Households; 2000 Census % Latino Population % of Population Latino; 2000 Census % Foreign Born % of Population Foreign Born; 2000 Census % % of Population Aged % Divorce % of Population 15+ Who are Divorced; 2000 Census Segregation White/Black Dissimilarity Index of Segregation; 2000 Census Police Force Size Number of Sworn Police Officers per 100K Population; LEOKA South Southern Region Indicator

124 CHAPTER FIVE VARIATION IN THE CONCENTRATION AND CLUSTERING OF CRIME Is there variation in the concentration and clustering of crime across cities in the U.S.? The current chapter discusses the distribution of the measures used in this study to capture the degree of concentration (i.e. the percentage of tracts within a city that account for 50 percent of the total volume of crime) and clustering (i.e. spatial H) of crime in a sample of relatively large U.S. cities. The chapter begins with a summary of the descriptive statistics for the key variables utilized in the dissertation. After this, the bivariate relationship between the measure of concentration and the spatial H measure of crime s clustering is discussed, both overall across crime types. Following the presentation of these results, Chapter 6 examines results of a series for multivariate regression models aimed at answering the second research question posed in this dissertation: Is variation in the concentration of crime (either spatial or aspatial) associated with the between-city differences in criminal activity, net of other factors known to contribute to city crime rates? This dissertation closes with a discussion of the findings reported in Chapters 5 and 6, along with the limitations of the current study as well as the implications for future research on the concentration of crime and aggregate-level crime research. 5.1 A Description of Crime s Concentration in Cities across America Before turning to the descriptive statistics designed to address the first research question posed in this dissertation, it is important to note again that the study considers two conceptually distinct measures: aspatial concentration, and spatial concentration, or clustering. The former is measured by the percentage of tracts within a city that account for 50 percent of the total volume of crime, and is referenced simply as crime concentration in the foregoing discussion and tables presented. The latter is measured with the spatial H index, and is referenced in the tables as crime clustering. I also use the term spatial concentration in the discussion to refer to this measure. The descriptive statistics for the key measures included in this dissertation derived from full sample of available cities included in the NNCS are included in Table 5.1. As mentioned in Chapter 4 the sample of cities considered does vary by crime type due to the differential availability of tract- 111

125 level crime data from the NNCS. For example, the measures of the concentration and clustering of homicide are available for a total of 87 out of 91 cities included in the NNCS because 2 cities (Philadelphia and San Antonio) failed to report the tract-level data necessary to compute these measures. Two additional cities (Bellevue and Sterling Heights) had a total of zero homicides during the 3-year study period, making impossible to compute the spatial and aspatial measures of concentration. Similarly, a total of 12 cities did not include tract-level assault data, resulting in a total sample of 78 cities valid listwise. In order to maximize the generalizability of the current work, the decision was made to retain the maximum number of cities available for each crime type. Accordingly, the descriptive statistics presented in Table 5.1 are based on samples ranging from 79 cities (for assault) to 91 cities (for burglary and robbery). Importantly, however, the decision to use the maximum sample available has implications for the conclusions drawn across crime types. For that reason, results using the sample cities for which all measures of crimes concentration (i.e. the percentage of tracts within a city that account for 50 percent of the total volume of crime) and clustering (i.e. spatial H) were available for all crime types are also discussed in the text. The question to be answered in this chapter is whether or not the concentration and clustering of crime varies across cities in the U.S. To that end, Table 5.1 presents descriptives for the traditional measure of the concentration of crime, defined here as the percentage of census tracts within a given city which account for over 50 percent of the total volume of crime. As mentioned in the previous chapter this measure will be reverse coded in the analyses that follow so it is consistent with the direction of the measure of spatial concentration (spatial H). Similar to prior research on the concentration of crime, however, Table 5.1 displays the percentage of tracts which account for 50 percent of the total volume of crime and has not been recoded. Accordingly, as currently displayed higher values on this measure denote lower levels of concentration. In the latter tables which assess the relationship between the measures of concentration and clustering this measure will be transformed so that the two are consistent in polarity. So what do the descriptive statistics presented in Table 5.1 tell us about the variation in the aspatial concentration of crime across the cities included in this dissertation? First, there is evidence of variation between cities. The cities included in the current sample exhibit variation on the first of the key variables examined in this dissertation. For instance, the percentage of tracts needed to account for 50 percent of the total number of homicides varies from 2.86 percent in Fort Collins, Colorado to percent in Inglewood, California. The mean number of tracts which accounted for half of the total number of homicides was across all 87 cities considered. In regards to 112

126 robbery, on average 21.1 percent of the tracts within a city were required to account for half of all robberies. The percentage of tracts ranged from a minimum of percent of the tracts in Bellevue, WA to a maximum of percent in McAllen, Texas. The percentage of tracts needed to account for half of the assaults ranged from to with a mean of Similarly, on average, it required percent of the tracts within the 91 cities to account for 50 percent of the total number of burglaries, but this measure ranged between and Secondly, homicide tends to be more highly concentrated than the other crime types considered, which may be anticipated given its relative rarity. Similarly, burglary tends to be less concentrated than other forms of crime, taking an average of percent of tracts within a city to account for half of the total number of burglaries within the city. Finally, assault and robbery tend to be similarly concentrated. As noted above, these comparisons may be an artifact of different sample sizes rather than true differences in distribution. Panel B of Table 5.1, however, suggests that these patterns hold when a uniform sample is considered. In the sample of 78 cities for which the measure of crime s concentration is available for each crime type, the patterns described remain accurate. Additionally, the descriptive statistics presented in Table 5.1, along with the histograms shown in Figure 4 suggest that the aspatial concentration of crime approximates a normal distribution across the cities considered. None of the values measuring the skew of the distribution approach the common cutoff of 2, suggesting that they are not skewed and therefore are fairly symmetric. Nor do the aspatial measures of concentration appear to be highly platykurtic (i.e. extreme kurtosis values). All the calculations presented here were done in Stata, which uses the definition of kurtosis found in Bock (1975), which identifies a normal distribution with a kurtosis of 3. Overall, the summary statistics shown in Table 5.1 suggest the aspatial measures of the concentration of crime appear to approximate a normal distribution. Overall, there is evidence that the concentration of crime, as defined by the percentage of tracts which account for 50 percent of the total volume of crime does vary considerably from cityto-city. This suggests that the claims made in prior research about there being relatively little variation in the concentration of crime are inaccurate, at least as measured with census tract crime data used here. These findings underscore the value of a comparative, multi-city approach to studying the concentration of crime. As mentioned in Chapter 4, this dissertation s reliance on tractlevel data from the NNCS may obscure a great deal of heterogeneity present at more micro levels of aggregation. It is possible that the use of data on smaller geographic units (i.e. census blocks or 113

127 street segments) for a large sample of cities would yield different conclusions regarding the city-level variability in the concentration of crime. However, the results of quick comparison between the results of prior research and the estimates of crime concentration obtained in the current study, suggests that the conclusions regarding the concentration of crime across the handful of cities studied to date may not be impacted by the use of census tract data. Specifically, the measure of concentration used in the dissertation (i.e. the percentage of tracts which account for 50 percent of the total volume of crime) indicates that consistent with prior research, the cities of Seattle, Boston and Minneapolis have similar levels of crime concentration. For example, the concentration of homicide, as measured by the percentage of tracts which account for the total number of homicides, is comparable between the cities of Seattle (12.6%), Minneapolis (13.1%) and Boston (11.8%). In regards to the concentration of all crime (i.e. the sum of homicide, robbery, assault and burglary), the percentage of tracts needed to account for 50 percent of the total number of crimes, is also very similar for the cities of Seattle (25.73%) and Boston (26.31%). Unfortunately, the overall concentration of crime was unavailable for Minneapolis as there was no information on the tract-level counts of assault provided in the NNCS. Although the estimates obtained here differ in absolute terms from those reported in prior research (i.e. 26% of tracts compared to 6% of street segments), the conclusions drawn regarding similar levels of concentration across these three cities, remain unchanged. This suggests that the variation in the concentration observed in the current study may hold up to analyses at different levels of aggregation. As suggested in the conclusion, however, the exploration of the concentration of crime in a comparative (i.e. multicity) context, at more micro-levels of analysis remains a promising avenue for future research to consider. Table 5.2 examines the distribution of the spatial concentration or clustering of crime across the cities considered, using a number of different radii in the calculation of spatial H. As mentioned in Chapter 4, one of the radii typically used in research on racial segregation is 1,000m, which is also a theoretically relevant spatial criterion to apply to the distribution of crime. However, because this study represents the first computation of spatial H for crime, it is instructive to consider plausible alternatives (e.g., 500m and 2000m) as well. Overall, patterns similar to that of the aspatial measure described above emerge. Specifically, the observed values of H vary both between the cities and crime types considered. Looking specifically at the measure which uses a radius of 500m, on average homicide tends to be the most highly spatially concentrated (µ =.120), followed by robbery (µ=.076), assault (µ=.065) and burglary (µ=.046), which is consistent with the aspatial measures 114

128 described above. The range in the spatial concentration of homicide (.243) is larger than that of the other three crime types. Additionally, these differences between crime types are consistent across the various radii explored, which is best demonstrated in Panel B where a uniform sample is applied. The comparison of the measures utilizing different radii suggests that the use of a larger radius (i.e. 1,000m) pushes the estimate of spatial concentration towards zero; across all crime types considered, the use of a larger radius reduces the estimate of spatial concentration. For example, the mean of for robbery drops from.076 to.069 when going from a radius of 500m to 1,000 and further drops to.050 using a radius of 2,000m. Smaller estimates of spatial H using larger radii are consistent with findings from prior research on racial segregation (Lee et al., 2008). This is due to the fact that as the radius increases, a more heterogeneous area (i.e. areas with different levels of crime) is included in the local environments which are used in calculation of spatial H, resulting in lower estimates of unevenness. As mentioned in Chapter 4, the relatively low absolute values on the measures of spatial H are a function of the reliance on tract-level crime data and the use of local environments in the calculation of the spatial H index. Due to the mass preserving smoothing procedures utilized, the building blocks of the local environments (defined here as the 100m X 100m cells which fall within a given a given radius) are assigned an estimated crime and population count which have been adjusted for the makeup of nearby cells as described in prior research (Reardon et al., 2008). While this smoothing method maintains the count of crimes and resident population within each tract, it effectively distributes a tract s count across all cells within that tract making each different from zero. So while mathematically the range of a given the entropy index is 0 to 1 (with 1 indicating complete unevenness), high values are improbable given that the count of crime within a single tract are distributed across the multiple cells which make up that tract. This interpolation results in the absolute low values on the spatial H measure. This again highlights how it would be preferable to use point-level data to estimate city-specific spatial H values. The use of tract-level data presumably skews the spatial H measure towards zero as it overestimates the evenness of the underlying data because the exact location of the crime is unknown. Future research should examine the impact of these limitations for the results presented in this dissertation. Are the spatial measures of crime s concentration normally distributed? In the case of homicide and burglary, the spatial H measure of clustering exhibits some evidence of being skewed. Figures 5-8 examine the distribution of spatial H for crime across cities in greater detail. While the distribution of robbery (Figure 5) and assault (Figure 6) appear to resemble that of a normal 115

129 distribution, Figure 7 (homicide) and Figure 8 (burglary) draw attention to the existence of a handful of somewhat extreme values. A close examination of the values on these measures revealed that a few cities (Carrollton, Livonia, Naperville, Plano, Simi Valley) have much larger values on the various measures of clustering. This is likely due to the relatively small number of crimes which occurred within each of these cities. For example, all 5 of the cities mentioned had fewer than 5 homicides during the three-year study period. The count of burglaries within each city is equally as small in comparison to the number of burglaries in other cities. Further, these cities are retained in the sample with valid measures on all crime types (i.e. the listwise sample) (see Panel B), resulting in similar patterns to that shown in Panel A. To minimize the potential distortions that these outliers may have on inferences drawn in subsequent analyses, these cities were removed from the analyses that follow. Table 5.3 presents the descriptive statistics for the concentration and clustering of crime for the sample of cities that remain, excluding the five outliers just noted. The resulting sample consists of 82 cities with measures of the concentration and clustering of homicide, 86 cities with data on robbery and burglary and 74 with data on assault. The results presented in the remainder of this chapter are based on these samples. Table 5.3 suggests that the removal of the outliers mentioned above resulted in measures of concentration and clustering which approximate a normal distribution for all of the crime types except burglary. The measures of skew and kurtosis for homicide, robbery and assault presented in Table 5.3 fall within the commonly accepted range, however, burglary remains positively skewed and highly platikurtic. These results hold for the sample of cities for which all measures of concentration and clustering are available (n=73), shown in Appendix C, Table 2 (C.2). The high degree of kurtosis observed for burglary suggests that a couple of large values remain which were not apparent in a visual inspection of the data. As the cases on the tails of the distribution do not appear to be clear outliers and in order to retain the modest sample size, no additional cases were excluded. However, in order to approximate a more normal distribution, the natural log of the measures of the clustering of burglary using the 3 different radii was computed. As illuminated in the next chapter, results of this transformation yield a distribution for the logged spatial concentration of burglary that more closely resembles that of a normal distribution. Overall, the results presented thus far suggest that the concentration and clustering of crime, as measured here, do vary considerably across the sample of cities considered. Furthermore, once aberrant values which were a product of extremely small crime counts were removed, the 116

130 distribution of the measures of concentration and clustering resemble that of a normal curve, with the exception of burglary which remained positively skewed and required transformation. Although both of the measures concentration are limited in a number of ways, mostly due to their reliance on the tract-level crime data from the NNCS, the results presented here provide reason to believe that the concentration of crime may not be as geographically invariant as suggested in prior research. Before elaborating on the implications that stem from these findings in Chapter 7, the relationship between the measures of concentration and clustering across crime types is explored. 5.2 The Association between the Concentration and Clustering of Crime Table 5.4 examines the relationship between the concentration of crime and the measures of clustering which utilize various radii in the calculation of spatial H. Importantly, prior to examining the associations between these measures, the inverse of the traditional concentration measure was computed so that higher scores would represent a greater degree of concentration. This was done by taking 100 minus the percentage of tracts which account for half of the crime present at the citylevel. Additionally for reasons outlined above, a log transformation was applied to the spatial concentration of burglary. The bivariate correlations displayed in Table 5.4 indicate a significant and moderate-to-strong linear relationship between the aspatial and spatial measures of crime concentration. The correlations between the spatial and aspatial measures are the strongest for homicide ( ; p<.01) and the weakest for burglary ( ; p<.01). The correlations between the measures of concentration and clustering are similar in magnitude for the sample of 73 cities for which all measures of concentration and clustering are available (results not shown in tabular form). More specifically, the relationship between the spatial and aspatial measures of homicide increases to ( ; p<.01), while the bivariate correlation for robbery reduces slightly to ( ; p<.01) in this smaller sample of cities. The relationship between the concentration and clustering of assault and burglary remain relatively unchanged. Additionally, the relationships described are not reliant on the definition of high-crime tracts (i.e. the percentage of tracts which account for 50 percent of the total volume of crime). As shown in Appendix C, Table 3 (C.3), the bivariate relationships between the aspatial measures of concentration calculated using different cutoffs (i.e. 40, 60, or 80 percent of the total volume of crime) suggest a remarkably strong relationship between the measures based on different definitions for all of the crime types considered (r>.828; p<.01). Given the strong and statistically significant association between the aspatial measures of crime s concentration using alternative definitions 117

131 suggests that the relationships observed above, are unlikely to be sensitive to the definition of the aspatial measure employed. An examination of the bivariate correlations between the measures of spatial concentration calculated using various radii suggests a strong significant relationship between the different definitions used. The correlations between the measures calculated using a radius of 500m and those using a radius of 1,000 are all above.950 (p<.01). The association between the measures using a radii of 1,000m and 2,000m remain above.800 (p<.01). Additionally, while the relationship between the spatial measures with proximate radii are quite strong, the observed correlations decrease as the gap in the size of radii increases, yet all relationships remain strong and statistically significant (p<.01). This remains true of the bivariate correlations in the sample restricted to cities with data on the concentration and clustering for all four crime types considered (results not shown in tabular form). As discussed in Chapter 4, the size of the radii used in the creation of these spatial measures of segregation has been subject to a significant amount of debate within the literature (i.e. Suttles, 1972; Chaskin, 1994) and has also substantive meaning for the current project. The smallest radii assessed in this dissertation (500m), is thought to represent a pedestrian neighborhood in which most walking activities are thought to take place (Lee et al., 2008). The largest (2,000m) translates to an area of nearly 5 square miles, an area one-half the size of a handful of cities included in the current sample, thus capturing a large amount of the crime within a city. This has a couple of implications for the current project. First, as a local environment defined using a radius of 2,000 meters is capturing a relatively large amount of the total land area (especially within small cities) it will bias the estimate of H towards zero as the crime rate in a given local environment will be closer to that of the city as a whole. Secondly, it will obscure any degree of clustering which is present within that area. Accordingly, for the purposes of this study, it is posited that a distance of 1,000m best captures the local environment in which the concentration of crime should be measured. While crime in one s immediate, pedestrian, neighborhood (i.e. 500m) is almost certainly relevant to their perceptions and resulting behavior, crime in the surrounding area where people are likely to shop, work, socialize or attend school is also likely to play a role. In this way, a local environment defined using a radius of 1,000m represents an area for which residents may perceive the crime problem as being nearby. For the sake of simplicity, the remainder to the results presented in the current chapter use one measure of crime s spatial concentration, that is spatial H calculated using a radius of 1,000m meters. In order to assess the robustness of the results presented in Chapter 6, however, 118

132 the results of a number of multivariate regression models which include measures of crime clustering based on the three radii discussed thus far are examined. Table 5.4 suggests that the spatial and aspatial measures of crime s concentration are highly correlated, but that they nonetheless may be capturing unique dimensions of concentration. To provide some context to this discussion, Table 5.5 displays the cities with the highest degrees of aspatial concentration of robbery (the measure that parallels the indicator often referenced in prior research) along with the corresponding rank on the measure of spatial concentration or clustering (i.e. spatial H) assessed using a radius of 1,000m. As seen in Table 5.5, while there is some correspondence in terms of rank-order (i.e. a few cities appear in the top 10 for both measures), the rank-ordering is dependent on the measures used. Rankings for the crimes of homicide, assault and burglary highlight similar patterns (results shown in Appendix D, Table 3 (D.3)). As shown in Table 5.5 and Table D.3, cities such as, Glendale and Madison tend to have high concentrations of crime, but the high-crime areas within these cities tend to be less clustered than in many other places. For example, Glendale has the eight highest concentration of assault, yet its ranking using the spatial H measure of clustering is 39 st. Similarly, Madison comes in third in terms of the concentration of burglary, yet is 39 th in clustering. The latter highlights the weaker bivariate relationship between the aspatial and spatial measures of burglary s concentration seen in Table 5.4. Figures 9 and 10 illustrate these differences in the concentration and clustering of robbery for two cities shown in Table 5.5: Chandler, Arizona and Madison Wisconsin. In both cities the high-crime tracts, those which account for 50 percent of total city crime (e.g. the traditional measure of crime concentration), are shown in dark red. Figure 9 illustrates the high degree of aspatial concentration which is present in both Chandler and Madison, as there are a relatively small number of tracts highlighted. However, these high-crime tracts are spread throughout the city rather than being highly clustered, resulting in a relatively low ranking on the spatial concentration measure. In contrast, Figure 10 displays two cities (Bellevue and Anchorage) in which robbery is not only highly concentrated, but where these high-robbery tracts (those which account for 50% of the total volume of robbery) are also tightly clustered within the city, resulting in a higher ranking in regard to spatial concentration. Overall, the results presented in section 5.2 suggest that the measures the concentration and clustering of crime used in this dissertation are closely related to one another but that the measure of clustering (H) taps into a slightly dimension of concentration, the spatial proximity of high-crime 119

133 places to one another within space. Although there is a high-level of agreement between the two measures considered, the results presented here suggest that accounting for the spatial nature of the underlying data offers additional insights about the nature of how crime is distributed within cities. These differences may impact the results of the multivariate analyses presented in Chapter 6, which examines the impact of the concentration and clustering of crime on city crime rates. As suggested in Chapter 2, there are a number of factors which may give rise to varying degrees of in the concentration and/or clustering of crime. Some factors may impact both crime s concentration and clustering, while others may impact its clustering more directly. Although this dissertation does not evaluate what factors have led to the observed variation in the concentration of crime in a multivariate context, the bivariate relationship between several city structural characteristics and the measures of crime concentration are presented in the beginning of Chapter 6. Future research that examined the factors that account for city-variation in the concentration of crime would be highly valuable. 5.3 Variability in the Concentration of Crime across Crime Types Table 5.6 presents the bivariate correlations for the two key measures of concentration developed in the dissertation, across the four types of crime considered. The bivariate correlations shown in Panel A of Table 5.6 indicate that the concentrations of various types of crime are significantly and weakly-to-moderately associated with one another. The concentration of homicide is most closely associated with that of robbery (r=.478; p<.01), followed by assault (r=.407; p<.01) and burglary (r=.276; p<.01). The concentration of assault is also significantly and strongly associated with the concentration of robbery (r=.816; p<.01) as is the concentration of assault with the concentration of burglary (r=.745; p<.01). It is important to point out that these city-level measures of concentration have uncertain implications for the association of these crime types at the tract level (i.e. high-robbery tracts may not also be high in assault). However, these results suggest that in cities where one form of crime is highly-concentrated, other forms of crime also tend to be. The majority of these relationships are highly similar in the sample of 73 cities for which all measures on concentration are available, however, the correlation between the concentration of homicide and the concentration of burglary is not statistically significant in the smaller sample (.220; p>.05) (results not shown in tabular form). Panel B of Table 5.6 indicates that a similar set of relationships exists in regards to the clustering of crime, though the correlations across crime types are weaker than those observed for 120

134 the aspatial measure of concentration. The clustering of homicide is significantly, albeit modestly related to the clustering of robbery (r=.356; p<.01) and assault (r=.261; p<.05). The clustering of homicide is not significantly related to the clustering of burglary using a radius of 1,000m (r=.020; p>.05). This is consistent with the fact that instrumental crimes such as burglary have been shown to be driven primarily by the distribution of criminal opportunities, while the factors that give rise to homicide are much more diffuse both in absolute terms and geographically (e.g. arguments in bars, domestic disputes, drug markets, and violent subcultures). Importantly, however, the nonsignificant association between the concentration of homicide and that of burglary is not wellcaptured using the aspatial measures of concentration as they cannot account for the location of these areas within space. This highlights a potential benefit of using a spatial measure of concentration when comparing across multiple crime types. Similar to the aspatial measures, the clustering of assault at the city-level is significantly and strongly associated with the clustering of robbery (r=.738; p<.01) as is the clustering of assault with the clustering of burglary (r=.508; p<.05). These results persist in the subsample of cities (n=73) for which measures of concentration for all crime types are available. Subsequent analyses reveal that the bivariate correlation between the clustering of homicide and the clustering of burglary is significant using a radius of 2,000m (r=.369; p<.05) which suggests this larger radius captures a wider range of areas where both homicide and burglary occur (results shown in Appendix C, Table 4). The correlations shown in Table 4 of Appendix C are consistent with this idea, as the size of the radius increases the bivariate relationship between the measures of clustering across crime types also becomes stronger. For the spatial H measure generated using a radius of 2,000 meters, the relationship between the clustering of all crime types is significant and modest in strength, rangin from.307 to.728 (p<.01). Table 5.7 presents the rank-order of crime concentration and clustering across the various crime types considered, providing some additional context to the bivariate correlations presented in Table 5.6. Here cities were ranked in terms of their aspatial (Panel A) and spatial (Panel B) concentrations of robbery in the first column, with the corresponding ranks yielded by each measure for the other crime types listed in subsequent columns. Looking at Panel A, while there is evidence that a high degree of concentration in robbery tends to be associated with the concentration of other forms of crime, there are instances when the differences are appreciable. For example, Austin, TX is ranked relatively highly in terms of the concentration of robbery, assault, but is less concentrated in terms of homicide and burglary. As seen in Figure 11, in the city of Chandler, Arizona the 121

135 number of high-crime tracts, those which account for 50% of the city s crime totals (denoted in red) varies by crime type. While the proportion of tracts which account for 50 percent of the city s total homicide, robbery and assault is pretty small (indicating a relatively high concentration), the number of tracts needed to account for the 50 percent of the city s total number of burglaries is larger, leading to a lower ranking in regards to the concentration of burglary. Panel B of Table 5.7 presents the rank order of crime clustering using the spatial measure of concentration (H) with a radius of 1,000m. Consistent with the correlations reported in Table 5.6, there is considerably less correspondence in terms of rank-order in terms of clustering between the four crime types considered. For example, Jacksonville is ranked relatively high in terms of robbery (3 rd ), assault (6 th ) and burglary (10 th ), yet is 41 nd in terms of the clustering of homicide. Figure 12 illustrates the variation in clustering present across crime types in Worcester, MA. Here again, the highest crime tracts (i.e. those that account for 50% of the city s crime totals) are denoted in red. In Worcester, while robbery and assault tend to be highly clustered, ranked 5 th and 11 th respectively, homicide (48 th ) and burglary (32 nd ) are to a lesser degree, consisting of neighborhoods from various parts of the city. Additional analyses which examined the rank-ordering in clustering across crime types (not presented here) are consistent with the correlations shown in Table C.4, which suggests that the correspondence in the degree of clustering across crime types increases as the size of the radius expands. Overall, the results presented in Tables 5.6 and 5.7 suggest that in cities where one form of crime is highly concentrated or clustered, other forms of crime also tend to be. However, the bivariate relationships tend to be weak-to-moderate in strength, suggesting that when discussing the concentration of crime within a given city it may be important to consider disaggregating by crime type. As past research has relied on measures of crime concentration based on total crime, or all calls for service which often do not delineate specific reasons for calls, the conclusions drawn may obscure between-city variation which could become apparent using data disaggregated by crime type. Since crimes such as assault and burglary and larceny (not discussed here) are more common, it is also possible that their distribution may be driving the results seen in past research. Therefore, it seems prudent to expand our knowledge of the concentration of crime by producing estimates of concentration which are crime-specific, which may lead to some interesting insights regarding the variation present between cities. This highlights an advantage of the crime-specific approach taken by this dissertation. 122

136 This chapter has been devoted to answering the first research question posed in this dissertation (Does the concentration and/or clustering of crime vary across U.S. cities?). Results suggest that the concentration and clustering of crime does exhibit substantial variability across the cities included in the NNCS. Additionally, although the two measures used to capture the concentration and clustering of crime are significantly related to one another, there is evidence that they each capture slightly different dimensions of concentration. Further, the results presented here suggest variation exists in the concentration and clustering of crime across different forms of crime. Just because one form of crime (i.e. robbery) is highly concentrated or clustered within a given city does not necessarily mean that the other forms of crime will be exhibit the same degree of concentration relative to other places. Prior to a full discussion of the implications of these findings, the results of a series of multivariate models designed to answer the second research question central to this dissertation are discussed. Chapter 6 assesses whether or not the observed variation in the concentration and clustering of crime has an impact on the between-city differences in the total volume of crime, net of other factors known to contribute to city crime rates. 123

137 Figure 4: Distribution of the Aspatial Measure of Concentration Figure 5: Distribution of the Spatial Concentration of Robbery 124

138 Figure 6: Distribution of the Spatial Concentration of Aggravated Assault Figure 7: Distribution of the Spatial Concentration of Homicide 125

139 Figure 8: Distribution of the Spatial Concentration of Burglary Figure 9: Cities in which Crime is Concentrated but not Spatially Clustered 126

140 Figure 10: Cities in which Crime is Concentrated and also Spatially Clustered Figure 11: Variation in Concentration across Crime Types in Chandler, AZ 127

141 Figure 12: Variation in Clustering Across Crime Types in Worcester, MA Table 5.1: Summary Statistics for the Concentration on Crime n Mean Median Min Max SD Skew Kurtosis Panel A: Max Samples Homicide Robbery Assault Burglary Panel B: Valid Listwise Homicide Robbery Assault Burglary