The Pennsylvania State University. The Graduate School. College of the Liberal Arts NEIGHBORHOODS, LAND-USE, AND ROBBERY RATES:

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1 The Pennsylvania State University The Graduate School College of the Liberal Arts NEIGHBORHOODS, LAND-USE, AND ROBBERY RATES: A TEST OF ROUTINE ACTIVITY THEORY A Thesis in Crime, Law, and Justice by Karen L. Hayslett-McCall 2002 Karen L. Hayslett-McCall Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2002

2 We approve the thesis of Karen L. Hayslett-McCall Date of Signature Richard Felson Professor of Crime, Law, and Justice and Sociology Thesis Advisor Chair of Committee Thomas J. Bernard Professor of Criminal Justice and Sociology D. Wayne Osgood Professor of Crime, Law, and Justice and Sociology Graduate Program Chair of Crime, Law, and Justice Stephen A. Matthews Associate Professor of Demography Adjunct Assistant Professor of Geography

3 ABSTRACT Routine activity theory suggests that neighborhood-level activity patterns influence crime rates, and that the convergence of three elements in space and time a motivated offender, a suitable target, and the absence of a capable guardian result in increased likelihood of criminal events. Opportunities for crime increase when neighborhood land-use patterns are conducive to crime. Criminogenic land-uses include intermixed patterns of residential, commercial, industrial, and vacant lands within neighborhoods, as well as the presence of particular establishments, such as shopping mall. Routine activity theorists suggest that criminogenic land-uses influence crime in two ways: (a) by inhibiting an area s social control capacity, and (b) by attracting particular types of routine activities (e.g., consuming alcohol at a bar, selling/using drugs in abandoned structures). These land-use patterns may explain why disadvantaged neighborhoods have higher crime rates than more advantaged areas. This dissertation examined whether the effect of neighborhood disadvantage on crime may be a function of its association with criminogenic land-use patterns. This research also examined whether criminogenic land-uses have greater effects in disadvantaged neighborhoods. By understanding the effects of land-use, planners and managers could make changes in land-use patterns that decrease crime rates. This dissertation examined the possible relationships between criminogenic land-use and crime, as measured by calls-for-service to the police. To address these issues, this research uses census and tax parcel data from three cities (Lincoln, NE; Columbus, OH; and San Antonio, TX), which vary in terms of size and racial composition. Within each city, census data are being used to create measures of neighborhood social composition, including concentrated disadvantage, population density, and residential mobility. In addition, land-use diversity indices, iii

4 created from tax parcel data, are used to indicate the degree to which single-family residential, multiple-family residential, commercial, industrial, and vacant or abandoned lands are intermixed within neighborhoods. Land-use diversity is measured using the Shannon Index, where the highest value occurs when equal areas of all types of land-use are present in a neighborhood. The three cities vary in both the size of their populations and their social composition. Thus, this dissertation also examined whether the effects of land-use are consistent across the three research sites. One problem with prior research on communities and crime is that it has largely ignored spatial autocorrelation the fact that locations that are near to each other are also likely to have similar levels of poverty, residential instability, and crime. Because ordinary regression models cannot control for spatial autocorrelation, traditional estimates are biased and inferences based on these models are likely to be incorrect. To address this problem, this study uses state-of-theart spatial regression analytic techniques. iv

5 TABLE OF CONTENTS LIST OF FIGURES...viii LIST OF TABLES... x ACKNOWLEDGEMENTS... xii THE NEIGHBORHOOD-CRIME RELATIONSHIP... 1 Introduction... 1 Social Disorganization Theory... 4 Routine Activity Theory... 8 Comparison of the Theories The Present Study Chapter 1 Summary ROUTINE ACTIVITES AND LAND-USE Introduction Social Characteristics and Crime The Land Use-Crime Relationship Interactions between Social Characteristics and Land-Use The Current Research Chapter 2 Summary METHODOLOGY AND DATA The Study Sites Data Preparation Sources of Data Calls-for-Service v

6 School Data Dependent Variables Independent Variables Chapter 3 Summary GIS AND SPATIAL REGRESSION Introduction GIS Definitions and Data GIS From Inception to Completion of a Project Analytic Techniques of Prior Studies Geographically Referenced Data and Spatial Autocorrelation GIS and the Current Study Analytic Strategy Chapter 4 Summary RESULTS FROM SAN ANTONIO, TX Introduction Descriptive Statistics Analyses for Autocorrelation Multivariate Analyses Chapter 5 Summary RESULTS FROM LINCOLN, NE Introduction Descriptive Statistics Analyses for Autocorrelation vi

7 Multivariate Analyses Chapter 6 Summary RESULTS IN COLUMBUS, OHIO Introduction Descriptive Statistics Analyses for Autocorrelation Multivariate Analyses Chapter 7 Summary DISCUSSION AND CONCLUSIONS Introduction Findings across the Three Research Sites Implications for Theory Implications for Policy Future Directions for the Current Project Limitations of the Present Study BIBLOGRAPHY APPENDIX vii

8 LIST OF FIGURES Figure 1. Neighborhood Characteristics Related to Crime Rates Figure 2. Land-Use as a Mediator of Social Characteristics on Crime Rates Figure 3. Hypothetical Model with the Addition of Interactions between Land-Use and Social Characteristics Regressed on Crime Figure 4. Illustration of Actual Residential Land-Use Patterns from a Sample of Blockgroups in San Antonio, TX Figure 5. San Antonio Land Use Heterogeneity, Tract-Level Figure 6. San Antonio Land-Use Heterogeneity, Blockgroup-Level Figure 7. The Main Components of a Geographical Information System Figure 8: Geospatial Data Layers to be used in the Project Figure 9. Example of Positive Autocorrelation, High Attribute Values Clustered Together (Evidence of Contagion or Spillover) Figure 10. Example of Negative Autocorrelation, Values Are Evenly Distributed (Evidence of Competition or Revulsion) Figure 11. Example of Zero Autocorrelation, Values Are Independent of Their Location (Evidence of Randomness Values Assigned by the Flip of a Coin) Figure 12. Example of a Queen Join Count Statistic Figure 13: Map of San Antonio Police Department s Six Patrol Districts with Substation Locations Noted Figure 13. Robbery Rates in San Antonio, TX Figure 14. Ten Category Histogram Logged Robbery Rates in San Antonio, TX Figure 15. Moran s I Value for Logged Robbery Rates in San Antonio, TX viii

9 Figure 16. High Robbery Areas in San Antonio, TX Figure 17. Robbery Rates in Lincoln, NE Figure 18. Ten Category Histogram of Logged Robbery Rates in Lincoln, NE Figure 19. Moran s I Value for Robbery Rates in Lincoln, Nebraska Figure 20. High Robbery Areas in Lincoln, NE Figure 21. Robbery Rates in Columbus, OH (Map Presented in Quartiles) Figure 22. Ten Category Histogram of Logged Robbery Rates in Columbus, OH Figure 23. Moran s I Value for Robbery Rates in Columbus, OH Figure 24. High Robbery Areas in Columbus, OH ix

10 LIST OF TABLES Table 1: Expected Direction of Influence of Land-Use and Interaction Variables Table 2. Racial and Ethnic Profiles of the Three Study Sites Table 3. Summary of Factors of Interest Available by Study Site Table Crime Rates per 1,000 Persons and Police Department Personnel Profiles of the 3 Study Sites Table 5: Description of Preliminary Data Sources and Information They Provide Table 7. Percent of Address-Matching Success for Robbery Events by City of Occurrence Table 8. Analytic Strategy Table 9. Description of Variables Used in Regression Models Table 10. Correlation Matrix of Independent Variables within Blockgroups Third, the concentrated immigration index was related to higher robbery rates in neighborhoods. Thus, blockgroups with higher levels of this index were more likely to report higher levels of robbery. Generally these neighborhoods are more likely to be highly populated and in the center of the city both of which are more likely to be near motivated offenders. Table 11. Spatial Regression Results for Robbery Rates in San Antonio Table 11. Spatial Regression Results for Robbery Rates in San Antonio Table 12. Description of Variables Used in Regression Models (N = 174) Table 13. Correlation Matrix of Independent Variables within Blockgroups (N = 174) Table 14. OLS Results for Robbery Rates in Lincoln, NE at the Blockgroup Level Table 15. Description of Variables Used in Regression Models (Columbus, N = 845) Table 16. Correlation Matrix of Independent Variables within Blockgroups (N = 845) x

11 Table 17. Spatial Regression Results for Robbery Rates in Columbus (N = 830) Table 18. Significant Findings across the Three Research Sites Table 19. OLS Results for Robbery Rates in San Antonio at the Blockgroup Level Table 20. OLS Results for Robbery Rates in Columbus at the Blockgroup Level xi

12 ACKNOWLEDGEMENTS I would like to acknowledge the support and assistance of several individuals, without whose help this dissertation would not have been possible. First, I would like to thank my chair, Rich Felson, for his thoughtful and poignant critiques throughout this process. His efforts have improved both this dissertation and my skills as an academic in many fundamental ways. My sincere appreciation also goes to all of the members of my dissertation committee: Rich Felson, Tom Bernard, Wayne Osgood, and Stephen Matthews. They were my academic Dream Team, serving as mentors and guides, brilliant theoretical minds, statistical wizards, and as a GIS and geography guru. The opportunity to work with each of these gentlemen was a blessing. Thanks go to Stephen Matthews, in his capacity as the Director of the GIA Core in the Population Research Institute. Stephen provided training to improve my skills, both within the Core and from outside workshops. He always provided opportunities for growth. In addition, I would like to thank all of the GIA Core staff who have taught me the fundamentals of GIS, worked through problems with me, and offered their friendship: Stephen Matthews, Jim Detwiler, Steve Graham, and Michelle Zeiders. The GIA Core gave me a home and I will miss each of you. I would like to offer my perpetual gratitude to Carole Pearce and Frances Burden. They have both proofread and commented on many drafts of this work and both provided support in innumerable ways. The support of my family and friends was instrumental in the completion of this dissertation. They always made me feel close, even though we were so far from home. I would like to say a special thanks to Amanda for just being herself. Finally, my eternal gratitude goes to my husband, Rodney, who put his dreams on hold to make mine come true. I love you. xii

13 CHAPTER 1 THE NEIGHBORHOOD-CRIME RELATIONSHIP Introduction Researchers have long been interested in why and how neighborhoods influence crime rates. To study this question, two predominant spatial explanations of crime have emerged: social disorganization theory (Sampson, 1997; Sampson, Morenoff, & Earls, 1999; Sampson & Wilson, 1995; Shaw, 1929; Shaw & McKay, 1942; Wirth, 1938) and routine activity theory (Cohen & Felson, 1979; Rountree, Land, & Miethe, 1994). When studying how and why neighborhoods affect crime, researchers from both spatial explanation camps examine why crime seems to concentrate in distinct types of neighborhoods, such as highly disadvantaged neighborhoods. Some researchers have suggested that neighborhoods and places could be criminogenic in and of themselves (Sampson & Raudenbush, 1999; Sherman, Gartin, & Buerger, 1989; Stark, 1987). In the past twenty years, social disorganization researchers have focused more narrowly on the social control aspects of the theory, and have placed prominence on census and survey measures of neighborhood composition and neighborhood social controls (Hunter, 1985; Sampson, 1985, 1987). As such, these social scientists largely have abandoned the early discussions of neighborhood land-use. However, routine activity theorists address the criminogenic effects of land-use directly and they offer a place-based description of the mechanisms by which neighborhoods are related to criminal activities. Routine activity theorists suggest that neighborhood-level activity patterns influence crime rates, and that the convergence of three elements in space and time a motivated offender, a suitable target, and the absence of a capable guardian result in increased likelihood of

14 criminal events. Space is central to this theory, with space being defined as the site where criminogenic activity patterns and the three theoretically required elements come together. Opportunities for crime increase when neighborhood land-use patterns are conducive to crime. Criminogenic land-uses include the presence of particular establishments, such as shopping malls and other commercial locations, and their direct influence on the local crime rates, as well as the presence of these same types of establishments in otherwise residentially homogenous neighborhoods. Routine activity theorists suggest that land-use influences crime in two ways: (a) by inhibiting an area s social control capacity, and (b) by attracting particular types of routine activities (e.g., consuming alcohol at a bar, selling/using drugs in abandoned structures). Thus, how land parcels are zoned (e.g., single-family residential, multiple-family residential, industrial, commercial, etc.) and how land-uses are interwoven within a neighborhood may affect crime. In addition, the current study examined the effects of local institutions (e.g., schools, banks, convenience stores, etc.) for two theoretical reasons. First, local institutions may be more likely to contribute to crime due to increasing opportunities for deviance (Cohen & Felson, 1979; Felson, 1987). Local institutions may be criminogenic due to their pull of outsiders into the area, which may increase outsiders familiarity with the neighborhood and may inhibit social control by making the definition of neighborhood resident versus stranger more difficult. Second, the local institutional base may mediate the relationship between social conditions and crime, as suggested in recent research (Peterson, Krivo, & Harris, 2000). Peterson et al. suggest that some institutions (e.g., banks, libraries, and recreation centers) can increase the social ties in an area and can promote economic stability. As noted above, disadvantaged neighborhoods have higher crime rates than more advantaged areas. Following the tenets of routine activity theory, this dissertation tests whether 2

15 the effect of neighborhood disadvantage on crime may be a function of its association with criminogenic land-use patterns. This research also examined whether criminogenic land-uses have greater effects in disadvantaged neighborhoods. To address these issues, this research uses census and tax parcel data from three cities (Lincoln, NE; Columbus, OH; and San Antonio, TX), which vary in terms of size and racial composition. Within each city, census data are used to create measures of neighborhood social composition, including concentrated disadvantage, population density, and residential mobility. In addition, land-use diversity indices, created from tax parcel data, are being used to indicate the degree to which single-family residential, multiplefamily residential, commercial, industrial, and vacant or abandoned lands are intermixed within neighborhoods. Because the cities vary in size (from under a quarter million residents to well over a million residents) and social composition (different ethnic/racial representation in their populations), this dissertation also examined whether findings hold across sites. The current research attempts to expand the relevant literature on neighborhoods and crime in two ways by expanding the types and numbers of land-uses studied in a single model and by using better specified statistical models. The current research examined the direct effects of a variety of institutions that researchers have found to be related to neighborhood crime. In addition, the current research introduces a measure of land-use heterogeneity to study the effects of the existence of multiple types of land-uses on crime. Furthermore, the current research follows recent work in the field by using Geospatial Information Systems (GIS) (Smith, Frazee, & Davison, 2000) and spatial regression techniques to study the crime-neighborhood relationship (Baller, Anselin, Messner, Deane, & Hawkins, 2001; Morenoff, Sampson, & Raudenbush, 2001). 3

16 Social Disorganization Theory Since the early 1900 s, researchers have been fascinated by how and why neighborhoods affected social conditions within their boundaries (Park, 1916; Park & Burgess, 1925). Shaw (1929) and Shaw and McKay s (1942) social disorganization theory introduced the importance of both social conditions and land-use in the study of community social organization and delinquency. Historically, social disorganization theory has become the primary theory used to explain the crime-neighborhood phenomenon. As such, the theoretical developments and research associated with social disorganization theory have been provided in this chapter to present a background to other bodies of research investigating the relationship between neighborhoods and crime, such as the routine activity theoretical approach being used in this dissertation. Social disorganization theory arose from the early discussions of the Chicago School of Human Ecology regarding rapid social change in neighborhoods and the breakdown of social control mechanisms (Vold, Bernard, & Snipes, 1998). Ecology was the study of plants, animals, and their relationships to each other, all examined within the confines of their natural environment. Human ecology was the study of people and their relationships to each other (i.e., bonds) within a neighborhood. The key to understanding neighborhood mechanisms was the dynamic equilibrium, or the balance, between individuals and their natural habitat/neighborhood (Vold et al., 1998). Thus, Shaw and McKay (1942) saw delinquency as being intertwined with an individual s environment or neighborhood. Social disorganization theory has evolved over time. Theorists initially suggested a simple causal sequence that lead to social disorganization and crime. That is, social disorganization theorists viewed the community processes that lead to crime as a linear 4

17 process this point of view of social disorganization theory has become known as the linear model. During the infancy of the linear model, Wirth (1938) was interested in the growing sense of anonymity even in neighborhoods of residence. He felt that residential neighborhoods had long been the harbor for interpersonal knowledge of those persons who lived nearby. Simply put, historically neighborhoods were viewed as a location where people living on the same block would know who each other were. Wirth believed that anonymity was evidenced by the loss of primary contacts, and the effects of formal control replacing traditional informal neighborhood controls. He felt that increasing population sizes, population density, and heterogeneity in neighborhoods were the causes of this process. Park and Burgess (1925) suggested that different neighborhood processes heterogeneity, poverty, and mobility were the cause of lowered social control, which in turn increased social disorganization. In turn, they felt that increased social disorganization lead to more crime. This body of research began with the investigation of two processes -- biotic and cultural. Park and Burgess considered biotic processes to include the sub-social and unplanned spatial outcomes of competition for resources, while cultural processes were the norms, values, and organization of society (Park, 1916; Park, Burgess, & McKenzie, 1925). These researchers saw the urban community as a dynamic adaptive system in which competition served as the principle organizing force. The ecologists saw the biotic and cultural processes as having evolved into a systematic progression of events that resulted in segregation of industries, social classes, and neighborhood activity patterns (Frisbie, Parker, & Kasarda, 1988). When urban areas were formed, certain types of social and physical phenomenon clustered more closely together. For example, poverty, crime, commerce, and industry tended to cluster at the center of cities. And, as cities grew, types of industries and persons of similar financial means were 5

18 clustered into neighborhoods, which in turn clustered into larger areas of the city (i.e., the central business district). Park and Burgess students followed the Chicago School s theoretical development by performing case studies of Chicago neighborhoods. Two sets of these researchers, Thrasher (1927) and Shaw and McKay (1929; 1942), focused on studies of crime. Thrasher (1927) was interested in the relationship between gangs and place. He found that gangs, and gang activity, were more likely to be found in the areas that were between those that could easily be labeled as commercial and residential. Shaw (1929) and Shaw and McKay (1942) focused their efforts on studying the relationship between juvenile delinquency and neighborhoods. These researchers supported Thrasher s findings that crime was likely to be higher in neighborhoods that were interstitial, or the intermixed conglomerates of commercial, industrial, and residential land-use neighborhoods. Shaw (1929) and Shaw and McKay s (1942) social disorganization theory introduced structural factors into research on community social organization and delinquency. These researchers argued that low economic status, ethnic heterogeneity, and residential mobility led to the disruption of a community s social organization and community solidarity. Shaw and McKay emphasized that place mattered in addition to the characteristics of residents. Their findings demonstrated that places could be thought of as criminogenic, as high crime areas tended to persist, despite changes in neighborhood composition. Later works by Wirth and Colleagues began to emphasize that the local community was actually a complex system of friendship, kinship, and associational networks into which new generations and new residents are assimilated while the community passes through its own lifecycle (Park and Burgess, 1969; Park, 1936; 1952; Park, Burgess, and McKenzie, 1928). The 6

19 systemic social disorganization perspective viewed the local community in terms of formal and informal interactions and socialization processes. Kasarda and Janowitz (1974) suggested that researchers should take this systemic approach when studying community attachment in mass society, rather than the linear model of social disorganization theory. In the last twenty years, there has been a resurgence of researchers exploring the impact of social disorganization on a neighborhood s or community s ability to exert informal social control on its inhabitants. A number of researchers works have become known as part of the systemic social disorganization model (Bursik & Grasmick, 1993; Hunter, 1985; Sampson, 1985, 1987; Sampson & Groves, 1989). In this version of the theory, tenure (i.e., length of residence) became a key variable of interest, and population size and density were no longer viewed as crucial to the weakening of bonds between individuals or the lessening of community sentiments (Kasarda & Janowitz, 1974). This new group of researchers has asked whether the collective of neighborhood residents had the capacity to realize its goals of an orderly, safe neighborhood. The relationship of social integration factors and crime has created a large theoretical and research response in the field. Systemic theorists have discussed the theory in terms of three levels of social control. Hunter (1985) introduced the notion of three types of social orders present in communities. First, the private social order was seen as the most personal level, and involved the social bond that is negotiated between participants (i.e., family). Second, the parochial order was the intermediate social order, and it occurred in residential neighborhoods or other places where individuals live in close proximity. For example, the parochial order would include the influence that neighbor residents have over their neighbors children. Third, the public order was the domain in which the first two domains interacted. Hunter viewed crime as evidence of the breakdown between the social orders, and that none of the social orders could 7

20 maintain order separately. Hunter suggested that the parochial order can act as surveillance for more formal institutions like the police which do not have the manpower for that job... [thus], the two [orders] have different functions, each is limited, and they are mutually interdependent upon one another (p. 236). From the development of theory about three levels of social control, Bursik and Grasmick (1993) created a model to explain increased crime rates in neighborhoods that were socially disorganized. Their work indicated that networks between individuals and networks that were more pervasive neighborhood-wide could impact the levels of private and parochial control respectively. Further, Bursik and Grasmick s model showed that both private and parochial levels of control could in turn impact neighborhood crime rates. Routine Activity Theory Routine activity theory is a place-based theory first developed in the 1970s and 1980s. Routine activity theorists offer an alternate explanation of neighborhood variations in crime rates that builds on some of the basic notions of the original, linear social disorganization model. The pioneers of this theory suggest that activity patterns influenced crime rates in places such as neighborhoods (Cohen & Felson, 1979). As noted in the introduction to this chapter, Cohen and Felson feel that the convergence in space and time of three elements a motivated offender, a suitable target, and the absence of a capable guardian result in more crime. Specifically, Cohen and Felson suggest that guardianship by ordinary citizens of one another and of property as they go about routine activities may be one of the most neglected elements in sociological research on crime, especially since it links seemingly unrelated social roles and relationships to the occurrence or absence of illegal acts (p. 590). Cohen and Felson (1979) also contend that illegal activities must feed upon other 8

21 activities, and as such, the structure of legal, routine activities must be considered when studying crime rates (p. 590). They suggest that a community s organization, or structure, should be studied to understand the relationship between neighborhoods and crime. And, routine activity theory suggests that patterns of activity and vary across neighborhoods, and that some of these patterns of activity may covary with crime rates. Thus, these theorists suggests that neighborhood activity patterns may increase the probability that motivated offenders will converge in time and space with suitable targets and the absence of a capable guardian. One example of this is that opportunities for crime increase when neighborhood land-use patterns are conducive to crime. Criminogenic land-uses include the presence of particular establishments, such as convenience stores and malls, as well as intermixed patterns of residential, commercial, industrial, and vacant lands within neighborhoods. Routine activity theorists, such as Cohen and Felson (1979) and Felson (1987) suggest that criminogenic land-uses influence crime in two ways: (a) by inhibiting an area s social control capacity, and (b) by attracting particular types of routine activities (e.g., consuming alcohol at a bar, selling/using drugs in abandoned structures). In 1987, Felson found that types of land-use were related to increased crime. Felson built on the prior theoretical position and even outlined of twelve types of land-use that he felt were conducive to crime. He felt that the routine activity approach offered the best explanation for the upsurge in crime rates in the 1960 s and 1970 s, and that although this theory had been used previously for describing exploitative crime, it could be extended to other definitions of crime as well (Felson, 1987, 1983). Felson argued that for the new age of automobility, traditional measures used in ecological research (e.g., the central business district and industrial zones) were no longer adequate. Felson (1987) proposed that a new ecology of crime was required for the study of crime. His work argued for the better understanding of the flow of individuals through 9

22 areas for the purposes of crime control. That is, he suggested that land-use patterns and road structures could be developed in manners that would reduce an area s vulnerability to higher crime rates. One of the contentions of routine activity theory is that technology, such as the automobile, has an impact on crime patterns. Individuals can easily travel and become familiar with neighborhoods other than their own. As such, an area s social control capacity can be inhibited through the influx of outsiders to an area, either by travel through an area or by the attraction of outsiders to an area via local neighborhood establishments (i.e., a convenience store/gas station). Further, Taylor and Harrell (1996) suggest that the internal layouts, boundary characteristics, and traffic patterns of neighborhoods may encourage or discourage different types of crime (p. 10). Taylor and Harrell (1996) describe two forms of land-uses that may increase the number of people in a geographic area. First, neighborhood land-uses that may attract more individuals to an area are movement generators, such as roads. Second, other neighborhood land-uses are attractors, and they include both commercial and public-area land-uses. Cohen and Felson (1979) suggest that land-uses that attract and generate an inundation of outsiders to an area, thereby increasing a neighborhood s target suitability for outsiders by providing more possibilities of direct-contact with the target (p. 595). Rengert and Wasilchick (1985) and Taylor and Harrell (1996) support the contention that changes in land-use patterns affected the exposure potential between offenders and that area s residents and users. The Seattle Victimization Study (SVS) researchers have suggested that busy places, such as shopping areas and bus stops, bring greater neighborhood traffic and human density to an area (Rountree et al., 1994; Rountree & Land, 1996). Routine activity theory suggests that this higher level of 10

23 exposure creates more opportunity for the three necessary elements to converge. The SVS researchers focused on burglary and violent crime (including physical assaults and forcible robberies). These researchers used a survey procedure to assess an individual s exposure to motivated offenders, target attractiveness, and guardianship to assess opportunity (Miethe & Meier, 1994; Rountree et al., 1994; Rountree & Land, 1996; Warner & Pierce, 1993). The SVS researchers used a dangerous activity index to measure exposure to violent crime and the number of evenings in the previous week during which the home was left unoccupied to measure exposure to burglary. The SVS researchers also measured neighborhood social conditions (e.g., incivilities (disorder), ethnic heterogeneity, and population density) and community characteristic measures (e.g., community integration, percent male, mean age, mean income, and the neighborhood burglary rate). In some of the original SVS research, Miethe and McDowall (1993) found that an individual s risk of violent victimization was higher if he or she was younger, participated in more dangerous public activities, had lower family incomes, and lived alone. An individual s risk of burglary victimization was higher if he or she was younger, left his or her home unoccupied more frequently, possessed goods that were more expensive, took fewer safety precautions, and lived alone. Contextual information demonstrated that target attractiveness and guardianship (particularly safety precautions) altered resident s risk of burglary in affluent neighborhoods but had little effect in poorer neighborhoods. An individual s risk of violent victimization was increased by poorer socioeconomic conditions and higher levels of public activity, with no interaction found between individual and neighborhood level measures. Rountree et al. (1994) reanalyzed the data using Hierarchical Logistic Modeling and found stronger contextual effects. In this work, the SVS researchers found that both neighborhood 11

24 incivilities and ethnic heterogeneity increased burglary risk, yet when both incivilities and ethnic heterogeneity were present an individual s risk of being burgled declined. This reanalysis also showed that residents of these areas could also reduce their risk of burglary if they used more precautions than their counterparts did. In another work that builds on routine activity theory, Felson (1998) argued that the construction patterns of neighborhoods were related to the types of crimes that take place within their boundaries. For example, neighborhoods with high numbers of multi-family housing, especially high-rise apartments, had higher population density. These same neighborhoods have tended to have much less burglary and household larceny than neighborhoods that were more spread out. High-rise apartments provided some protection from potential offenders entering a domicile through the window, except at the level of the first story. Further, neighborhoods with lower population densities were more likely to have higher assault and rape rates. Other crimes, such as motor vehicle theft, were likely to increase with increases in population density. Felson suggested that the differences between crime types in the influence of land-use were related to the modus operandi of each crime. For example, neighborhoods with houses that have backyards or side entries provide more opportunity for offenders to commit a burglary unobserved by neighbors. Stark developed a place-based theory about neighborhoods and crime in a series of thirty propositions (Stark, 1987). To this end, Stark suggested that an ecological theory of crime should explain why crime was so heavily concentrated in certain areas. He believed that an ecological theory of crime is meant to achieve an explanation of why crime and deviance are so heavily concentrated in certain areas, and to pose this explanation in terms that do not depend entirely (or even primarily) on compositional effects that is, on answers in terms of kinds of people (p. 12

25 904). Stark believed that factors such as population density, poverty, and transience were related to crime. Crime appeared to concentrate in disadvantaged, heavily populated neighborhoods. Stark (1987) believed that poor, dense neighborhoods tended to be mixed-use neighborhoods. He defined mixed-use as urban areas where residential and commercial land use [co-existed], where homes, apartments, retail shops, and even light industry [were] mixed together (p. 898). Along the same theoretical lines, Stark believed that opportunity was an important key when discussing crime rates. His theory further suggested that mixed land-use increased the opportunities for deviance. Stark suggested that opportunities for criminal events amplified by increasing outsiders familiarity with neighborhood, by creating more opportunities/targets for crime, and by increasing the rates of congregation outside homes. Stark provided a series of testable propositions in his theory of deviant places. One proposition was that poor, dense, mixed-use neighborhoods with high transience rates tend to be dilapidated and have reduced levels of community surveillance (i.e., fewer guardians) and more opportunities for deviance. Thus, Stark felt that these types of variables were related to higher rates of crime. Thus, Stark s theory of deviant places also suggested that land-use was related to crime. Felson (1998) and the routine activity theorists have offered an interesting explanation about the relationship between economically deprived neighborhoods and high crime rates. Felson argued that while poverty-stricken people have had an incentive to commit crime, all of us are relatively deprived and thus we all have had incentives to commit crime. Further, routine activity theory suggested that residents living in areas near criminogenic land-uses had greater opportunity to commit crime. Thus, poor individuals have had to live in areas with greater nonresidential use, which puts both adults and juveniles in closer proximity to malls and other 13

26 places that afford opportunity. Felson s argument avoided compositional explanations, as poverty-stricken people are not, in and of themselves, more criminal the land-uses were the key to differences in crime rates between neighborhoods. Comparison of the Theories Social disorganization theory and routine activity theory are compatible explanations of the neighborhood-crime relationship. Both theories are control theories, in that they both assume that people would commit crime if left to their own devices (Vold et al., 1998, p. 201). Social disorganization theorists believe that control mechanisms are present in neighborhoods, and that these controls have varying degrees of effectiveness in deterring or preventing predisposed offenders from committing crime. Routine activity theorists also discuss control, in that motivated offenders are assumed to be disinhibited and will commit crime against suitable targets when a capable guardian is not present. Both social disorganization and routine activity theorists agree that when controls are in place, predisposed offenders will be less likely to act even when an opportunity is present. In the case of social disorganization theory, the social controls include the three levels of social control (private, parochial, and public), while routine activities discusses control in terms of the presence of the capable guardian. While still taking an ecological approach, routine activity theorists study the concept of neighborhood influence from a slightly different perspective. These theorists explanations are more situational, in that neighborhoods are one part of the context surrounding the crime event (Vold et al., 1998). Cohen and Felson (1979) do not discuss the influences on or degree of motivation present in an offender. Rather, routine activity theorists focus their efforts on explaining how neighborhoods and land-use may provide opportunities for crime. For example, routine activity theorists emphasize the importance of surveillance in a neighborhood, while 14

27 social disorganization theorists focus on the importance of neighborhood bonds for the prevention of crime. Stark s theory of deviant places also argues that land-use changed the amount of criminal opportunity (Stark, 1987). Thus, routine activity traditionally gives greater attention to land-use patterns than do current social disorganization researchers. This research will study the relationship between neighborhoods and crime. Based on the past findings of ecological research, this study will utilize a wide range of variables in an attempt to better capture the effects of neighborhood land-use on crime. The current research will attempt to further the extant research by exploring interactions between land-uses and neighborhood social contexts. (See Chapter 2 for a review of research on the relationship between crime and land-use patterns.) The Present Study This research was designed an aggregate-level study of neighborhoods and crime rates, that tested the tenets of routine activity theory. First, the study explored whether various patterns of zoned land-use and the presence of local establishments and institutions affected crime rates at the blockgroup level. Were mixed land-use neighborhoods or neighborhoods with commercial establishments, such as shopping centers or convenience stores, more likely to have higher crime rates? This research examined whether land-use impacts crime rates above-and-beyond neighborhood demographics commonly associated with higher crime rates, such as the percent of female headed-households. Second, this dissertation examined its key issue whether the effect of neighborhood disadvantage on crime may be a function of its association with criminogenic land-use patterns. Third, the current research tested whether land-uses interacted with neighborhood-level social characteristics to influence crime rates. For example, did neighborhoods that are more heterogeneous in land-use have a stronger effect on crime rates in 15

28 neighborhoods with higher population densities? To accomplish these goals, I constructed a unique data set incorporating three types of information to examine the context of crime. Sources of data included calls-for-service data from three police agencies (San Antonio, TX; Lincoln, NE; and Columbus, OH), census data, and tax parcel data from the appraisers offices located in either the three cities or their affiliated counties. The sources of data were integrated using a Geospatial Information System (GIS) database. GIS has been commonly thought of and used as a computer mapping technology. However, the uses of GIS can go far beyond simply creating a map. Through GIS, this researcher enhanced the value of the three independent data sets in two ways. First, GIS provided a framework for combining these datasets. A large proportion of non-census data contained no reference or obvious link to census information. As such, non-census data would have been difficult to aggregate to census-defined neighborhoods, such as blockgroups, without using GIS technologies. The current project used GIS to place crime locations and tax parcel information within the appropriate census blockgroup and tract. In order to accomplish these goals, this research controlled for variables that previously have been found to be related to crime rates. For example, research suggests that young males are more likely to be offenders, thus the current project controlled for the aggregate percentages of these demographics in each neighborhood. Second, GIS was used to assess the effects of nearby neighborhoods on one another. For example, did criminogenic land-use patterns in one neighborhood effect the crime rates of nearby neighborhoods? Chapter 1 Summary In summary, this dissertation first examined the possible relationships between various 16

29 land-uses and crime, as measured by calls-for-service to the police. Specifically, this dissertation tested to determine whether land-use patterns could be criminogenic with these patterns explaining why disadvantaged neighborhoods have higher crime rates than more advantaged areas. Second, this dissertation examined whether the effect of disadvantage on crime may be partially mediated by neighborhood-level criminogenic land-use patterns. Third, this research tested for interactions between neighborhood social characteristics and land-use. For example, the study examined whether more heterogeneous land-use patterns have greater effects in disadvantaged neighborhoods. To address these issues, this research used census and tax parcel data from three cities that vary in terms of size and racial composition. Census data were used to create measures of neighborhood social composition, and land-use diversity indices were created from tax parcel data. This research attempted to go beyond previous work on neighborhoods in crime in several ways. This dissertation attempted to argue for the inclusion of land-use measures in all ecological research if land-use did indeed mediate the effects of social characteristics, such as disadvantage, on crime. Further, this research included the use of creative measures of land-use, such as the diversity indices, which may prove to be useful to researchers studying a variety of neighborhood social processes (e.g., resident well-being, social ties, etc.). The current research also attempted to go beyond prior research in its statistical methodology. One problem with prior research on communities and research on crime was that it has largely ignored spatial autocorrelation in the dependent variable of study the fact that locations near to each other are also likely to have similar levels of poverty, residential instability, and crime. Because ordinary regression models cannot control for spatial autocorrelation, traditional estimates may have been biased and inferences based on these models were likely to be incorrect. To address this 17

30 problem, this study used spatial regression analytic techniques. This research also attempted to demonstrate that city and county government agencies may enhance their own understandings of their community s crime rates by sharing data across agencies. That is, the datasets used in this study were commonly used by several government agencies (e.g., planning departments, police agencies, etc.), and criminologists have recently explored their utility (Smith et al., 2000). This dissertation will argue for the integrated use of this data by city governments to develop a more comprehensive understanding of the effects of the built environment on residents and crime rates. Joining the individual datasets may prove to be useful when making planning decisions regarding appropriate land-use zoning procedures (Village of Euclid v. Amber Realty Co., 1926). That is, as suggested by Felson (1987), the study of the relationship between neighborhood land-use and crime may lead to city planning efforts that could divert likely offenders from suitable targets. The current research also attempts to encourage the creative use of geospatial data, GIS methodologies, and appropriately specified regression techniques. As noted above, these methodological and statistical techniques are still new and underused in criminology. Only in the past year have works of this nature been published (Baller et al., 2001; Morenoff et al., 2001). Chapter 2 presents three bodies of research, and a series of theoretical diagrams that will be used to illustrate the model of this study. Chapter 2 closes with a description of this study s hypotheses and the rationale associated with each hypothesis. Chapter 3 describes the three study sites, the data sources, and the variables and measures. Chapter 4 presents a discussion of Geospatial Information Systems (GIS), spatial autocorrelation (the clustering of similar values), the study s analysis plan, and a description of spatial regression analysis. Chapters 5 through 7 18

31 describe analytic issues and the results from San Antonio, TX; Lincoln, NE; and Columbus, OH respectively. The final chapter, Chapter 8, presents the conclusions from the research. The results for robbery are compared and contrasted across the three sites. Theoretical and research implications are also be presented. 19

32 CHAPTER 2 ROUTINE ACTIVITES AND LAND-USE Introduction One of the richest fields of study in Criminology is the study of criminogenic factors associated with neighborhoods. Historically, there have been three important literatures examining the neighborhood-crime relationship. This chapter first presents the three literatures that represent the development of knowledge of crime and communities. The first body of research examines the relationship between neighborhood social characteristics and crime. The second sphere of research focuses on the relationship between types of land-use and crime. The third area addresses two types of interactions that can occur, one amongst land-uses (e.g., between residential and commercial land-uses) and the other between land-uses and neighborhood social characteristics (e.g., neighborhood bars and poverty levels). Preceding the discussion of each of these bodies of research is a diagrammed model that helps to conceptualize both the significant variables and the findings. The review of prior research serves to frame the theoretical model of this study, and demonstrate how the current research will contribute to the understanding of the complex relationship between neighborhood characteristics, land-uses, and crime. Chapter 2 closes with a presentation of the hypotheses of this study. Social Characteristics and Crime In the last twenty years, social disorganization theorists have focused more closely on neighborhood-level financial resources and activity patterns, including criminal activity patterns. The systemic social disorganizationists have explored a series of new variables, beyond the variables of the original linear model, that have identified levels of social disorganization by a neighborhood s social composition patterns. These theorists have developed a body of research 20

33 that studies the linear disorganization variables including neighborhood level of poverty and the amount of residential mobility. In addition, they have updated the model to include variables such as: the percentage of residents that rent versus own, a higher number of abandoned buildings, the percentage of female-headed households, and high crime rates (Morenoff & Sampson, 1997; Sampson, 1985, 1987; Sampson & Groves, 1989; Sampson et al., 1999; Sampson & Raudenbush, 1999; Skogan, 1990; Taylor, 1997; Warner & Pierce, 1993). Where these variables are present, researchers have used this expanded list of important neighborhood factors to define disorganization within neighborhoods. Systemic social disorganization research focused on how the indicators of social disorganization in a neighborhood affected the collective ability of the residents to maintain informal social control, thereby reducing neighborhood crime and victimization. Researchers have found that neighborhood social conditions (i.e., poverty), social integration factors, and population density were related to neighborhood crime. The study will control for each of these types of variables in its statistical analyses. The following diagram illustrates three relationships described by researchers between a neighborhood s characteristics and its crime rates (See Figure 1). 21

34 Figure 1. Neighborhood Characteristics Related to Crime Rates Disadvantage: poverty and heterogeneit.y. There has been a great deal of research conducted examining the individual effects of poverty and race on crime. Collectively, scholars have developed the convention of describing neighborhood disadvantage using the percent of residents living in poverty, the percent of unemployed residents, the percent receiving public assistance, the percent of female-headed households, the percent black, and ethnic heterogeneity. This section first explores the effects of poverty and race/ethnic heterogeneity independently and then describes more recent research, which suggests these variables are highly interrelated. While examining the effects of poverty, Sampson (1987) linked persistently high crime rates in black neighborhoods to community-level unemployment and poor economic conditions. More specifically, Miethe and McDowall (1993) demonstrated that the socioeconomic conditions of the neighborhood significantly increased an individual s risk of being burgled. Furthermore, the authors found that areas with signs of socioeconomic decay showed increased risk of other victimizations. Controlling for the percentage of female-headed households and 22

35 structural density, Warner and Pierce (1993) found that poverty was related to higher rates of assault and burglary. When neighborhood mobility was low, poverty was also related to rates of robbery. Finally, Smith et al. (2000) found that single-parent households were related to higher numbers of street robberies. Other scholars have examined the relationship between ethnic heterogeneity or percent black and crime. For instance, Rountree, Land, and Miethe (1994) found that a high level of neighborhood ethnic heterogeneity was related to increased victimization risks. Regarding burglary rates, Warner and Pierce found that heterogeneity interacted with poverty, with racial heterogeneity increasing crime when poverty was low. Since the writings of Shaw and McKay, the relationship between poverty, race/ethnic heterogeneity, and crime has been studied in concert. That is, the effects of poverty (including the percentage of female-headed households with children) and race on crime have become intertwined. While works from the 1980 s and 1990 s examined the effects of the two variables independently, these themes are difficult to disentangle. Recently, some researchers have called for the merging of these concepts. For example, Sampson et al. have argued researchers should collapse each of these highly interrelated individual variables into a single index of concentrated disadvantage, examining this measure as a single concept (See Morenoff et al., 2001; Sampson et al., 1999; Sampson, Raudenbush, & Earls, 1997). Further, Sampson et al. support this argument by noting that prior research [reflects] neighborhood segregation mechanisms that concentrate the poor, African Americans, and single-parent families with children (Morenoff et al., 2001, p. 528) (See also Bursik & Grasmick, 1993; Land et al., 1990; Massey and Denton, 1993; Wilson, 1987). The results of the factor analysis support prior research and [reflects] neighborhood segregation mechanisms that concentrate the poor, African Americans, and single-parent families 23

36 with children (Sampson, Morenoff, and Earls, 2001, p. 528). Further, Morenoff et al. (2001) argued that it is problematic to test the effects of percent black separately from the other components of the index, as prior research has found no white neighborhoods that reflect the extreme disadvantage faced by black neighborhoods (See also Krivo and Peterson, 2000; Sampson & Wilson, 1995). Morenoff et al. (2001) have related higher scores on their concentrated disadvantage index to higher levels of neighborhood violence. Neighborhood-level social integration factors. Researchers have also linked social integration factors to neighborhood crime and to the ability to reduce crime at the neighborhood level. Social integration factors have been measured as the social bonds between neighbors, levels of family disruption, and neighborhood levels of residential instability. Sampson and Groves (1989) found that sparse social bonds and low participation in community organizations were related to more crime in a community. Sampson (1987) also linked persistently high crime rates in black neighborhoods to family disruption. Sampson (1985) found that social integration factors, such as family structure and mobility affected individual likelihood of being a crime victim. Indeed, some research has shown that neighborhoods still capable of exercising some level of informal social control could reduce their collective likelihood of criminal victimization. Sampson, Raudenbush, and Earls (1997) found that the relationships between concentrated disadvantage and violent victimization as well as between neighborhood residential instability and violent victimization were mediated significantly by collective efficacy. Collective efficacy was defined as the social cohesion among neighbors combined with their willingness to intervene on behalf of the common good (p. 918). Thus, neighborhoods that were willing to act for the common good were able to affect the number of victimizations that occurred within their boundaries. 24

37 It must be noted that the relationship between neighborhood social integration factors is an important area of study. Regrettably, this dissertation is limited by its data constraints (i.e., no survey materials) and is unable to control for collective efficacy. However, both social disorganization and routine activity researchers commonly use another measure of neighborhood-level social integration residential instability (often measured as the percentage of persons who have moved in a neighborhood in the past five years, the percent of residents that rent rather than own, or as a scale combining movers and renters). Regardless of how it is measured, both groups of researchers consider residential instability to be an important predictor of neighborhood crime rates. Further, systemic social disorganizationists suggest that residential stability is key in the development of the very neighborhood networks (i.e., social integration) that are responsible for the development of collective efficacy (Bellair, 1997; Kasarda & Janowitz, 1974). Residential instability has been found to significantly predict both property and violent crime rates. Bellair (1997) found that stable neighborhoods had significantly lower rates of motor vehicle theft. LaGrange (1999) found that a higher percent of renters in a neighborhood was related to more mischief and transit property crimes. Supporting this research, Sampson and Groves (1989) found that residential instability was related to higher levels of auto theft and vandalism. Regarding violence, Sampson, Raudenbush, and Earls (1997) found that stable neighborhoods had lower rates of violence. Two sets of researchers, Bellair (1997) and Peterson, Krivo, and Harris (2000), found that residential instability was related to higher violent crime rates. Specifically, these researchers suggested that instability was related to increased rates of rape, robbery, and aggravated assault. However, not all research has supported the notion of residential instability playing an important factor in neighborhood crime rates. For example, 25

38 Warner and Pierce (1993) found no relationship between mobility, as defined by numbers of persons moving in the past five years, and burglary or robbery, yet found that mobility was related to lower rates of assault. Population density. Population density has also been related to crime. Researchers have suggested that larger numbers of crime targets are available in densely populated neighborhoods, which also contributes to higher levels of anonymity in these neighborhoods. According to the routine activity approach, higher levels of exposure between potential offenders and their targets creates more opportunity for crime. For example, Sampson (1985) and Klinger (1997) found that densely populated neighborhoods were more likely to be crime-ridden. Miethe and McDowall (1993) showed that areas with higher levels of public activity showed increased risk of victimizations. Further, Warner and Pierce (1993) found that higher structural density (i.e., the density of residences per area) was associated with higher rates of assault and robbery, but was not related to burglary rates in neighborhoods. Figure 2. Land-Use as a Mediator of Social Characteristics on Crime Rates 26

39 The Land Use-Crime Relationship Routine activity theory explains the effects of social activity patterns and criminal opportunity on crime (Cohen & Felson, 1979; Felson, 1987). Neighborhood land-use patterns influence the movement of individuals in and through an area, which in turn affect crime rates. In order to study this relationship, researchers have been faced with the question of how to conceptualize neighborhood land-use what to measure and how when trying to examine the physical layout of a neighborhood. Traditionally in this literature, land-use and neighborhood physical conditions have been measured as types and counts of places (i.e., types of land use in an area) or as the amount of physical disorder (i.e., vandalism) in an area. Many researchers have studied the effects of land-uses one at a time and their influence on the immediately surrounding neighborhood. Other researchers have studied the effects of land-use in terms of their theoretical relationship to the increase of crime. As mentioned in Chapter 1, arguments have been made that land-use patterns or types of land-use affect crime by inhibiting an area s social control capacity, and/or by attracting particular types of routine activities. Thus, neighborhood land-uses may serve as movement generators, attractors, or busy places (Rountree et al., 1994; Rountree & Land, 1996; Taylor & Harrell, 1996). A large body of research has examined the relationship between land-uses and crime. Two recent studies have reintroduced the concept of neighborhood land-use to the study of neighborhood crime rates. First, Peterson et al. (2000) performed a study of violent crime rates in Columbus, Ohio. They examined the effects of local institutions on violent crime. Their measures of local institutions included total counts of libraries, recreation centers, retail establishments, and bars that were located within the tract in question or within adjacent tracts. Peterson et al. s research is an outgrowth of the place-based theories, and their 27

40 findings about local institutions are presented below in the following categories: publicly used land, commercial land-use, and residential land-use. Regarding publicly used land, several researchers have found that particular land-uses are related to increased crime. White (1990) found that neighborhood permeability, or the number of roads coming into an area, significantly influenced burglary rates. In addition, many researchers have found mass transit stops (e.g., bus stops, subway stops, etc.) are related to danger (Block & Block, 2000; Block & Davis, 1996; Roncek, 2000) and have their highest effect in the areas immediately surrounding the location (Block & Block, 2000). Other publicly-used land types have been related to crime rates. Sherman, Gartin, and Buerger (1989) suggested that parks were also a hot spot for some crimes. Roncek (2000) found that public junior and senior high schools were related to higher amounts of robbery. Smith (1996) found that public parking facilities, and their environmental design, were related to increases in criminal activity. Several researchers have found that hospitals were related to higher crime rates (except murder and robbery) (Roncek & Fladung, 1983; Roncek & Franz, 1988; Smith, 1987). Finally, Smith, Frazee, and Davison (2000) found that the number of vacant lots and parking lots were related to higher street robbery rates. Regarding commercially used land, researchers have found that particular land-uses are related to property and violent crime. Taylor and Harrell (1996) found that the proportion of nonresidential land-uses was related to higher burglary rates. More specifically, Harrell and Gouvis (1994) found that the percentage of lots zoned for commercial use was related to increased robbery rates. Duffala (1976) and Gordon and Brill (1996) demonstrated that convenience stores were related to increased crime rates, as were shopping centers, while many researchers have found that bars were related to higher rates of crime in the surrounding areas (Frisbie et al., 1988; 28

41 Peterson et al., 2000; Roncek & Maier, 1991). Of these studies, Peterson et al. (2000) found that bars were related to increased violent crime. However, while bars had sizable effects on the violent crime rates, the authors were unable to tell whether this finding is related to disinhibition, defensive posturing, and social interaction in situated company, or the fact that numerous bars are signs of disorder and weak community control in a neighborhood (Peterson et al., 2000, p.56). In another recent study, Smith, Frazee, and Davison (2000) found that several commercial land-use variables were related to more street robberies: number of stores; number of bars, restaurants, and gas stations; and the number of commercial places (defined as businesses, industries, and warehouses). They also found that the number of motels/hotels was related to lower street robbery rates. Peterson, Krivo, and Harris (2000) found that some institutions were not related to violent crime rates retail establishments and libraries. The authors suggested two possible explanations for these findings. First, retail establishments and libraries may lack sufficient social influence to buffer advantaged areas from violent crime or to combat the criminogenic patterns of crime in disadvantaged neighborhoods. Second, Peterson et al. suggest that their measures of these institutions may have been inadequate to detect the social power that these types of institutions may have. Finally, differing structures and patterns of residential land-use at the neighborhood level may affect particular crime rates. For example, apartment buildings are related to increased crime, especially if not adequately designed (e.g., poor control of entrance ways, etc.) (Gordon & Brill, 1996). Further, Fagan and Davies (2000) found that public housing increased the violent crime rates in the immediately surrounding areas. In support of this finding, Roncek (2000) found that public housing was related to higher amounts of robbery. Smith, Frazee, and Davison 29

42 (2000) found that higher rates of multi-family residential lots were related to higher street robbery rates, however the number of owner occupied lots were related to lower rates. One study does not neatly fit into the commercial, public, and residential categories presented above. The SVS researchers examine a series of commercial and public places compiled into a single index of busy places, which influence movement through neighborhoods. They composed an additive index of the number of busy places reported by neighborhood respondents that were within three to four blocks of their home, including: schools, convenience stores, bars, fast food restaurants, office buildings, parks or playgrounds, shopping malls, hotels, and bus stops. The concept of busy places was based on respondents perceptions, and the measure is a count of busy places present. The findings of this body of research show that the movement of individuals through an area due to the presence of busy places was related to neighborhood crime. According to the SVS researchers, higher levels of public activity increased an individual s risk of violent victimization, with no interaction found between individual and neighborhood level measures (Miethe & McDowall, 1993). In addition, Rountree et al. also found that neighborhood incivilities and busy places increased the overall mean risk of violent victimization. The present study focuses on the extension of routine activity theory by exploring the relationship between land-uses and crime in greater depth than previous researchers. In addition, the study will contribute to the methodology by using GIS to assist in the combination of multiple data sources, to explore spatial relationships between areas, and finally to prepare data for spatial regression models. Initially, the current study examined the effects of various landuses on crime. However, a key component of the current study is to go beyond the findings of prior research, by examining whether land-uses mediate some of the relationships between 30

43 neighborhood characteristics and crime (See Figure 3). Figure 3. Hypothetical Model with the Addition of Interactions between Land-Use and Social Characteristics Regressed on Crime Interactions between Social Characteristics and Land-Use Beyond the direct relationships that social characteristics and land-uses may have on crime, theorists have also suggested that these variables may interact to increase crime (Jacobs, 1961; Newman, 1972; Sherman et al., 1989; Stark, 1987; Taylor & Gottfredson, 1986). That is, institutions and land-uses may have stronger effects in some neighborhoods (i.e., disadvantaged neighborhoods), while having weaker or no effect in others. Routine activity theory implies interaction effects in its basic tenets when suggesting that the convergence of the three elements necessary for a crime is enhanced at particular places. That is, any location or social condition that increases the vulnerability of targets, whether they are individuals or places (i.e., houses), and simultaneously increases the presence of motivated offenders will interact to increase crime 31

44 in more than an additive fashion. Motivated offenders must be present, as the vulnerability of a place or person is not of consequence if a motivated offender is not present to take advantage of the situation. For example, routine activity theorists would suggest that neighborhood social conditions such as high population density and neighborhood disadvantage are likely to increase the number of motivated offenders. Furthermore, neighborhood social conditions and land-uses interact to increase vulnerability through the reduction of neighborhood-level guardianship capacities and the increase of potential targets. Stark (1987) suggests that poor, dense neighborhoods tend to be mixed-use neighborhoods, and that mixed land-use increases the opportunities for deviance by increasing outsiders familiarity with the neighborhood, by creating more opportunities/targets for crime, and by increasing the rates of congregation outside homes. For example, Stark proposes that social conditions interact with the physical layout of neighborhoods in that poor, dense, mixed-use neighborhoods with high transience rates tend to be dilapidated and have reduced levels community surveillance (i.e., fewer guardians) and more opportunities for deviance. The routine activity tenets are supported by the traditional neighborhood literature from social disorganization and broken-windows theorists who also suggest that neighborhood social conditions may interact with neighborhood land-uses. These theorists speculate that neighborhood disorder serves to break down neighborhood regulatory capabilities and institutions (Black, 1976; Kelling & Moore, 1988; Kelling, 1998). For example, Kelling (1998) suggests that crime undermines the influence of social and economic institutions within areas, such as schools, churches, and commerce, which were traditionally considered to have positive influences on their surrounding neighborhoods. Two recent papers examined the relationship between land-use, social characteristics, and 32

45 their interactions on crime at the neighborhood level. Peterson, Krivo, and Harris (2000) argued that disadvantaged neighborhoods lacked many of the economic and social resources for developing new and maintaining old institutional bases. They stated that this lack of resources may be related to an employer s choice not to invest in poverty-stricken neighborhoods. Peterson et al. found only one local institution that interacted with neighborhood social conditions and crime recreation centers were related to reduced violent crime, but only when located in extremely economically deprived neighborhoods. Calling for additional research, these researchers hope to better specify the types of institutions that have either crime-reducing or crime-inducing impacts. In another recent study, Smith et al. (2000) suggested that few interaction effects had been found in previous research due to size of land aggregations being chosen. Thus, larger areas, such as a census tract, may have masked the within-unit heterogeneity present at smaller aggregations like the face-block. In order to test for interaction effects, Smith, Frazee, and Davison (2000) attempted to integrate social disorganization theory and routine activity theory. Smith et al. (2000) used tax data, 1990 Census data, and 1993 crime-incident data for a midsized southeastern city in the United States. These researchers address-matched robbery incidents and telephone directory data (e.g., bars, restaurants, convenience stores, taverns, and cocktail lounges) to the face-block level using Atlas-GIS. Robbery incidents were restricted to street robberies, thus did not include commercial robberies or robberies of banks. Unlike previous research, Smith et al. (2000) found several interactions between individual risk factors (as specified by routine activity theory) and type of neighborhood (as specified in social disorganization theory) (p. 491). Several interactions predicted street robbery, including interactions between single-parent households and (1) the number of 33

46 motels/hotels and (2) the number of bars, restaurants, and gas stations. Smith et al. also found interactions related to the distance of land-uses from the central region of the city including: an interaction between distance from center of city and the number of multi-family residences; an interaction between distance from the center of city and the number of bars, restaurants, and gas stations; and, an interaction between the distance from the center of city and the number of vacant or park lots. Smith et al. argued that using smaller levels of aggregation (i.e., the face block) allowed them to see the types of interactions that occur between social characteristics and criminogenic land-uses. The Current Research This review of routine activity research suggested that lower neighborhood social control was related to higher crime rates. Thus, the current research examines the effects of neighborhood variables on robbery rates, as indicated by the levels of calls-for-service to the police. Figure 3 illustrates the model that will be tested. Research questions and hypotheses. This study is designed to examine the relationship between neighborhood land-use patterns and crime. Specifically, the study is designed to analyze which social characteristics and which land-use patterns explain the differing rates of robbery across neighborhoods. There were several relevant research questions. First, did neighborhood land-use patterns influence crime rates in the neighborhood? Second, did criminogenic land-use patterns mediate the effects of neighborhood social characteristics on crime. Third, did neighborhood land-use patterns have greater effects in disadvantaged neighborhoods? As noted above, routine activity theorists suggested that criminogenic land-uses influence crime in two basic ways: (a) by inhibiting an area s social control capacity, and (b) by attracting particular types of activities that are conducive to crime. Thus, if land-uses were indeed related 34

47 to crime, then neighborhood crime rates should reflect this relationship. As such, the hypotheses for the current research have been presented in groupings based on their theoretical relationship to neighborhood robbery rates. However, when composing hypotheses about the influence of land-uses on crime rates, one must also consider the modus operandi of the type of crime (Felson, 1998, 2002). As mentioned in Chapter 1, Felson suggests that the differences in the influence of landuse by crime were related to the protocol that a motivated offender must follow to commit each type crime. Therefore, the crime of robbery and the motivations behind committing a robbery, who may become the motivated offenders or the suitable targets of a robbery, and the neighborhood context(s) surrounding a robbery should be understood. Robbery is a form of illicit coercion in which an offender unlawfully takes goods from the possession of a target, against his or her will, by means of force (Luckenbill, 1980, p.361). Robbery spans a variety of typologies from an armed commercial robbery to a mugging, but regardless all robberies share two common elements (1) violence or the threat of violence, and (2) theft or attempted theft (Cook, 1976, p. 175). Robbers often rely on creating an illusion of the potential for severe bodily harm, and help to create this illusion by using the element of surprise (Wright and Decker, 1997). Motivated robbers are likely to be individuals who are seeking to solve a pressing financial need, and their suitable targets are likely to be selected on the basis of appearing to be individuals who are carrying ready cash and who will offer little resistance (Wright and Decker, 1997). Further, some research suggests that offenders are more likely to be black or Hispanic (Cook, 1976). Some patterns are suggested by the findings of past research regarding the likelihood of individuals becoming victims of or the motivated offenders of a robbery. Victims are more likely 35

48 to be black, male, and young adults or youths (Cook, 1976). These individuals may become suitable targets, as they are more likely to be in contact with motivated offenders. Thus, due to differential association or residential segregation, minority individuals are more likely to come in contact with motivated offenders (Felson, Baumer, Messner, 2000). However, some robbers may prefer to rob whites, who are widely regarded as less likely than blacks to offer resistance (Wright and Decker, 1997, p. 94). Felson (2002) argued that the construction patterns of neighborhoods were related to the types of crimes that take place within their boundaries. For example, the relationship between robbery and the locations of local establishments, such as convenience stores, may be due to their suitability as targets of commercial robbery. These businesses are known for dealing in cash. Further, convenience stores/gas stations and banks often house Automated Teller Machines (ATM s), which are used to obtain cash. ATM users may be seen as suitable targets, thus the presence of these land-uses which may draw the attention of motivated offenders and the presence of suitable targets is likely to increase the number of personal robbery events in a neighborhood. Other land-uses such as hospitals also provide suitable targets for personal robberies. Patrons and employees of hospitals often come and leave at intervals throughout the day and night, with the irregular schedules of arrival and departure becoming vulnerable victims with less guardianship available. Basically, robbery is a crime motivated by cash or other items that can be liquidated quickly and without risk. Individuals or the guardians of an establishment, such as a convenience store, make the most suitable targets when access to them is easy, capable guardianship is absent or low (i.e., the individual is isolated), and when cash is readily available. The inhibition of social control. Land-uses have been categorized according to their purpose, or patterns of use. Several categories of land-use have been found to be related to 36

49 neighborhood crime rates (see literature review above). The percentage of land dedicated to commercial and industrial uses was expected to be related to the numbers of robberies. The percentage of multi-family residences was not expected to predict the numbers of robberies. Furthermore, Thrasher (1927), Shaw and McKay (1929; 1942), Cohen and Felson (1983), and Stark (1987) have all suggested that the co-existence of residential, commercial, light industry, and other land-uses was related to higher crime rates. The intermixing of land-use inhibits an area s social control capacity, and provides opportunities for the types of legal routine activities off which illegal activities feed (i.e., the cash business at a convenience store may increase its suitability as a target for robbery). I used the Shannon Index, or the Entropy Index, to attempt to measure the degree of intermixing between several land-uses: single family residential, multiple family residential, commercial, industrial, and vacant lands (See Chapter 3 for a full description of the variable and its components). I expected that higher values of this index will be related to higher rates of robbery. Attractors of criminogenic routine activities. Offenders are more likely to be youthful and male. In addition, particular types of land-uses are likely to bring young men together, thereby increasing the likelihood of criminal events occurring. Malls and schools are likely to be the locations of routine activities that attract young men into group settings. Thus, I expected that the presence of malls and schools in a neighborhood, or nearby neighborhood, would be related to increased robberies within a neighborhood. These land-uses were likely to attract the youthful male offender that would be likely to commit crimes if capable guardians were not present. Some land-uses were also likely to bring non-residents to a neighborhood. Outsiders have served as both motivated offenders and suitable targets. As such, land-uses such as convenience 37

50 stores, stores, hotels, hospitals, and banks were expected to increase crime in the neighborhood. The crime events may not have been limited to the address of the commercial establishment itself, with victims being selected as suitable targets on their way to and from the establishment (i.e., one block away from the bank). Thus, I expected that the presence of convenience stores/gas stations, hospitals and hotels, and malls were likely to be associated with high rates of robberies. For example, two of these land-uses were very suitable targets for robbery convenience stores/gas stations and malls. As mentioned above, these types of businesses have been known for dealing in cash, which may be seen as an opportunity for a motivated offender. Thus, I expected that the presence of these two land-uses will be related to higher rates of commercial robbery in a neighborhood. In addition, convenience stores/gas stations, malls, and banks have often served as housing for Automated Teller Machines (ATM s), which are used to obtain cash. Users of such machines may be seen as suitable targets, thus the presence of these land-uses which may draw the attention of motivated offenders and the presence of suitable targets is likely to increase the number of personal robbery events in a neighborhood. Land-uses were also expected to mediate the effects of neighborhood social characteristics, such as poverty and population density, on crime. In other words, the land-use patterns of a neighborhood were predicted to explain why disadvantaged neighborhoods had higher crime rates than more advantaged areas. Land-uses were expected to interact with a number of neighborhood social characteristics. In particular, I hypothesized that robbery would increase in densely populated neighborhoods with land-use types that attract outsiders to that neighborhood (e.g., percent commercial, percent industrial, etc.). Further, mixed-land-use was expected to interact with residential instability. The turnover in residents was expected to decrease a neighborhood's 38

51 capacity for social control in conjunction with the increased supply of both human and building suitable targets that are in a mixed land-use area. Some land-uses were also expected to interact with neighborhood social characteristics to decrease crime. For example, following the findings of Peterson et al. (2000), recreation centers were expected to reduce crime of all types in disadvantaged neighborhoods, as motivated offenders may be diverted into non-criminal activity. In addition, libraries were also expected to be related to lower robbery rates in disadvantaged neighborhoods. These two land-uses have been suggested as able to act as stabilizing forces in neighborhoods and their presence provides entertainment (recreation centers) and solid employment (libraries) in their neighborhood. However, while banks may also provide stable employment in a neighborhood, the presence of banks in a disadvantaged neighborhood may have provided opportunities for robbery of both the institution and of bank patrons. Thus, it was expected that the presence of banks in disadvantaged neighborhoods will be related to higher rates of neighborhood robbery. Chapter 2 Summary In summary, chapter 2 presents research findings about the relationship between neighborhoods and their crime rates. The chapter closes with a presentation of the hypotheses of the current study. The next chapter, Chapter 3, describes the three study sites and the data sources. A discussion about the strengths and weaknesses of calls-for-service to the police as a data source is included. The chapter closes with a description of the dependent and independent variables to be used in the study. 39

52 Table 1: Expected Direction of Influence of Land-Use and Interaction Variables Robbery Distance Central City - Index of Mixed-Use + % Multiple Family Residential Use % Industrial Use + % Commercial Use + % Vacant Land + Schools + Convenience Stores/Gas + Hospitals + Hotels + Malls + Banks + Recreation Centers + Libraries - Churches - Mixed-Use*Instability + Mixed-Use*Density + Mixed-Use*Disadvantage + Recreation Centers*Disadvantage - Libraries*Disadvantage - Banks*Disadvantage + 40

53 CHAPTER 3 METHODOLOGY AND DATA This chapter provides an overview of the research project data and methods. First, as the hypotheses for the current research project were tested in the cities of San Antonio, Texas; Lincoln, Nebraska; and Columbus, Ohio. A description of these three cities is provided. Second, the sources of data are described in detail, as are the unique challenges that must be addressed when using these forms of data. Third, the measurement of the dependent and independent variables of the current research study are described. The Study Sites Three cities have been selected for this study: San Antonio, Lincoln, and Columbus. These cities were selected for at least four key reasons. First, these sites were each patrolled by one major police agency rather than the city having fragmented districts of patrol (i.e., the majority of each city has been incorporated into the governmental control of that city and was patrolled by one police force). Second, these sites were selected because they represent major metropolitan areas of three different sizes and population compositions (See Table 2). Third, all cities maintained records in an electronic format, which added to the ease of collection of large numbers of calls-for-service data that are necessary for this research study. Finally, each of police departments maintained a Geospatial Information Systems (GIS) Analyst section and was receptive to having a researcher use spatial analysis techniques to better understand the effects of neighborhoods on crime rates. Specifically, San Antonio Police Department (SAPD) publishes data online, while both the Lincoln and Columbus Police Departments agreed to participate in this study. Letters requesting the use of data were sent to approximately 20 large metropolitan police departments. Positive responses were received from both Columbus and Lincoln. In 41

54 addition, I personally visited with department representatives in detail to acquire the data that was needed. A few other agencies expressed interest, but either did not have the types of data available necessary to complete this study or they were currently restructuring various departments (including their research departments) and were unable to cooperate during the time frame necessary to complete this project. It is worth noting that SAPD does not publish the addresses for violent crimes that are either sex offenses or offenses against children. SAPD uses these precautions to protect the privacy of these victims (SAPD, 2001). A brief profile of each city follows. Table 2. Racial and Ethnic Profiles of the Three Study Sites San Antonio Lincoln Columbus Total Population 1,144, , ,470 % White % Black % Hispanic % Asian % American Indian (Source: American Factfinder, Census 2000) San Antonio is the 9 th largest city in the United States, and San Antonio residents enjoy a sense of history and tradition (San Antonio Visitor s Bureau, 2002). A group of Spanish explorers and missionaries came upon a large river in 1691, and named it San Antonio in honor of the day the feast day of Saint Anthony. The City itself was founded in 1718 by Father Antonio Livares, when he established the Mission San Antonio de Valero, later known as The Alamo (San Antonio Visitor s Bureau, 2002). San Antonio now has a population of over one million residents (1,144,646). The city has a diversified economy and has strong representation in agriculture, manufacturing, industrial, 42

55 government (including several military institutions), health care, tourism, and high technology. In addition to its diversified economy, the city s population is also racially heterogeneous including a majority Hispanic community (Marlin et al., 1983; Straub & Dupuis, 1988). 816 blockgroups fall entirely within the San Antonio Police Department (SAPD) patrol boundaries. Any blockgroup that extended beyond the SAPD patrol boundaries, either at the edge of the city or within the city s unincorporated zones, is excluded from the analyses. Lincoln s history is linked to the journey of Native American s to the salt deposits that are located in what is today Capital Beach Lake. In the 1850 s salt companies sent representatives to determine whether salt could be harvested from the beds in large quantities, one of which was Captain W.T. Donovan of the Cresent Salt Company. The capital of Nebraska, the city was originally named Lancaster (in honor of Donovan s home town in Pennsylvania), but was renamed just after the Civil War in honor of Abraham Lincoln. (Lincoln/Lancaster County Convention and Visitor s Bureau, 2002) Lincoln is currently the 77 th largest city and the 147 th largest metropolitan statistical area (MSA) in the United States (United States Census Bureau, 2000). The city now has a population of 225,581 (United States Census Bureau, 2000), and the population base is relatively racially homogenous. Lincoln has a diversified economic base, which is spread across agriculture, industry/manufacturing (including the production of motorcycles, tires, and pharmaceuticals), the service sector, construction, educational and health professionals, and it is the center for all Nebraska state agencies (Marlin, Avery, & Collins, 1983). The city of Columbus was founded in 1812, and became Ohio s state capital in 1816 (City of Columbus Planning Division, 2002). The city has the highest population in Ohio, with its population growing to 711,470 in Columbus is now the 15 th largest city in the nation, 43

56 and the Columbus Metropolitan Statistical Area is currently the 33 rd largest MSA in the country (United States Census Bureau, 2000). Industry/manufacturing, the service sector, construction, educational and health professionals, and finance (e.g., real estate and insurance) are the key economic sectors (Columbus Police Department, 2001; Marlin et al., 1983). According to the Greater Columbus Chamber of Commerce, business diversity is the core strength of the city s economy. Columbus is the home to five Fortune 500 companies and four Inc. 500 companies (Greater Columbus Chamber of Commerce, 2002). Columbus is a racially heterogeneous city with a large African American population (Marlin et al., 1983; Straub & Dupuis, 1988; City of Columbus, 2002). As the city has grown, it has incorporated the surrounding bedroom communities. Helping to facilitate this population growth has been the city's tremendous land area expansion. Annexation has enabled the city to grow from 39.9 square miles in 1950 to over square miles today. (City of Columbus Planning Division, 2002). The study sites share some factors of interest as well. (See Table 3) For example, diversity is evident within each research site on four main factors: economy, land-use, the race/ethnicity of residents, and the socioeconomic status of residents. The physical ecologies of the cities are heterogeneous, with land-uses varying amongst commercial, industrial/manufacturing, institutional, vacant land, and residential areas (high, medium, and low density residential). The socioeconomic status of the three cities residents varies between extreme poverty and wealth, often with pockets of more well-to-do neighborhoods being found in areas surrounded by more poverty-stricken neighborhoods. Further, the three cities all experience a large number of crime events (see Table 4 for crime rates). 44

57 Table 3. Summary of Factors of Interest Available by Study Site Lincoln Columbus San Antonio Racial Heterogeneity Economic Heterogeneity Diverse Economy Land-use Heterogeneity Small Metropolitan Area Mid-sized Metropolitan Area Large Metropolitan Area Police Department with Electronic Files Table Crime Rates per 1,000 Persons and Police Department Personnel Profiles of the 3 Study Sites Lincoln Columbus San Antonio CRIME TYPE Homicide Rape Robbery Aggravated Assault Burglary Larceny-Theft Auto Theft Data Preparation Table 5 provides a listing of the different types of data that were used in this project, which can be layered based on geographic coordinates (see Figure 4). The following section describes the data sources in detail, and describes the dependent and independent variables that were used in the analyses. 45

58 Table 5: Description of Preliminary Data Sources and Information They Provide Data Type Variables Lincoln Columbus San Antonio Calls for Service Data Crime Location Crime Types Directories/Tax Parcel Data Local Institutions Common Core of Data (98) Public School Locations School Characteristics Tax Parcel Data Zoned Land Use Parcel Specific Data Addresses Street Data Addresses and Streets Census Data 1990 Tract 1990 Blockgroup = acquired for this study. All data are for 2000 or 2001 unless specified. (2000 Census data release not anticipated until September 2002). Sources of Data To investigate the primary research topics in the manner proposed, required the construction of a unique data set for each city. These data sets are were created from an array of sources, each providing theoretically relevant information on different dimensions of the contexts of crime. Data sources included calls-for-service data; tax parcel data; census data; and Common Core Data from the National Center of Educational Statistics. Locations of residential, commercial, and industrial areas were geocoded at the tax parcel level of aggregation. Both the tax parcel data and the crime-incident data were then aggregated to the blockgroup and census tract levels. This section opens with a description of the San Antonio Police Department (SAPD), the Lincoln, Nebraska Police Department (LPD), and the Columbus, Ohio Police Department (CPD) calls-for-service data that were used in this study, and their strengths and weaknesses. Next, the section discusses the different types of data that were used to supplement the calls-for-service data in the process of creating a unique set of data for investigating the problems at hand, as well 46

59 as the variables of interest that were provided by each of these sources. Calls-for-Service This project used calls-for-service data from San Antonio Police Department, Lincoln Police Department, and Columbus Police Department. The data sources each had a variety of crimes available, but this project only focused on robbery events. These events were transformed into rates per neighborhood, which were defined as census blockgroups. Issues regarding calls-for-service data. Calls-for-service data, as with any data, had both advantages and disadvantages associated with its use. Many researchers have discussed calls-for-service data (Gove, Hughes, & Geerken, 1985; Sampson & Groves, 1989; Sherman et al., 1989; Warner & Pierce, 1993). Warner and Pierce stated that calls-for-service data are similar to official measures in that they are able to examine criminal behavior and citizens willingness to report it. Sherman et al. pointed out the benefit that is attractive to many researchers --- this was an ongoing collection of data, by police departments, that captures many different types of events. Additionally, Warner and Pierce argued that calls-for-service eliminated the effects of police discretion, as well as the fact that many calls identifying crimes go unrecorded by the police. Sampson and Groves stated that it was important to consider the extent to which crime rates reflected systematic neighborhood differences in police officer s reactions to crime (i.e., whether a report is taken or not). Similarly, it was important to consider what the community deems as important enough to call the police. Gove et al. refined this idea and stated that if a victim and the community defined an event as criminal and worthy of police intervention, then perhaps this was a good definition of the extent to which an event should be viewed as criminal. Calls-for-service data have several benefits for this research project. It is less costly and 47

60 more practical in terms of cost and time than conducting an extensive participant observation study. This type of data is more informative than police reports alone, because it includes information that may be screened out by patrol officers (i.e., not taking a report). There are limitations associated with the use of calls-for-service data. First calls-for-service data may reflect the address of a caller s location rather than the location of the criminal event. This is most problematic when doing analyses at the address level and less so when aggregating to larger areas such as census blockgroups and tracts (Warner & Pierce, 1993), as I do here. Second, there are many data cleaning considerations when dealing with any type of data, and calls-for-service data are no exception. For example, there may be multiple calls-for-service for a single incident. In addition, calls-for-service data may be coded as the calls are relayed to the dispatchers. That is, a citizen may call and report a robbery, when in actuality the crime that has occurred meets the burglary statute. Lastly, researchers must also have considered the department s coding scheme when obtaining data, as police departments will have different elaborations of addressed data that they are willing to share. School Data Following past research, public secondary schools were selected from the Common Core of Data for this research (Roncek, 2000; Roncek & Fladung, 1983). The Common Core of Data (CCD), a program of the U.S. Department of Education's National Center for Education Statistics, is a comprehensive, annual, national statistical database of information concerning all public elementary and secondary schools (approximately 91,000) and school districts (approximately 16,000) (United States Department of Education, 2001). The CCD database provided a rich data set with information about public schools that includes the names, addresses, and telephone numbers of the schools (United States Department of Education, 2001). Thus, the 48

61 public schools in San Antonio, Lincoln, and Columbus have been address-matched, thus assigned to their correct geographic location in each blockgroup. The CCD data used for each of the sites was from the academic school year. 1 Dependent Variables Neighborhood robbery rates were investigated in this research. To construct these rates, the crime-incident (calls-for-service) data were first address-matched using the GIS program ArcView 3.2a together with street and tax-parcel databases for 2000/2001. It was a straightforward task within a GIS interface to overlay geospatial data layers and establish spatial links or joins between two data layers. In this way crime events (points) were aggregated up to the census blockgroup level (areas). The number of robbery events in an area were combined with census data to generate robbery rates per 10,000 residents. Independent Variables The selected census variables followed the body of current research in Chicago (Morenoff and Sampson 1997; Sampson, Raudenbush, and Earls 1997; Sampson, Morenoff, and Earls 1999). These variables were selected as basic measures of neighborhood social characteristics that are available from Census data. Social dynamics and characteristics. The current research used two scales to describe social structure, created from 1990 census variables (Morenoff and Sampson 1997; Sampson, Raudenbush, and Earls 1997; Sampson, Morenoff, and Earls 1999). First, a concentrated economic disadvantage scale describes the relative conditions of poverty stricken neighborhoods. This scale was defined as percent of individuals below the poverty line, percent receiving public assistance, percent unemployed, and percent of households that are female- 1 The academic school year is the most recent year of data available for public use. 49

62 headed with children. Second, concentrated immigration was defined by the percent of persons foreign born, the percent linguistically isolated, and the percent Hispanic. Following the precedent of Sampson et al. (1997, 1999) the above variables were processed using factor analysis. The variables were highly interrelated and each loaded on a single factor using either principal components or alpha-scoring factor analysis (Morenoff, Sampson, and Earls, 2001; Morenoff and Sampson 1997; Sampson, Raudenbush, and Earls 1997; Sampson, Morenoff, and Earls 1999). Third, routine activity theory research suggested that neighborhood instability was also conceptually important. Thus, the current research employed an index of residential instability in the analytical models. This index was created by calculating the z-scores for the census measures of residential mobility and percent renters, summing the z-scores and then divided by two (Sampson et al., 1997, 1999). Fourth, population density was defined as the number of persons per square kilometer. Rather than relying on the physical area of land within the blockgroup to serve as a denominator for this variable, area was calculated after subtracting out commercial parcels, industrial parcels, parks and vacant lots, which are not intended for residential use. This definition was different from traditional measures of population density, as it strictly refers to the number of people in the land-use available for residential purposes. In prior work in San Antonio, the correlation between traditional density measures and the measure described in this paper was only.33. Figure 5 (see below) has provided a sample illustratration of the differences between traditional blockgroup sizes and residentially-zoned land. This illustration demonstrated that traditional measures of population density were inaccurate if they were incorporating other forms of land-uses that were not designated for households (represented as white areas on the illustration) thus, traditional population density measures most likely inaccurately represent the 50

63 population density of census blockgroups. Finally, the current research included two other individual correlates that are commonly related to crime rates the percent male and the percent between the ages of 18 and 24 in the population. Figure 4. Illustration of Actual Residential Land-Use Patterns from a Sample of Blockgroups in San Antonio, TX Example Blockgroups Residential Land Use 51

64 Measures of neighborhood land-use. Several indices of urban land-use patterns were also used. First, following early social disorganization theory distance from central city was defined as the distance between the centroid of the central downtown blockgroup/tract and the centroids of all other blockgroups/tracts in the appropriate city (Jenness, 2000). Second, as noted above, locations of residential, commercial, and industrial areas were included in the tax parcel data. As land-use patterns were dictated by city/county zoning regulation bodies, the current research suggested the divisions of land-use that were already established by the local Tax Appraiser s Office were appropriate for use as labels for type of use. Each tax parcel was zoned and had a unique land-use classification (e.g., single-residential, industrial, etc.). As these databases were constantly being edited when spot-zoning changes are made, the current research argues that this type of terminology is more appropriate and useful than terminology created by researchers. Beyond the practicality of this suggestion, this type of research lends itself to policy and applied work. That is, by basing land-use identification following agency criteria it should be easier to assess policy decisions about land-use and crime by city governments, appraisal offices, and police departments. Land-use measures that could be extracted or derived from the tax land-parcel data were represented in two forms, as an index of zoning diversity and as well as separate land-use types when deemed as theoretically important. To calculate an index of land-use heterogeneity, this research employed a form of the entropy index. The entropy index, also called the Shannon index, is a measure of relative diversity and it describes both the richness or variety of subpopulations and the equality or evenness of their sizes (Allen and Turner, 1989, p.525). This index was used to rank blockgroups. The entropy index was at its highest value when all types of land-use are equally represented in the census tract, and the index is zero when only one type of 52

65 land use is in the tract. (White, 1986; Allen and Turner, 1989) The index is calculated as follows: k H = -Σ P k log P k k=1 where H is the entropy index, P k = N k /N, N k = amount of land in the kth group, and N = sum of the total area (i.e., blockgroup) being measured. The current research used this index to measure the heterogeneity of zoning practices within blockgroups. For example, The Entropy Index combined residential, commercial, industrial, and vacant land uses. Figures 6 and 7 illustrated land-use heterogeneity in San Antonio. Note that areas in the tract map that appear to be heterogeneous are actually more homogenous at the blockgroup level. A tract level analysis may then wash out the effects of land-use heterogeneity in these areas. In addition, it is the assumption of many researchers that a smaller aggregation study area is more appropriate for studying crime (e.g., Smith et al., 2000). Thus, both of these support this project s use of blockgroups rather than tracts. In addition to examining zoning diversity and individual land-use types, the current research has drawn on past findings suggesting that several institutions as individual variables. Briefly, the following institutions have been determined to represent crime attractors: hotels/motels, convenience stores/gas stations, shopping centers, schools, and hospitals. Others have been determined to represent traditional institutions that may decrease crime (e.g., recreation centers, libraries, and banks). The presence or absence of each of these institution types were aggregated to the census blockgroup level. Much of this type of information was found in the tax parcel databases but the current research also supplemented the data using business and telephone directories as necessary. 53

66 Figure 5. San Antonio Land Use Heterogeneity, Tract-Level (Based on the quartile breakdown of the Blockgroup-Level Map, see Figure 7 Below) 54

67 Figure 6. San Antonio Land-Use Heterogeneity, Blockgroup-Level Note the different levels of homogeneity and heterogeneity apparent at this level of aggregation. 55

68 Chapter 3 Summary As described in Chapter 3, I developed a series of parallel contextual databases for each of the three cities at the blockgroup level. Chapter four first discusses the basic issues involved with geographically referenced data and the Geospatial Information Systems (GIS) processes that were involved in the preparation of data for analyses. Second, the chapter presents a discussion of exploratory analyses that were used in the project, including tests for spatial dependence in the dependent variable (robbery). Third, the chapter describes spatial regression analytic techniques and justifies their use in the current study. Finally, the chapter closes with the plan of the specific spatial regression techniques used in the study. 56

69 CHAPTER 4 GIS AND SPATIAL REGRESSION Introduction Chapter 4 opens with a review of the limitations faced by prior communities and crime researchers. Second, Chapter 4 is designed to provide an overview of Geospatial Information Systems (GIS), spatial autocorrelation, and spatial regression techniques. The chapter defines and explains GIS and details the processes used to merge data sets together and aggregate to the blockgroup level. Chapter 4 discusses the use of GIS in the current research. GIS is used from the inception of the research project in the context of address-matching and exploratory analyses to examine the effects of spatial clustering, through the development of spatial regression models. Third, the chapter introduces and explains the nature of geographically referenced data and the methodological considerations when attempting to compensate for spatial autocorrelation. Fourth, Chapter 4 closes with a discussion of the exploratory and confirmatory statistical techniques used in the current research. Finally, the chapter closes with a discussion of the strategy for analyzing the variables in question. GIS Definitions and Data What is GIS? While seemingly a simple question, the initials GIS can represent many different words, definitions, and processes. For example, GIS can stand for Geographic Information Systems, Geographical Information Sciences, Geographic Information Studies, and Geospatial Information Systems among others. For the purposes of the current research, GIS will stand for Geospatial Information Systems. In addition to having multiple phrases attached to its initials, GIS is a very difficult term to define. The difficulty may be due to its integration into many subject areas across both the hard and soft sciences, including: geography, urban planning, 57

70 engineering, landscape architecture, environmental science, demography, sociology, and criminology among others (DeMers, 2000; Huxhold, 1991; Maguire, Goodchild, and Rhind, 1991; Lee and Wong, 2001; Martin, 1991; 1996; Cromley, 2002; Longley, 2001). At a very broad level, geospatial information systems (GIS) can be defined as the ability to process spatial data into information about some portion of the earth (DeMers, 2000). A more specific and toolbased definition is that GIS is a powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes (Burrough and McDonnell, 1998). (See Figure 7 below.) Figure 7. The Main Components of a Geographical Information System (Burrough and McDonnell, 1998) Regardless of an individual s definition of choice, all forms of GIS consist of a set of tools that improve GIS analysts and researchers ability to work more effectively with map 58

71 information and non-graphic attribute data (Huxhold, 1991). Before computer mapping was widely available, cartographers, analysts, and researchers were limited by the fact that their spatial database was a drawing on a piece of paper (Burrough and McDonnell, 1998). Burrough and McDonnell (1998) state that paper maps have five important consequences for research: 1) the original data has to be reduced in volume, thus losing detail; 2) maps have to be drawn extremely accurately and the presentation of the many different themes had to be very understandable/clear; 3) large areas could only be represented on several map sheets (dependent on scale); 4) once data was placed on a map, it became expensive and timely to retrieve and combine them with other datasets; and 5) a printed map is a qualitative document that captures information at one point in time. For the purposes of the current research, GIS is defined as a facilitating and applications-led technology, which transparently assesses the importance of space, and as such should be central to our geographical understanding of the world (Longley, 2000, p. 157). The past twenty years have witnessed a tremendous growth in the field of computers and Geospatial Information Systems (GIS). Many of today s city and county governments collect data that is geographic in nature, providing the opportunity for both governmental entities and researchers to study service delivery, management, and policy-making in these governments (Huxhold, 1991). Further, the introduction of GIS was important to being able to measure and represent the spatial relationships in [these geographically referenced] data (Anselin, Cohen, Cook, Gorr, and Tita, 2000, p. 215). Data within a GIS are all georeferenced, or linked to a specific location on the surface of the Earth through a system of coordinates. One of the most common systems of coordinates incorporates the latitude and longitude of each location (Bernhardsen, 1999, p. 5). There are 59

72 three basic formats of data that can be georeferenced: points, lines, and polygons. These types of shapes are used together to produce the most accurate picture of what is reality in a place. In addition, qualities and characteristics can be assigned to each of these points, lines, and polygons (Bernhardsen, 1999). For example, a polygon can be assigned as a tax parcel, owned by John Smith, with an address of 4618 Main Street, with 3 bedrooms, 2 bathrooms, 2600 square feet of household structure, and it is zoned within a strictly residential neighborhood. The qualities of events that occur at a specific location can be assigned to a point, line, or polygon. For example, this same tax parcel owned by John Smith may have been burglarized at 10:30 am on January 14, Beyond the simple storage of georeferenced data, which could also be done on a paper map, a GIS has the ability to store relationships between different types of data. For example, John Smith s tax parcel may be located in Census tract numbered as (Bernhardsen, 1999). All data stored in a GIS project can be mapped, which allows for the visualization of the distribution of selected phenomenon, such as neighborhood crime rates. GIS From Inception to Completion of a Project As a GIS project can store information about a place and link it to its surrounding features (i.e., tract, roads, etc.), it is an invaluable tool regarding many types of research projects. Further, GIS can contribute to a research project on crime from its inception to its completion. At the beginning of this process, GIS can improve data collection techniques (i.e., selection of like neighborhoods for purposes of comparison) and the visualization of the data through the use of maps. Often a picture (i.e., the map) is worth a thousand words. However, GIS is much more than just making a map. 60

73 Figure 8: Geospatial Data Layers to be used in the Project During the stages of data preparation, researchers can also use GIS to create new variables, to merge previously unaffiliated data sets from a variety of sources (as illustrated in Figure 8), and to provide flexibility in the levels of spatial aggregation. Researchers can modify existing measures of variables when attempting to make a more accurate assessment of the real world. For example, population density is often measured as the number of persons within a census tract to give a measure of number of persons per square mile. The current project uses GIS to create a measure of population density based on the amount of land that is zoned for residential use. Census tracts may have huge areas within them, but only a small area designated for residential use. As such, measures based on the full tract may be misleading. GIS also allows researchers to create variables by assigning points to polygons. For example, as is being done in 61

74 the current research, a number of robberies may have occurred within a designated census tract. These robberies can be aggregated to a count within the census tract and then made into a rate using census population information as a denominator. Researchers can use GIS to create measures of distance. As georeferenced data is stored in reference to its neighboring data (Bernhardsen, 1999), GIS can also be used to calculate distance. Following a large body of research (i.e., Shaw and McKay, 1942), the current research uses this feature of GIS to measure the distance of the centroid of a census blockgroup from the center of the city. Researchers can also use GIS as a medium to merge previously unaffiliated data sets from a variety of sources. For example, the current research project uses GIS to merge Census data, calls-for-service data, tax parcel/land-use data, and the Common Core of Data to designate locations of schools. Once data are merged, GIS allows researchers to aggregate data vertically up the organization levels, such as from crime data, to tax parcel data, and finally to census data (Huxhold, 1991, p. 23). The current research aggregates all data to the census blockgroup level. Once a dataset is created and prepared, researchers are able to analyze the spatial relationship between variables and their values. The term spatial analysis is used to describe several techniques found in a GIS environment (Anselin, 1995; 1996; Anselin and Kelejian, 1997; Bailey & Gatrell, 1995; Ding & Fotheringham, 1992; etc.). One type of spatial analysis is exploratory in nature, and these techniques are used to describe and visualize spatial patterns, to search for spatial association, or to identify atypical observations (Anselin, 1995). Researchers are able to perform several types of exploratory spatial analyses, including: 1) the manipulation of variables in the data set (i.e., the addition of buffered areas around incidents to visually examine which other variable characteristics are present in close proximity to the event of concern) (Bailey and Gatrell, 1995; Anselin, 1988); 2) the investigation of LISA statistics (Local 62

75 Indicators of Spatial Association) 2 (Getis & Ord, 1996); as well as 3) the investigation of global statistics of spatial association that indicate the clustering of events/values. Global statistics are different in the sense that they provide a single statistic that indicates the overall pattern of clustering evidenced in the data, while local statistics only indicate the relationship between one areal unit (i.e., the census tract) and those areal units immediately surrounding it (Kamber, Mollenkoph, & Ross, 2000). 3 The current research uses GIS to investigate the clustering of events/values with the global Moran s I statistic. Once the exploratory stages of analysis are complete, GIS gives researchers the capacity to create simple contiguity matrices for representing neighbor relationships between different areal units (Anselin, et al., 2000, p. 215) and to prepare datasets for use in multivariate spatial regression models. ArcView 3.2a in tandem with the SpaceStat extension allows researchers to perform an export of data into a format suitable for use in the SpaceStat regression model sequence. This same use of the SpaceStat extension also provides the option to create a contiguity matrix. The current research project uses this extension to export data, including the contiguity pattern (see below for an in-depth discussion of spatial autocorrelation and contiguity measures). Finally, toward the completion of a project, confirmatory spatial analyses will be performed on the data for the current research (see below for a discussion of which spatial regression techniques are being used in this study). While exploratory techniques suggest hypotheses and whether or not spatial clustering may be present in datasets, confirmatory spatial analysis is necessary to test these hypotheses (Anselin, et. al., 2000; Anselin and Getis, 1992). 2 One example of a commonly used LISA is the local Moran s I statistic, which allows researchers to investigate the degree of spatial autocorrelation in their data. 3 The sum of the local Moran s I is equal to the global Moran s I (Getis and Ord, 1996). 63

76 Analytic Techniques of Prior Studies With the increase in technological capabilities, criminology has refocused some of its early efforts to understand the neighborhood-crime relationship. For instance, Sherman, Gartin, and Buerger s (1989) identification of crime hot spots renewed interest in the study of the geographic components of crime. Sherman et al. s work suggested that the distribution of crime in neighborhoods was not determined by the distribution of offenders alone rather that places themselves could be criminogenic. Since this time, a broad spectrum of researchers have used GIS to examine the effects that place has on crime, with the most recent publications in this area showing a heavier reliance on GIS and spatial technologies (For examples see Baller, Anselin, Messner, Deane, and Hawkins, 2001; Morenoff, Sampson, and Raudenbush, 2001; Smith, Frazee, and Davison, 2000; Peterson, Krivo, and Harris, 2000; Morenoff and Sampson, 1997). However, a large portion of the existing research has suffered from many methodological and statistical limitations (for a review of research findings, see Chapter 2). As such, much of the extant neighborhoods and crime research seems to fail to explain neighborhood differences adequately. The geographic correlation of events often causes problems for traditional statistical techniques like ordinary least squares (OLS) regression. One of the primary assumptions that criminologists must make in order to use OLS is that of independence (McClendon, 1994; Anselin, 1995; 1996; Anselin and Kelejian, 1997). But, geographic data, by their very nature, imply the possibility that more proximate neighborhoods are more likely to have similar values of social (i.e., poverty) and physical characteristics (i.e., industry). Thus, the assumption of independence is often violated by this type of data (Anselin, 1995; 1996; Anselin and Kelejian, 1997; Odland, 1988; Robinson, 1998). In addition, autocorrelated data may result in the standard error estimates being biased. Standard error 64

77 estimates of regression coefficients are underestimated if there is positive spatial autocorrelation and overestimated if negative spatial autocorrelation is present (Odland, 1988; Robinson, 1998). This project attempts to perform an evaluation of the potential inadequacies of prior studies by examining past methods used. When studying neighborhood context, researchers must address the associated geographic characteristics. The layouts of neighborhoods in large cities are such that neighborhoods that are struggling to maintain their collective neighborhood control lie adjacent to neighborhoods that are crime-ridden and demoralized. Research suggests that in some urban areas, neighborhoods that share social characteristics will cluster together. For example, neighborhoods with higher victimization rates, with higher percentages of minority residence or ethnic heterogeneity, and with other characteristics of lower informal social control will cluster together in different locations within large metropolitan areas (Shaw and McKay, 1942; Massey and Denton, 1993; Wilson, 1987; Sampson, 1985, 1987). Researchers should expect that the defined size of neighborhoods will have important consequences for their results. Relatively large numbers of criminal events are often massed into relatively small geographical spaces. For example, Sherman, Gartin, and Buerger (1989) find a substantial concentration of all police calls... [indeed] over half of all calls to the police... [being] dispatched to 3.3% of all addresses and intersections (p. 37). As such, the effects of the spatial relations between independent crime events may wash out when summed across to larger areas. In effect, this process averages the crime events across an area. Therefore, a crime rate of five crimes within one block is a higher rate than if those same five crimes were dispersed throughout a census tract. Thus, the dispersion pattern of criminal events is important, because summing the total number of crimes to large areas of aggregation (e.g., a tract or a county) may 65

78 mask individual crime-ridden areas. Scale effects include the tendency within a system of areal units for different statistical findings to be obtained from the same dataset (Openshaw, 1983; Fotheringham and Wong, 1991; Wrigley, Holt, Steel, Tranmer, 1996). 4 Criminologists have also faced limitations related to their methodology and analytic techniques. While some attention has been given to the physical characteristics, or the layout/composition, of neighborhoods, social scientists have primarily relied on the measures and effects of neighborhood social characteristics. Indeed, criminological theorists have given primacy to social characteristics. These characteristics have included economic/poverty rates, racial heterogeneity, age composition, and gender composition, amongst others. The importance of individual characteristics and other social ecological phenomena has been demonstrated when studying the rates of crime and deviance, however common sense suggests that differences in a neighborhood s physical characteristics such as land-use patterns are also important when studying neighborhood crime rates. Several researchers have discussed the importance of physical ecology when studying various social phenomenon (Shaw and McKay, 1942; Newman, 1972; Taylor and Gottfredson, 1986; Stark, 1987; Raudenbush and Sampson, 1999), yet these same researchers have faced many challenges and difficulties when attempting to measure and define physical characteristics of neighborhoods. Raudenbush and Sampson (1999) suggest one solution to determining the differences in 4 Scale effects are one component of the Modifiable Areal Unit Problem (MAUP) that is commonly discussed in Geography (Openshaw, 1983). The other effect is the zoning effect, which is found when results are different using the same data set due to the manner in which units are grouped at a given scale, and these differences are not strictly due to size differences in the units (Wrigley, Holt, Steel, Tranmer, 1996). 66

79 neighborhood physical ecology. These researchers use direct observation to collect evidence on the physical conditions of a neighborhood. This process is both time-consuming and costly. Few researchers have either the manpower or the financial means to perform such a study. Prior to this work by Raudenbush and Sampson, Felson (1987) examined the frequencies of property crimes reported to police agencies across the state of Illinois by the type of physical location at which the crime occurred. Researchers have also examined the relationships between certain types of places, such as public housing units or public transit stations, and the occurrence of crime at or near these places to see if there is a distance decay effect (Fagan and Davies, 2000; Block and Block, 2000). Yet other researchers, studying victimization patterns in Seattle, have relied upon survey measures of physical ecology (e.g., Rountree, Land, and Miethe, 1994). Specifically, this series of papers has asked survey respondents about whether or not particular land-uses (e.g., parks, bus stops, schools, etc.) could be found within 3-4 blocks of their homes. Each of these methods has associated strengths, but each also has its own limitations. Related to the difficulties associated with measuring neighborhood land-use patterns, the predominance of social characteristics in crime theory and research may also be symptomatic of the convenience of finding testable data. While social characteristics of neighborhoods are relatively accessible through census and survey measures, measures of physical ecology are much more difficult to tap. Land-use patterns of a neighborhood are the composite of many structural characteristics. For example, does the housing market consist of single-family residences or high-rise multi-family dwellings? Is there a park in the neighborhood? Is commerce or industry represented in the neighborhood bars, shopping areas, factories? As noted above, traditional criminological studies have focused on the effects of social activity patterns on crime and victimization rates. However, some researchers have suggested that 67

80 neighborhoods and places could be criminogenic in and of themselves (Stark, 1987; Sherman et al., 1989; Raudenbush and Sampson, 1999). That is, theorists have discussed and studies have examined whether the physical characteristics in neighborhoods increase crime. For example, Stark proposed that neighborhoods that share their residential area with industry and commerce have higher crime rates. Criminologists are also faced with many challenges when defining appropriate units of analysis, or neighborhood sizes. Because this issue is complicated by the availability of data at each level of aggregation, researchers traditionally relied upon large census-defined geographic areas, such as tracts, as their definitions of neighborhoods, even though these areas can hold up to 8,000 residents within their boundaries. However, newer and less financially draining data collection techniques and resources have become available. Increasingly, police departments publish crime data at the 100-block street address level. Researchers can aggregate this information to fit their own definition of neighborhood, rather than a previously assigned area. City governments can provide tax parcel data that describes land-use at the level of each separate land parcel, such as for each housing unit or building (Smith, Frazee, and Davison, 2000). This information can also be aggregated to a larger definition of neighborhood. With these new formats, researchers have the opportunity to examine the effects of neighborhood on crime rates. This current study extends prior studies on the neighborhood-crime relationship. First, this paper examined multiple land-uses simultaneously, following the examples of Smith, Frazee, and Davison (2000) and Peterson, Krivo, and Harris (2000), which both went beyond prior works focusing on how social characteristics related to crime. Second, using data that identifies the current land-use of individual land parcels, this research explores how the presence of various patterns of neighborhood land-use affect crime rates. The current research adds to prior 68

81 research by creating a measure of mixed land-use and by testing for the mediational effects of neighborhood land-use. Finally, the current research builds on prior research by running spatial regression models. Geographically Referenced Data and Spatial Autocorrelation Researchers must consider that there are some interesting idiosyncrasies when working with spatial or geographically referenced locations. This type of data often does not satisfy the requirements of independence and homogeneity required in classical statistics (Anselin, 1998, p. 5). Tobler (1979), developer of the First Law of Geography, states that all things are related; however, near things are more related than things that are distant from each other. Crime data is spatial, and such data often violates the requirements of independence and homogeneity required in classical statistics (Anselin, 1998, p.5). Geographically referenced data is often linked by locational similarity and value similarity, a condition known as spatial autocorrelation (Anselin, 1988; 1990; 1992; 1998; Ding and Fotheringham, 1992). Spatial autocorrelation can be tested for using tests, such as the Moran s I statistic (Moran, 1950). The Moran s I statistic is one of the most commonly used univariate statistic designed to test the null hypothesis of the absence of spatial clustering (Cliff and Ord, 1973; 1981; Baller, Anselin, Messner, Deane, and Hawkins, 2001). That is, Moran s I measures the deviation from spatial randomness, or the concentration of an attribute over space, and is calculated as follows: I = Σ i Σ j w ij (y i - µ) (y j - µ) / Σ i (y i - µ) 2 where w ij are elements of a row-standardized spatial weights matrix, y is the robbery rate, and µ is the average robbery rate in the sample (Anselin, Cohen, Cook, Gorr, and Tita, 2002; Cliff and 69

82 Ord, 1973; 1981; Upton and Fingleton, 1985; Ding and Fotheringham, 1992). Moran s I is similar to a Pearson correlation coefficient and is scaled to be less than one in absolute value. If locations are close together and tend to be similar in attributes, then this will be reflected with a positive spatial autocorrelation score (contagion, spillover, externalities) (See Figure 9). Conversely, if locations are proximate but instead have very dissimilar values, then this is reflected as a negative spatial autocorrelation score (competition, revulsion) (See Figure 10). Larger absolute values indicate higher levels of spatial autocorrelation in the data. When values are independent of their location, then zero autocorrelation is present (See Figure 11). (Ding and Fotheringham, 1992; Baller et al., 2001) Figure 9. Example of Positive Autocorrelation, High Attribute Values Clustered Together (Evidence of Contagion or Spillover) 70

83 Figure 10. Example of Negative Autocorrelation, Values Are Evenly Distributed (Evidence of Competition or Revulsion) Figure 11. Example of Zero Autocorrelation, Values Are Independent of Their Location (Evidence of Randomness Values Assigned by the Flip of a Coin) 71

84 The spatial weights matrix is a key concept when thinking about spatial autocorrelation. This is a square matrix with dimensions equal to the number of areal units (e.g., tracts or blockgroups) in the data. The diagonal elements of the matrix are zeroes. Elements are also equal to zero if they are not spatial neighbors, with contiguous elements, or neighbors, being non-zero. Spatial weights matrices can be based on: contiguity, distance, or social distance. (See Anselin, Cohen, Cook, Gorr, and Tita, 2002; Cliff & Ord, 1981; Upton and Fingleton, 1985; and Anselin 1988) If spatial autocorrelation is detected when using Moran s I values, then researchers must find a way to compensate for this problem. Moreover, alternative regression techniques have been developed depending on the type of autocorrelation that is present in the data. One such example of routines designed to deal with issues of spatial autocorrelation is the SpaceStat program developed by Luc Anselin (1988). Further, ArcView GIS technologies have been integrated recently with the SpaceStat statistic program. 5 SpaceStat (Anselin, 1988) allows researchers to form matrices that describe the levels of variables in geographically adjacent areas or areas that are within certain distances of the neighborhood of study. There are three different join count statistics that can be used to select areas around your neighborhood to create the joint count statistic s value. This value is in simple terms the average of the variable of interest s value in the selected surrounding areas. The three join count statistics are rook (edge-to-edge), bishop (vertex-to-vertex), and queen (edge-toedge and vertex-to-vertex) (Robinson, 1998). These statistics are most easily understood in the context of a chessboard and the movement of the pieces that are given as names to the join count 5 This interface is not always speedy, and has been criticized for not being user-friendly (Armstrong, 2000; Bailey, 1994; Williamson, Ross, McLafferty, & Goldsmith, 2000; etc.). 72

85 statistic. To use the chessboard analogy to illustrate the notion of spatial autocorrelation if the values of the variable of interest were independent then you would expect your map to look like a chessboard with alternating colors. Spatial autocorrelation implies that values that are black on the chessboard are likely to be closer to one another. Simply speaking, these events are clustered. The current research will employ the Queen method. (See Figure 12 for example.) Figure 12. Example of a Queen Join Count Statistic GIS and the Current Study As noted above, this project relied on the use of GIS from the initial stages of data preparation and creation through the development of statistical models. The accuracy of the crime data preparation was an important consideration. As such, a description of the address- 73

86 matching process was provided below and reports of the level of address matching success were provided in tabular format as well. In addition, a description of initial exploratory analyses were provided. Finally, the spatial regression models were described. Data preparation. In order to accomplish the goals of this research project, several steps had to be taken to prepare the data for use in a regression model. First, the crime-incident data was first address-matched to the appropriate tax parcel or centerline file using the GIS programs ArcView 3.2a. Address matching was performed interactively. That is, addresses were assigned to a location at the discretion of the GIS analyst, and will not be automatically assigned to the programs initial match of choice. Final matching rates varied by crime type and by police department. The crime data were assigned to the appropriate tax parcel location. If the event occurred at an intersection, the crime was addressed-matched to a street centerline file. Both Lincoln, NE and Columbus, OH provide address number, street name, and zip code identification numbers in their data sets. The initial overall matching rates in Lincoln were approximately 97%, as were the rates for Columbus. San Antonio, TX crime data consisted of information downloaded from SAPD s website. That said, this study was limited by the unavailability of zip code information in the data. However, the data were provided to the public by police district. SAPD had six districts and the crimes were address-matched to each district independently, thus eliminating the possibility of a crime event being placed in the wrong police district (See Figure 13 for a map of the districts). Preliminary matches in the San Antonio Police Districts ranged from 82-97%, with robbery incidents matching at an 97% success rate. Because the published addresses did not provide additional information to which a location could be attached, such as a zip code, the rates were unable to be further improved. 74

87 Figure 13: Map of San Antonio Police Department s Six Patrol Districts with Substation Locations Noted (San Antonio Police Department, 2001) 75

88 Table 7 displays percent of successfully address-matched robbery events by the city they are in. Table 7. Percent of Address-Matching Success for Robbery Events by City of Occurrence City Robbery Events San Antonio, TX Lincoln, NE Columbus, OH Exploratory analyses. Because geographically referenced data is often plagued by locational similarity and value similarity (i.e., spatial autocorrelation), researchers must determine whether their own data are problematic (Anselin, 1988, 1990, 1992, 1995, 1996, 1998, 2001; Anselin & Kelejian, 1997; Ding and Fotheringham, 1992; Martin 1991; 1996; Lee and Wong, 2001; Atkinson and Martin, 2000; Fotheringham and Rogerson, 1994; Maguire, Goodchild, and Rhind, 1991). The Moran s I statistic, which measures the clustering of events across space, is an appropriate test for the presence of spatial autocorrelation (Cliff and Ord, 1973; 1981). The data from the three research sites will be analyzed using the Moran s I test. Spatial regression. The data was first analyzed using Ordinary Least Squares (OLS) techniques to test the theoretical model. The variables were entered progressively across three models. The first model was simply the dependent variable (crime rates) regressed on neighborhood social characteristics (disadvantage, residential instability, population density, percent male, and percent 18-24). The second model added statistical controls for the direct effects of neighborhood land-uses (e.g., distance from center city, mixed land-use, percents of types of land-use, and the various commercial and public establishments). The third model added the interactions hypothesized to occur between land-uses and neighborhood social 76

89 characteristics. Diagnostic tests from these OLS models indicated whether these analyses required the use of spatial error models or spatial lag models. The diagnostic tests determined the nature of the problem caused by the spatial dependence was it a nuisance, meaning that one needed to increase sample size or incorporate the spatial autocorrelation in a regression error term, or was it substantive, meaning that the structure of the spatial dependence needed to be incorporated as an explanatory variable in the model (Anselin, 1988, 1990, 1992, 1995, 1998). Spatial dependence in the form of a nuisance was indicative of omitted covariates that were spatially correlated, and if this condition existed then researchers abilities to make inferences would be impacted (Baller, et al., 2001). However, if spatial dependence existed in the form of spatial effects, then this suggested a possible diffusion process events in one place predicted and increased likelihood of similar events in neighboring places, net of the effect of structural covariates (Baller, et al., 2001). The key test for spatial dependency in the dataset was the LaGrange Multiplier tests. This test was based on testing the residuals of an OLS regression. The SpaceStat statistical program provided two LaGrange Multiplier (LM) tests the LM Error and the LM Lag tests. The LaGrange Multiplier Error was calculated as follows: LM error = [e We/(e e/n)] 2 / [tr(w 2 + W W)] Anselin and Kelejian (1997) demonstrated that the LM Error tests was asymptotically equivalent 77

90 to the square of Moran s I, and it has an asymptotic χ 2 distribution. 6 The LM Lag was calculated as follows: LM lag = [e Wy/(e e/n)] 2 / D where D = [(WXβ) (I X(X X) -1 X )(WXβ) / σ 2 ] + tr(w 2 + W W)]. The LM Lag test also has an asymptotic χ 2 distribution (Anselin, forthcoming). The LM test that is the most statistically significant determines which Spatial Regression Alternative is the proper alternative to OLS Modeling (Anselin et al, 2002) 7. If diagnostics revealed that the problem was a nuisance (the LaGrange Multiplier Error test is the larger of the two diagnostics and is significant), a spatial error model would be utilized. Anselin (1992) illustrated the standard regression specification model as follows, with a spatial autoregressive error term: Y = Xβ + ε ε = λw ε + ξ 6 The only difference between these tests was a scaling factor (Anselin and Kelejian, 1997; Anselin, 2001). 7 For reviews of the LaGrange Multiplier test see Anselin and Florax (1995) as well as Anselin, Bera, Florax, and Yoon (1996). 78

91 where y is a N by 1 vector of observations on the dependent variable, X is a N by K matrix of observations on the explanatory variables, β is a K by 1 vector of regression coefficients, ε is a N by 1 vector of error terms, W ε is a spatial lag for the errors, λ is the auto regressive coefficient and ξ is a well-behaved error, with mean 0 and variance matrix σ 2 I (p.208). Under the conditions of spatial dependence, the error term becomes: E[εε ] = Ω = σ 2 [(I - λw) (I - λw)] -1 Because λ was not known, a full maximum likelihood estimation process was necessary (Anselin, 1992, pp ). Anselin (1992) noted that the consequence for ignoring dependence amongst the errors using geographic data is that the OLS estimates become inefficient. However, if diagnostics revealed that the problem was more likely substantive (the LM Lag is the larger of the two diagnostics and is significant), a spatial lag model would be utilized. Anselin (1992) illustrated the mixed regressive spatial autoregressive model to be specified as follows, with a spatially lagged dependent variable term as one of the explanatory variables: Y = DW y Xβ + ε where y is a N by 1 vector of observations on the dependent variable, W y is a N by 1 vector of spatial lags of the dependent variable, D is a spatial autoregressive coefficient, X is a N by K matrix of observations on the (exogenous) explanatory variables with associated a K by 1 vector 79

92 of regression coefficients β, and ε is a N by 1 vector of normally distributed random error terms, with means 0 and constance (homoskedastic variances σ 2 ). (p ). The spatial lag term captures the average rate of crime in neighboring census tracts or blockgroups, and can be interpreted as the extent the crime rate in a blockgroup can be explained by the average of its neighbors crime rates (Anselin et al., 2002). When the autoregressive parameter is known, the model then becomes: y - βw y = Xβ + ε If a normal error assumption can be made then a full maximum likelihood estimation process will be performed (Anselin, 1992). Anselin (1992) noted that the consequence for ignoring dependence amongst the errors using geographic data is that the OLS estimates will be biased and inferences based on an OLS model will be incorrect. In addition, he suggests that failure to include the W y coefficient would be similar to the failure to include a significant explanatory variable in the model (See also Anselin et al., 2002). Analytic Strategy As noted above, variables were first analyzed in OLS. The LaGrange Multiplier tests were used to determine whether spatial regression was an appropriate second step. The first model consisted of the social characteristics of the neighborhood, that were commonly used in social disorganization research: concentrated disadvantage, concentrated immigration, percent male, percent 18-24, residential instability, population density, and distance from the center of the city. The second and third models added the land-use measures and interactions respectively. (See Table 8). 80

93 Table 8. Analytic Strategy Variable Name Model 1 Model 2 Model 3 Concentrated Disadvantage X X X Concentrated Immigration X X X % Ages X X X % Male X X X Residential Instability X X X Population Density X X X Distance Central City X X X Index of Mixed-Use X X % Multiple Family Residential X X % Industrial Use X X % Commercial Use X X % Vacant Land X X Schools X X Convenience Stores/Gas X X Hospitals X X Hotels X X Banks X X Recreation Centers X X Churches X X Malls X X Libraries X X Mixed * Density X Mixed * Disadvantage X Mixed * Instability X Rec. Centers * Disadvantage X Banks * Disadvantage X Libraries * Disadvantage X Chapter 4 Summary As described in Chapter 4, communities and crime researchers have faced many limitations that can be at least partially addressed using GIS, spatial autocorrelation, and spatial regression techniques. As noted above, GIS was used from the inception through the conclusion of this research. Specifically, the chapter introduced and explained the nature of geographically referenced data and the methodological considerations when attempting to compensate for 81

94 spatial autocorrelation. In addition, the chapter discussed the exploratory and confirmatory statistical techniques used in the current research. 82

95 CHAPTER FIVE RESULTS FROM SAN ANTONIO, TX Introduction Chapter 5 begins with the descriptive statistics and a correlation matrix for variables used in the analysis of the San Antonio data. Next, I present two types of exploratory analyses used to test for the presence of spatial autocorrelation in the data. This section is followed by the presentation of results from the multivariate analyses. Finally, the chapter concludes with a brief summary of the findings. Descriptive Statistics Descriptive statistics for the dependent and independent variables are presented in Table 9. The logged rate of robberies per 10,000 people is the dependent variable. 8 The descriptive statistics in Table 9 demonstrate that many blockgroups did not experience any robberies in In fact, 315 of the 806 blockgroups in San Antonio did not have a single robbery reported to the police in 2000 (approximately 39.1 percent). The pre-transformation mean rate of robberies is per 10,000 people, however one blockgroup had a robbery rate of more than 676 robberies per 10,000 people. Clearly there is tremendous variation across neighborhoods making the study of the differences between high and low crime neighborhoods important. 8 The dependent variable was transformed to reduce the amount of positive skew that was present in the original form of the variable. Thus, the natural log of the rate of robbery robberies per 10,000 people is the dependent variable for the San Antonio analyses. 83

96 Table 9. Description of Variables Used in Regression Models (San Antonio, N = 806) Variable Name Min. Max. Mean Std. Dev. Dependent Variable A 1) Logged Robbery Rates Independent Variables 2) Concentrated Disadvantage ) Concentrated Immigration ) % Ages (LN) ) % Male ) Residential Instability ) Population Density ) Distance Central City ) Index of Mixed-Use ) % Multiple Family Residential (LN) ) % Industrial Use (LN) ) % Commercial Use ) % Vacant Land ) Schools ) Convenience Stores/Gas ) Hospitals ) Hotels ) Banks ) Recreation Centers ) Churches ) Malls ) Libraries A = DV calculated as the transformed (natural log) rate of robberies per 10,000 people Figure 13 illustrates the logged robbery rates across the city using quartile divisions. Supporting the descriptive statistics, the map demonstrates that many blockgroups did not have a single reported robbery in The blockgroups that are robbery-free are depicted in white. In contrast, the highest quartile of robbery rates is depicted in black. The Northwest and North Central suburbs appear to have relatively low rates of robbery (most are illustrated in white and the lighter of the two shades of grey), unlike some of the more central and downtown regions of 84

97 the city. It is also noteworthy that many high crime neighborhoods are adjacent to low crime neighborhoods. Figure 13. Robbery Rates in San Antonio, TX A histogram of the dependent variable is presented in Figure 14. The histogram was created using the Dynamic ESDA extension within ArcView 3.2a. The far left column in the histogram represents the 315 blockgroups that did not have a robbery reported to the police in

98 Figure 14. Ten Category Histogram Logged Robbery Rates in San Antonio, TX LOGGED ROBBERY RATES 86

99 Zero-order correlations are presented in Table 10 (a key appears below the table). The logged robbery rates are most highly correlated with population density (r = -0.28) and the percent of land dedicated to commercial use (r = 0.26). However, the majority of the variables are not highly correlated. There is no evidence suggesting that multicollinearity will be a problem in the multivariate analyses. The strongest correlation amongst the independent variables is between residential instability and the percent of land dedicated to multiple family residential use (r = 0.55). Multiple family housing units are often rental properties, and renters are likely to move. The correlations between the percent of land that is vacant and the distance from the center of the city (r = 0.47), between the percent of land dedicated to commercial land-use and the degree to which land-uses are intermixed within a blockgroup (r = 0.47), between residential instability and the presence of recreation centers in a blockgroup (r = 0.46), and between the population density of a blockgroup and concentrated disadvantage (r = 0.45) were also high. 87

100 Table 10. Correlation Matrix of Independent Variables within Blockgroups (N = 806) ) Logged Robbery Rates (ln) 9) Index of Mixed-Use 17) Hotels 2) Concentrated Disadvantage 10) % Multiple Family Res. (ln) 18) Banks 3) Concentrated Immigration 11) % Industrial Use (ln) 19) Recreation Centers 4) % Ages (ln) 12) % Commercial Use 20) Churches 5) % Male 13) % Vacant Land 21) Malls 6) Residential Instability 14) Schools 22) Libraries 7) Population Density 15) Convenience Stores/Gas 8) Distance Central City 16) Hospitals 88

101 Analyses for Autocorrelation As noted in Chapter 4, crime data are geographically referenced (located in space, as on a map), and they often violate the assumptions of independence and homogeneity required for Ordinary Least Squares analyses (Anselin, 1998). The Moran s I statistic and the LaGrange Multiplier Tests are two methods used to test for spatial autocorrelation, or the concentration of an attribute. In the case of robbery rates in San Antonio, exploratory analyses indicate that locations that are physically close together also tend to be similar in value. Figure 14 presents a graphic of a Moran s scatterplot, which illustrates the spatial autocorrelation existing in the San Antonio data. The X-axis is the robbery rates in San Antonio neighborhoods. The Y-axis is the Queen s weighted spatial lag of the robbery variable (the average of the surrounding blockgroups crime rates). When both of these values are high (i.e., when a high crime blockgroup is surrounded by other neighborhoods that have high crime rates) then the blockgroup would be represented with a point in the upper right quadrant of the Moran s scatterplot (Figure 14). When a blockgroup has low crime rates and is surrounded by blockgroups that also have low crime rates, then the blockgroup would be represented with a point in the lower left quadrant of the scatterplot. However, when a high crime blockgroup is adjacent to low crime neighborhoods, the neighborhood would be represented in the lower right quadrant of the scatterplot. Further, low crime blockgroups surrounded by high crime blockgroups would be represented in the upper left quadrant of the scatterplot. The closer in value blockgroups are to each other, the closer their points will be on the scatterplot. 89

102 Figure 15. Moran s I Value for Logged Robbery Rates in San Antonio, TX LOGGED ROBBERY RATES In order to calculate the Moran s I values in ArcView 3.2a, an extension entitled Dynamic ESDA must be used. One benefit of using Dynamic ESDA is that it is interactive. If one highlights a quadrant in the figure, then the neighborhoods represented by the points are highlighted in the map. For example, Figure 15 is a map of blockgroups with high crime rates that are surrounded by other neighborhood with high crime rates (the upper right quadrant of the scatterplot). It appears that a robbery pattern may exist in the central and southeastern portions of 90

103 San Antonio, as is evidenced by the dark patterns within these areas of the city. However, there are pockets of neighborhoods throughout the city that suffer from high robbery rates and are adjacent to similarly victimized neighborhoods. 291 of the 806 neighborhoods met the criteria to be in the upper right quadrant of the Moran scatterplot. The Moran s value of almost 0.12 was significant at the p < level, thus confirming that these neighborhoods are closer to each other than would be expected by chance. 9 If there were no autocorrelation present in the map, then the dark and light blockgroups would be distributed randomly. The visual proximity of many of the dark blockgroups on the map is supported by the Moran s I value being significant. In order to test whether the autocorrelation in the data was significant enough to require the use of spatial models rather than OLS models, the LaGrange Multiplier (LM) tests were utilized. The LM Lag tests suggested that a Spatial Lag model was the appropriate form of analysis for each of the three regression models. As the spatial lag models were deemed more accurate, the table for the OLS models was included only as an appendix (See Table 19). It must be noted that, while most of the Ordinary Least Squares results were similar in the direction and significance of the effects found in the Spatial Lag models, the percent of individuals aged 18 to 24 and the interaction between mixed land-use and residential instability were not significant when the models were run using spatial regression techniques. 9 Moran s I was calculated using Dynamic ESDA as well as by using the global Moran s I routine in SpaceStat (Anselin, 1988). 91

104 Figure 16. High Robbery Areas in San Antonio, TX Multivariate Analyses The multivariate results are presented in Table 11. The first multivariate model presents the effects of census variables: concentrated disadvantage, concentrated immigration, percent 18-24, percent male, residential instability, population density, and the distance from the center of the city. These initial variables are commonly used in crime research, particularly that research investigating the presence of neighborhood effects (For examples see, Sampson, 1997; Sampson, Morenoff, & Earls, 1999; Sampson & Wilson, 1995; Shaw, 1929; Shaw & McKay, 1942; Cohen & Felson, 1979; Rountree, Land, & Miethe, 1994, etc.). Following the precedents of prior ecological theory and research (e.g., Shaw and McKay, 1942; Sampson & Raudenbush, 1999; 92

105 Sherman, Gartin, & Buerger, 1989; Stark, 1987; Peterson, Krivo, & Harris, 2000; Smith, Frazee, and Davison, 2000) the second multivariate model adds a variety of land-use measures to the regression: the index of mixed land-use; the percent of land dedicated to multiple family residential land-use; the percent of industrial land-use; the percent of commercial land-use; the percent of vacant land; as well as the presence of schools, convenience stores/gas stations, hospitals, hotels, banks, recreation centers, churches, malls, and libraries. The third multivariate model builds on the second model by adding the following interaction terms: mixed land-use and instability, mixed land-use and population density, mixed land-use and concentrated disadvantage, recreation centers in disadvantaged neighborhoods, libraries in disadvantaged neighborhoods, as well as banks in disadvantaged neighborhoods. These interactions between neighborhood social and physical characteristics also are intended to further examine variables suggested by prior theory and research (e.g., Shaw and McKay, 1942; Sampson & Raudenbush, 1999; Sherman, Gartin, & Buerger, 1989; Stark, 1987; Peterson, Krivo, & Harris, 2000; Smith, Frazee, and Davison, 2000). The hypotheses of this project addressed the impact of neighborhood land-use patterns on robbery rates. Specifically, the following analyses sought to determine which land-uses had a direct effect on neighborhood robbery rates, and whether land-uses mediated the effects of social characteristics (i.e., disadvantage) on crime. It was expected that some land-uses (e.g., commerce and industry) and some types of establishments (i.e., convenience stores) would serve as attractors and generators for crime. Several variables were significant in Spatial Model 1: the spatial lag of robbery (+), concentrated disadvantage (+), residential instability (+), the percent of the population that was between the ages of 18 and 24 (-), population density (-), and the distance from the center of the city (-). 93

106 First, the spatial lag of the dependent variable was related to higher robbery rates. This can be interpreted as the spatial flow, or a spillover, of crime. That is, blockgroups with high robbery rates are likely to be adjacent to other blockgroups with similarly high robbery rates. Second, as expected, concentrated economic disadvantage was related to higher robbery rates. While a number of theories address the relationship between poverty and high crime rates, this finding can be interpreted in terms of routine activity theory. For example, extreme poverty may increase the number of motivated offenders in a neighborhood. Third, the concentrated immigration index was related to higher robbery rates in neighborhoods. Thus, blockgroups with higher levels of this index were more likely to report higher levels of robbery. Generally these neighborhoods are more likely to be highly populated and in the center of the city both of which are more likely to be near motivated offenders. 94

107 Table 11. Spatial Regression Results for Robbery Rates in San Antonio (N = 806) Variable Name SAR Model 1 SAR Model 2 SAR Model 3 Lagged Dependent Variable 0.17**** 0.14*** 0.14*** Constant Conc. Disadvantage 0.56**** 0.38**** 0.36 Conc. Immigration * 0.20 % Ages ** -0.95** -0.91** % Male Residential Instability 0.41**** * Population Density -0.00**** Distance Central City -0.09*** Index of Mixed-Use % Multiple Family Res % Industrial Use % Commercial Use % Vacant Land Schools Convenience Stores/Gas 0.67**** 0.65**** Hospitals Hotels 0.25* 0.25* Banks Recreation Centers Churches Malls 0.41**** 0.41**** Libraries Mixed*Instability Mixed*Density 0.00 Mixed*Disadvantage 0.32 Recreation*Disadvantage 0.15 Library*Disadvantage Bank*Disadvantage Pseudo R **** p <.001 *** p <.01 **p <.05 *p<.10 95

108 Fourth, the percent of individuals between the ages of 18 and 24 was related to lower robbery rates in neighborhoods. This finding can be interpreted in terms of routine activity theory as well. For example, individuals between these ages may be more likely, or more physically able, to act as capable guardians for themselves and others. Further, these individuals may be less suitable targets as they may not carry as much cash or wear as valuable of jewelry as their older peers. Fifth, residential instability was related to higher robbery rates. In neighborhoods where residents are on the move, capable guardianship may be more difficult to provide. High levels of residential turnover may make it more difficult for residents to distinguish between strangers and neighbors, thus providing a level of anonymity for motivated offenders. However, social disorganization theorists may interpret this finding in terms of residential mobility leading to the disruption of a community s social organization and community solidarity. Social disorganizationists believe that tenure (i.e., length of residence) is crucial to the weakening of bonds between individuals or the lessening of community sentiments (Kasarda & Janowitz, 1974), thereby impacting a neighborhood s ability to exert informal social control on its inhabitants. Sixth, contrary to findings from city-level research (Decker, Shichor, and O Brien, 1982; Felson, 2002), higher population density was related to lower robbery rates. One can think of population density as putting more eyes on the street, thus allowing neighborhood residents to serve as guardians, at the local level, for their neighbors by simply being present (e.g., being visual or auditory witnesses of a robbery). However, it must be noted that the size of this coefficient was very small (-0.003). Finally, the greater the distance from the center of the city the lower the robbery rates. 96

109 This may be related to the complex issues surrounding target suitability and opportunity. As the distance from the center of the city increases, neighborhoods are less likely to experience high robbery rates. In suburban areas, robbery targets (both places such as convenience stores and banks, as well as the people who may be visiting these places to purchase items or to get cash from the ATM provided by these places) are more dispersed across geographic space. Beyond the types of density that are controlled in the model by including the residential population density, the locations of buildings and individuals on the street are more likely to be more spread out, or less dense. Thus, the journey to crime may become longer between the types of suitable targets that robbers seek. On the other hand, the dependent variable was not related to the percent of the population that was male in the blockgroups. However, it must be noted that this study is focusing strictly on where robberies occur, rather than where offenders reside. Thus, although young males are more likely to be robbers, the locations where high numbers of youthful males live are not necessarily related to where robberies occur. Several land-use variables were significant in Spatial Model 2: the presence of convenience stores/gas stations (+), the presence of malls (+), and the presence of hotels (+) were directly related to robbery rates. As noted in prior chapters, some land-uses may bring nonresidents to a neighborhood. Outsiders can serve as both motivated offenders and suitable targets. As such, establishments (such as convenience stores, etc.) were predicted to increase crime in neighborhoods where they are located. As expected, the presence of convenience stores/gas stations, malls, and hotels were associated with higher rates of robberies. The relationship between the presence of hotels and blockgroup robbery rates was only a trend (p <.10). 97

110 The introduction of neighborhood land-uses mediated some of the effects of the sociodemographic variables. One of the key findings was that the addition of the land-use variables mediated some of the census measures. For example, land-use mediated the effect of the population density by 33.3%. 10 Thus, approximately one-tenth of the effect of the percent of individuals between the ages of 18 and 24 on robbery rates was actually due to the land-use patterns in San Antonio neighborhoods. In addition, residential instability was no longer related to robbery rates after the introduction of neighborhood land-use patterns. The coefficient goes from 0.41 (p <.05) to 0.17 and non-significance. These findings suggest that land-uses may be responsible for the higher rates of robbery in neighborhoods experiencing high levels of residential turn-over. Further, the distance from downtown was no longer significant in the second model. Its coefficient goes from (p <.05) to and non-significance. These findings suggest that land-uses, rather than location (e.g., downtown versus suburb), may be responsible for the higher rates of robbery in neighborhoods that are closer to downtown. It is unclear which of the variables mediated these relationships. However, of the landuse variables that are correlated with robbery rates, the highest include the index of mixed landuse, the percent of land dedicated to industrial use, and the percent of land dedicated to commercial use. However, none of these variables were significant predictors of robbery rates. Finally, contrary to the hypotheses of this study, several land-uses were not related to robbery rates. The variables designed to test the percent of land-use dedicated to multiple-family 10 Partial mediation was tested by comparing the coefficients from the variables across the two models. In this case the coefficient for population density had been in Model 1 and in Model 2 in the Spatial Regression results from San Antonio {[ (-0.002)] / }. 98

111 housing, to industrial land-use, to commercial land-use, and to vacant lots were not significant. In addition, counter to expectations, the index of mixed land-use was not related to blockgroup robbery rates. Several neighborhood establishments were not related to robbery rates the presence of schools, banks, hospitals, recreation centers, libraries, and churches had no effect on robbery rates. However, in additional analyses not presented in this chapter, mixed land-use was a significant predictor of robbery rates, but only when 5 types of land-uses were entered into the entropy equation: single-family residential land-use, multiple-family residential land-use, commercial land-use, industrial land-use, and vacant land. When the two forms of residential land-use were combined, the variable no longer predicted robbery rates in San Antonio. Thus, the combination of the two different types of residential use, single-family use (often home owners) and multiple-family use (often renters), eliminated this effect. It was unclear why the combination of these two types of housing units would make a difference in the significance of this variable. In Model 3, land-uses were expected to interact with a number of neighborhood social characteristics. In particular, robbery rates were expected to be higher in densely populated neighborhoods with mixed land-uses. Further, mixed-land-use was expected to interact with residential instability, a combination hypothesized to simultaneously decrease a neighborhood's guardianship capacity and increase the numbers of suitable targets. Following Peterson et al. (2000), two interactions were investigated to determine if they would be related to lower robbery rates in neighborhoods. Specifically, the interaction between recreation centers and disadvantage and the interaction between libraries and disadvantage were expected to be related to lower robbery rates. These two land-uses have been suggested as able to act as stabilizing forces in 99

112 neighborhoods and their presence provides entertainment (recreation centers) and stable employment (libraries) in their neighborhood. However, the presence of banks in a disadvantaged neighborhood may provide opportunities for robbery of both the institution and of the institution s patrons. Thus, it was expected that banks in disadvantaged neighborhoods would be related to higher rates of neighborhood robbery. Of the originally predicted six interaction terms, none of them were significant. Chapter 5 Summary Chapter 5 has examined the possible relationships between land-use and robbery in San Antonio. Findings suggested that particular land-use patterns may be criminogenic. As predicted, neighborhoods where more commercial establishments were present, such as convenience stores/gas stations, malls, and hotels (p <.10), were more likely to have higher robbery rates. Presumably, these particular land-uses attract both motivated offenders seeking cash-carrying victims (i.e., suitable targets) or, as in the case of convenience stores/gas stations, this type of land-uses itself may be a suitable target. Further, these land-use patterns explained why residentially instable, or more highly transient, neighborhoods had higher crime rates than more stable blockgroups in San Antonio. In addition, these land-use patterns explained why inner-city, or more centrally located, neighborhoods had higher crime rates than more suburban blockgroups in San Antonio. Several predictions were not confirmed. A variety of land-use patterns and establishments were not significant predictors of robbery. For example, one of the key hypotheses of this project was that mixed land-use was associated with higher levels of crime. Mixed land-use was not a predictor of higher neighborhood robbery rates in San Antonio, except if measured with the types of residential land-use differentiated (single-family versus multiple-family dwellings). 100

113 In addition the following land-uses were not related to robbery rates: the percent of land dedicated to multiple-family use, the percent of land dedicated to industrial use, the percent of land dedicated to commercial use, the percent of land that was vacant, the presence of schools, the presence of recreation centers, the presence of churches, and the presence of libraries. Further, the neighborhood institutions that were expected to have an effect in disadvantaged neighborhoods (recreation centers and libraries) were not related to robbery rates. One finding was unexpected. While no relationship had been specifically predicted between population density and robbery rates, previous city-level research had suggested that population density would increase robbery rates. However, there is an ongoing debate in the criminological field as to whether population density provides more targets and offenders, raising crime rates, or whether it provides more guardianship by placing more eyes on the streets, lowering crime rates. The results of the current research suggest that densely populated neighborhoods, as measured by population density increases guardianship. 101

114 CHAPTER SIX RESULTS FROM LINCOLN, NE Introduction Chapter 6 begins with the descriptive statistics and the correlation matrix for variables used in the analysis of the Lincoln data. Next, I present the exploratory analyses used to test for the presence of spatial autocorrelation in the data. This section is followed by the presentation of results from the multivariate analyses. Finally, the chapter concludes with a brief summary of the findings. Descriptive Statistics Descriptive statistics for the dependent and independent variables are presented in Table 14. The logged rate of robberies per 10,000 people is the dependent variable. 11 The descriptive statistics in Table 14 demonstrate that many blockgroups did not experience any robberies in In fact, 98 of the 174 blockgroups in Lincoln did not have a single robbery reported to the police in 2000 (approximately 57.5% of all blockgroups). The mean rate of robberies is slightly over 18 per 10,000 people, however one blockgroup experienced robbery rates of over 147 per 10,000 people. As was seen in San Antonio, there is a noticeable amount of variation in robbery rates across the Lincoln blockgroups. However, the Lincoln robbery rates are generally lower than those in San Antonio. Supporting the descriptive statistics, Figure 17 illustrates robbery rates across the city using quartile divisions. The lowest quartile is depicted in light grey, and this quartile illustrates 11 The dependent variable was transformed to reduce the amount of positive skew that was present in the original form of the variable. Thus, the natural log of the rate of robbery robberies per 10,000 people is the dependent variable for the Lincoln analyses. 102

115 the locations of blockgroups that did not experience even a single robbery event in the year The highest quartile range is depicted in black. Robbery rates are very low in the Eastern and Southern suburban fringes of Lincoln (most of these blockgroups are illustrated in the white and the lighter of the two shades of grey). Rates appear to be somewhat higher in the downtown region of Lincoln (the Northwestern section of town). As was seen in San Antonio, several crime-ridden neighborhoods are adjacent to low crime neighborhoods in Lincoln. Table 12. Description of Variables Used in Regression Models (N = 174) Variable Name Min. Max. Mean Std. Dev. Dependent Variable A 1) Robbery Independent Variables 2) % Unemployed ) % Receiving Public Assistance ) Concentrated Immigration ) % Ages ) % Male ) % Moved in the Last 5 Years ) Population Density ) Distance Central City ) Index of Mixed-Use ) % Multiple Family Residential ) % Industrial Use ) % Commercial Use ) % Vacant Land ) Schools ) Convenience Stores/Gas ) Hospitals ) Hotels ) Banks ) Recreation Centers ) Churches ) Malls ) Libraries A = DV calculated as the logged (ln) rates per 10,000 people 103

116 Figure 17. Robbery Rates in Lincoln, NE (Map Presented in Quartiles) A histogram of the dependent variable is presented in Figure 18. The histogram was created using the Dynamic ESDA extension within ArcView 3.2a. The far left column in the histogram represents the 98 blockgroups that did not have a single robbery reported to the police in

117 Figure 18. Ten Category Histogram of Logged Robbery Rates in Lincoln, NE LOGGED ROBBERY RATES Zero-order correlations are presented in Table 16 (a key appears directly below the table). Robbery rates are most highly correlated with the distance from downtown (r = 0.38) and the percent of land dedicated to commercial use (r = 0.37). However, the majority of the variables are not highly correlated. 105

118 Table 13. Correlation Matrix of Independent Variables within Blockgroups (N = 174) ) Robbery Rates 9) Index of Mixed-Use 17) Hotels 2) % Unemployed 10) % Multiple Family Res. 18) Banks 3) % Rec. Public Assistance 11) % Industrial Use 18) Recreation Centers 4) Concentrated Immigration 12) % Commercial Use 20) Churches 5) % Ages ) % Vacant Land 21) Malls 6) % Male 14) Schools 22) Libraries 7) Population Density 15) Convenience Stores/Gas 8) Distance Central City 16) Hospitals 106

119 There is no evidence of multicollinearity in the zero-order correlations. The strongest correlation amongst the independent variables is between the percent of land dedicated to multiple family residential use and the percent of the population that is between the ages of 18 and 24 (r = 0.60), which is consistent with the city s large population of university students that must seek rental housing while in Lincoln. In addition, the distance from downtown is negatively correlated with the percent of the population that is between the ages of 18 and 24 (r = 0.56). Again, this correlation may be due to the location of the university being closer to the central regions of the city, and the number of students that are living in housing close to the university. The correlation between the population density and the percent of persons between the ages of 18 and 24 and the correlation between the mixed land-use measure and the percent of the land dedicated to industrial use are also relatively high (r = 0.53). In a large university town such as Lincoln (University of Nebraska), we might assume that these young individuals were renting in areas that are likely to be high in population density, while attending the university. Further, zoning for industrial land-use may be easier to achieve in areas where many other diverse landuses are also present. Analyses for Autocorrelation While crime data often violate the assumptions of independence and homogeneity required for Ordinary Least Squares analyses (Anselin, 1998), exploratory analyses of robbery rates in Lincoln indicated that while some autocorrelation was present, there was not enough of a problem to merit the use of non-standard regression techniques. The Moran s I value was slightly more than 0.16, with 51 of the 174 blockgroups in Lincoln falling into the upper right quadrant of the Moran s scatterplot (see Figure 18). While, the resulting Moran s I value was significant (p < 0.05), the LaGrange Multiplier tests indicated that the autocorrelation was not present to 107

120 such a degree as to require spatial analysis of the models. The map of the Moran s I was calculated using the SpaceStat program and the Dynamic ESDA extension of ArcView 3.2a (See Figure 19 below). Areas that were high in robbery rates and that were surrounded by other like neighborhoods are illustrated in black. All other neighborhoods are illustrated in light grey. While there was not enough spatial autocorrelation present to merit the use of spatial regression, it appears that high robbery rates may have been focused near the downtown regions of Lincoln. Figure 19. Moran s I Value for Robbery Rates in Lincoln, Nebraska LOGGED ROBBERY RATES 108

121 Figure 20. High Robbery Areas in Lincoln, NE 109

122 Multivariate Analyses The multivariate results are present in Table 14. As noted above, spatial regression techniques were not required for the robbery analyses in Lincoln. The analyses investigating robbery in Lincoln use only Ordinary Least Squares statistical techniques. Testing the hypotheses of this project, the following analyses sought to determine which land-uses had a direct effect on neighborhood robbery rates, and whether land-uses mediated the effects of social characteristics on crime. It had been hypothesized that some land-uses and some types of establishments were likely to serve as attractors and generators for crime. The first OLS model presents the effects of census variables. Despite recent theoretical arguments that measures of poverty, unemployment, female-headed households, and the receipt of public assistance represent a single condition denoted as deprivation, these variables do not merit the creation of a single indices. Regardless of methodology (e.g., principal components, alpha factoring, etc.) or the rotation used, these variables do not converge in a single factor, and indeed are not so highly correlated as to cause problems in the analyses if not combined. As such, two of the four variables are included in the analyses for Lincoln percent unemployment and percent receiving public assistance. The percent of poverty was not included, as this variable was too closely correlated to the percent of persons between the ages of 18 and 24 in blockgroups (i.e., most likely due to the presence of the large college population in the city). The female-headed households with children variable was also excluded, as it too highly correlated with the percent receiving public assistance. In addition, the percent of the population that was between the ages of 18 and 24 was too highly correlated with the percent of the population that rented (r = 0.73), the percent of the population that had moved in the past 5 years (r = 0.71), and the residential instability index (r = 110

123 0.77). As such, all three of these neighborhood stability measures were excluded from the analyses. The correlation between the residential instability measures and the percent of persons between the ages of 18 and 24 may be due to the size of the university in Lincoln. Model 1 examined the impact of sociodemographic characteristics on robbery. Only the distance from downtown (-) was significant. The distance from downtown Lincoln was the only viariable that was significant, and it was related to lower robbery rates. Lincoln suburbs had lower robbery rates than more central locations (i.e., downtown) within the city. This finding may be related to the distribution patterns of both human and commercial establishment targets being more spread across space in the suburbs. The distance to travel may make these targets less desirable. Model 2 introduced measures of land-use in order to test whether the relationships between sociodemographic characteristics and robbery were a function of neighborhood land-use patterns. Several land-uses were significant in OLS Model 2: the percent of land dedicated to commercial use (+) and the presence of banks (+). First, as hypothesized, the presence of high percentages of commercial land-use was found to be related to higher robbery rates. These results were as expected. Commercial activity may attract many suitable targets and motivated offenders to an area, while simultaneously reducing the capable guardianship in the area. Second, the percentage of land dedicated to multiple-families was related to higher rates of robbery. These neighborhoods, according to the correlation matrix, are likely to incorporate a highly mobile and youthful population in Lincoln. While the San Antonio results suggested that multi-family dwellings put more eyes on the street, in Lincoln the opposite is true. Thus, it appeared that Lincoln neighborhoods with more multiple-family housing structures may have 111

124 provided more opportunities for robbery, and fewer opportunities for guardianship. Table 14. OLS Results for Robbery Rates in Lincoln, NE at the Blockgroup Level (N = 174) Variable Name OLS Model 1 OLS Model 2 OLS Model 3 Constant % Unemployed % Receiving Public Assistance Concentrated Immigration % Ages % Male Population Density Distance Central City -0.68**** -0.82**** -0.84**** Index of Mixed-Use % Multiple Family Residential % Industrial Use % Commercial Use 1.20** 1.15* % Vacant Land Schools Convenience Stores/Gas Hospitals Hotels Banks 0.97*** 0.52 Recreation Centers Churches Malls Libraries Mixed*Instability Mixed*Density 0.00 Mixed*Disadvantage Recreation*Disadvantage Library*Disadvantage Bank*Disadvantage Adjusted R **** p <.001 *** p <.01 **p <.05 *p<

125 Third, one of the key hypotheses of the current project was that neighborhood land-use patterns would mediate the effects of sociodemographic characteristics such as disadvantage. However, the measures of poverty and neighborhood instability were not significant. Thus, this hypothesis was unable to be tested for these variables. In addition, neighborhood land-use patterns did not mediate any of the significant sociodemographic characteristics in Lincoln (e.g., age and population density). Finally, contrary to the hypotheses of this study, several land-uses were not related to robbery rates. Specifically, mixed land-use, multiple-family residential land-use, industrial landuse, and vacant land were not related to robbery rates. In addition, only the presence of banks was related to robbery rates. None of the other measures examining the locations of local establishments (i.e., convenience stores) were significant. In Model 3, land-uses were expected to interact with a number of neighborhood social characteristics. However, similar to the results in San Antonio, of the originally predicted six interaction terms, none of them were significant. Chapter 6 Summary Chapter 6 has examined the possible relationships between criminogenic land-use and neighborhood robbery rates. Of the land-uses studied, robbery rates are affected by the percent of commercial land in a neighborhood and the presence of banks in blockgroups. Commercial land serves both as an attractor of crime/criminals by providing suitable targets that are both buildings, such as stores, and individuals (persons shopping in commercial areas that may be carrying cash). Similarly, as noted in prior chapters, some land-uses may bring non-residents to a neighborhood, with outsiders serving as both motivated offenders and suitable targets. As expected, the presence of banks was associated with higher rates of robberies. 113

126 Several predictions were not confirmed. A variety of land-use patterns and establishments were not significant predictors of robbery. For example, one of the key tests of this project had been to test the effects of mixed land-use patterns on crime. Mixed land-use was not a predictor of higher neighborhood robbery rates in Lincoln. As was found in San Antonio, the results indicated that particular forms of land-use were important predictors of robbery. In addition, the following land-uses and establishments were not related to robbery rates: the percent of land dedicated to industrial use, the percent of land that was vacant, the presence of schools, the presence of convenience stores/gas stations, the presence of hospitals, the presence of hotels, the presence of recreation centers, the presence of churches, the presence of malls, and the presence of libraries. Further, the neighborhood institutions that were expected to have an effect in disadvantaged neighborhoods (recreation centers and libraries) were not related to robbery rates. 114

127 CHAPTER SEVEN RESULTS IN COLUMBUS, OHIO Introduction Chapter 7 begins with the descriptive statistics and a correlation matrix for variables used in the analyses of the Columbus data. Next, I present two types of exploratory analyses used to test for the presence of spatial autocorrelation. This section is followed by the presentation of results from the multivariate analyses. Finally, the chapter closes with a brief summary of the findings. Descriptive Statistics Descriptive statistics for the dependent and independent variables are presented in Table 15. The logged rate of robberies per 10,000 people is the dependent variable. 12 As in the previous two research sites, the descriptive statistics confirm that many blockgroups experienced no robberies in In fact, 255 of the 830 blockgroups did not experience any robberies in The mean rate of robberies is per 10,000 people. However one blockgroup experienced a large number of robberies in proportion to the number of persons living in the blockgroup, as the blockgroup had a robbery rate of 40,000 per 10,000 residents. This does not necessarily indicate that every resident of that blockgroup was a victim of 4 robberies, rather it is probably indicative of large numbers of both personal and commercial robberies occurring within the boundaries of the blockgroup. 12 The dependent variable was transformed to reduce the amount of positive skew that was present in the original form of the variable. Thus, the natural log of the rate of robbery robberies per 10,000 people is the dependent variable for the Columbus analyses. 115

128 Supporting the descriptive statistics, Figure 22 illustrates the robbery rate in Columbus. The lowest quartile range is depicted in white, which indicates that no robberies occurred in these blockgroups. The highest quartile range is depicted in black. Table 15. Description of Variables Used in Regression Models (Columbus, N = 845) Variable Name Min. Max. Mean Std. Dev. Dependent Variable A 1) Robbery Independent Variables 2) Concentrated Disadvantage ) Concentrated Immigration ) % Ages ) % Male ) Residential Instability ) Population Density ) Distance Central City ) Index of Mixed-Use ) % Multiple Family Residential ) % Industrial Use ) % Commercial Use ) % Vacant Land ) Schools ) Convenience Stores/Gas ) Hospitals ) Hotels ) Banks ) Recreation Centers ) Churches ) Malls ) Libraries A = DV calculated as the logged rates per 10,000 people When examining this map of the dependent variable, it appears that there may be a concentration of robbery in the central part of the city. High rates of robbery appear to extend from the center of the city along an East and West axis, as well as perpendicular from this axis 116

129 heading North from downtown Columbus. However, a wide range of values is present in the data. As noted in the descriptive statistics, a large number of Columbus blockgroups did not experience a single robbery event in the year 2000 (illustrated in white). Many of these zerorobbery neighborhoods, and neighborhoods that were in the second lowest quartile of crime events (illustrated in light grey) can be found in the suburban areas of Columbus. In addition, many crime-ridden neighborhoods can be found adjacent to low-crime neighborhoods. Figure 21. Robbery Rates in Columbus, OH (Map Presented in Quartiles) 117

130 A histogram of the dependent variable is presented in Figure 23. The histogram was created using the Dynamic ESDA extension within ArcView 3.2a. The far left column in the histogram represents the 255 blockgroups that did not have a robbery reported to the police in Figure 22. Ten Category Histogram of Logged Robbery Rates in Columbus, OH LOGGED ROBBERY RATES 118

131 Zero-order correlations are presented in Table 16 (a key appears below the table). Robbery rates are most highly correlated with concentrated disadvantage and the presence the distance from the center of the city (r = 0.39 and r = respectively). The majority of variables are not highly correlated. There is no evidence suggesting that multicollinearity will be a problem. The strongest correlation is between residential instability and the percent of land dedicated to multiple family residential use (r = 0.62). As noted previously, multiple family housing units are often rental properties. Residential instability is also correlated with population density (r = 0.59) and with the percent of land dedicated to commercial use (r = 0.56). In addition, the percent of land dedicated to multiple family use is correlated with the percent of land dedicated to commercial use (r = 0.55). In Columbus, it appears that rental units are present in densely populated areas that are zoned in areas that share blockgroup space with commercial land-use. Further, the percent of persons between the ages of 18 and 24 and neighborhood instability are correlated (r = 0.57), suggesting that these individuals are more likely to live in neighborhoods with high percentages of renters and people who have moved in the past 5 years. Finally, the distance from the center of the city is correlated with concentrated disadvantage (r = -0.51), suggesting that suburban neighborhoods are more financially well-off than their inner-city counterparts. 119

132 Table 16. Correlation Matrix of Independent Variables within Blockgroups (N = 845) ) Robbery Rates 9) Index of Mixed-Use 17) Hotels 2) Concentrated Disadvantage 10) % Multiple Family Res. 18) Banks 3) Concentrated Immigration 11) % Industrial Use (ln) 19) Recreation Centers 4) % Ages (ln) 12) % Commercial Use 20) Churches 5) % Male 13) % Vacant Land 21) Malls 6) Residential Instability 14) Schools 22) Libraries 7) Population Density (ln) 15) Convenience Stores/Gas 8) Distance Central City 16) Hospitals 120

133 Analyses for Autocorrelation As was found in San Antonio, the Columbus robbery data violated the assumptions of independence and homogeneity required for Ordinary Least Squares analyses (Anselin, 1998). The Moran s I statistics and the LaGrange Multiplier tests were the two methods used to test for the presence of spatial autocorrelation in the data. In Columbus, the Moran s I suggests that positive spatial autocorrelation is present (0.39) and significant (p <.001). 13 Figure 23 presents a graphic of the Moran s scatter plot, which illustrates the presence of spatial autocorrelation found within the Columbus data. The Moran s I is indicative of neighborhoods that are geographically similar in location also sharing similar values of robbery and other characteristics being measured as independent variables. 13 Moran s I was calculated using Dynamic ESDA, as well as by using the global Moran s I routine in SpaceStat (Anselin, 1988). 121

134 Figure 23. Moran s I Value for Robbery Rates in Columbus, OH LOGGED ROBBERY RATES 122