A GIS MODEL IDENTIFYING HIGH-RISK AREAS FOR DRUG CRIMES WITHIN BURLINGTON, IOWA

Transcription

1 A GIS MODEL IDENTIFYING HIGH-RISK AREAS FOR DRUG CRIMES WITHIN BURLINGTON, IOWA A THESIS PRESENTED TO THE DEPARTMENT OF HUMANITIES AND SOCIAL SCIENCES IN CANDIDACY FOR THE DEGREE OF MASTER OF SCIENCE By ROBERT MILLER NORTHWEST MISSOURI STATE UNIVERSITY MARYVILLE, MISSOURI 15 NOVEMBER 2014

2 A GIS MODEL OF HIGH-RISK AREAS FOR DRUG CRIMES WITHIN BURLINGTON, IOWA A GIS Model of High-Risk Areas for Drug Crimes Within Burlington, Iowa Robert Miller Northwest Missouri State University THESIS APPROVED Thesis Advisor, Dr. Patricia Drews Date Dr. Yi-Hwa Wu Date Dr. Ming-Chih Hung Date Dean of Graduate School Date

3 A GIS Model of High-Risk Areas for Drug Crimes within Burlington, Iowa Abstract This thesis examines the feasibility of creating a GIS model that outlines projected risk levels for drug crimes in Burlington, IA. The project answers the following question: Can a GIS model be created to identify areas of high risk for drug crimes in Burlington, Iowa? This model used academic research on criminology, crime mapping, and drug abuser profiling to identify five model variables. The five variables were demographics (age, homeless, income, and education) and the density of property crimes. The demographic data were U.S. Census Bureau data, while the property crimes were geocoded street addresses provided by the Burlington Police Department. These variables were then evaluated for their relationship to drug crimes using regression analysis, which confirmed some relationship. Following the regression analysis, the five variable layers were added together using a raster calculation to produce one overall risk layer. This overall risk layer was then tested for correlation to drug arrests per square kilometer using the Spearman's rank correlation coefficient. The correlation coefficient suggested a link between the risk levels and the amount of drug arrests that occurred in the level of risk. This is significant as the model can be used to assist law enforcement with resource allocation and targeting of high-risk areas. iii

4 Table of Contents List of Figures... vi List of Tables... viii List of Equations... ix Acknowledgments... x Abbreviations... xi Chapter 1: Introduction... 1 Research Question... 2 Justification... 2 Chapter 2: Literature Review... 3 Crime Theory... 3 Crime Mapping... 7 Drug Abuser Profiling Chapter 3: Conceptual Framework and Methodology Study Area Data Sources Methodology Data Preparation Methodology Regression Analysis Methodology Overlay Analysis iv

5 Methodology Model Validation Chapter 4: Analysis Results and Discussion Analysis Results Multivariate Regression Chapter 5: Conclusion References v

6 List of Figures Figure 1. Study Area Figure 2. ACS Table Figure 3. Drug Crimes Figure 4. Property Crimes Figure 5. Drug Crime by Median Age Figure 6. Drug Crime by Vacant Figure 7. Drug Crime by Population Receiving Public Assistance Income Figure 8. Drug Crime by Population without a Diploma Figure 9. Drug Crime by Property Crime Figure 10. Median Age Figure 11. Number of Vacant Houses Per Square Kilometer Figure 12. Population Receiving Public Assistance Income Figure 13. Population without a High School Diploma Figure 14. Kernel Density Based upon 64 Meter Radius Figure 15. Kernel Density Based upon 132 Meter radius Figure 16. Risk Layer Based upon 64 Meter Radius Figure 17. Risk Layer Based upon 64 Meter with Drug Crimes Figure 18. Zoomed in 64 Meter Radius with Drug Crimes vi

7 Figure 19. Risk Layer Based upon 132 Meter Radius Figure 20. Risk Layer Based upon 132 Meter with Drug Crimes Figure 21. Zoomed in 132 Meter Radius with Drug Crimes Figure 22. Spearman Rank Based upon 64 Meter Density Figure 23. Spearman Rank Based upon 132 Meter Density Figure 24. Risk Layer Based upon 64 Meter Radius Figure 25. Risk Layer Based upon 64 Meter with Drug Crimes Figure 26. Zoomed in 64 Meter Radius with Drug Crimes Figure 27. Risk Layer Based upon 132 Meter Radius Figure 28. Risk Layer Based upon 132 Meter with Drug Crimes Figure 29. Zoomed in 132 Meter Radius with Drug Crimes Figure 30. Spearman Rank Based upon 64 Meter Density Figure 31. Spearman Rank Based upon 132 Meter Density vii

8 LIST OF TABLES Table 1. Regression Results Table 2. Results Based upon 64 Meter Radius Table 3. Results Based upon 132 Meter Radius Table 4. Spearman Rank Based upon 64 Meter Density Table 5. Spearman Rank Based upon 132 Meter Density Table 6. Multivariate Regression Table 7. Multivariate Regression Table 8. Multivariate Regression Table 9. Multivariate Regression Table 10. Multivariate Regression Table 11. Multivariate Regression Table 12. Multivariate Regression Table 13. Multivariate Regression Table 14. Multivariate Regression Table 15. Results Based upon 64 Meter Radius Table 16. Results Based upon 132 Meter Radius Table 17. Spearman Rank: 64 Meter Density with Property Crime and Vacant Only Table 18. Spearman Rank: 132 Meter Density with Property Crime and Vacant Only viii

9 List of Equations Equation Equation Equation Equation Equation Equation ix

10 Acknowledgments I would like to thank three groups that provided data for this project. First is the Burlington Police Department. If it wasn t for their collaboration and willingness to share their crime data, I would not have been able to finish this study. I would also like to thank the Minnesota Population Center for making the Census Bureau data more accessible and available. The final group I would like to thank is the Des Moines County GIS team for providing the city boundary file for my study area. The development of this thesis also took the help of numerous family, friends, and co-workers. These groups of people assisted with everything from proof reading my paper to helping me understand statistical testing. I am forever grateful for their support. I would not have finished this paper without the help of a few specific people. The first group is my thesis committee: Drs. Drews, Hung, and Wu. Their support advising and guiding me was instrumental in the development of this thesis. They truly helped me turn this concept into a scientific and repeatable piece of analysis. Thanks for challenging me to improve this project. The second group is my family: Laura, Corwyn, and Saelin. You put up with me while I stressed out about my deadlines and helped remind me why I was working on this in the first place. You put up with a lot of lonely nights without me as I typed away in the study. Thank you for supporting me through this process. x

11 List of Abbreviations BPD BJS DMCGD Esri GBP GIS NIJ Burlington Police Department Bureau of Justice Statistics Des Moines County GIS Department Environmental Systems Research Institute Greater Burlington Partnership Geographic Information System National Institute of Justice xi

12 Chapter 1: Introduction This project builds on the work of many other crime mapping projects in the United States. One of these projects is the Drug Market Analysis Project completed by the U.S National Institute of Justice (NIJ, 2013). This study and others like it helped develop the idea that in order to better conduct drug enforcement operations, police departments need to think in terms of place, not people. This is because people are mobile, but their actions are tied to a specific place and time. Although the model described in this paper does not account for temporal analysis, it does build on the concept of spatial analysis (Taxman and McEwen, 2010). Eck (2010) asserted that there are two ways to sell drugs: acquaintance networks and routine activity markets. By this, he meant that you either buy drugs from people you know, or you buy from strangers in a mutually beneficial location. The benefit for the drug user operating within the acquaintance market is that the drug distribution time and location can be scheduled. This is not the case for the user in the routine activity market. In the routine activity market, the user has to depend on the fact that a person will routinely be at a general location at a general time trying to sell drugs (Eck, 2010). This thesis research does not specifically analyze the difference between the two distribution methods, but the model used in this thesis is more practical for drug sales operating within the routine activity market. The GIS model in this research will attempt to identify risk areas for the routine drug markets in the city of Burlington, Iowa. Some of the criteria drawn for this model come from the anecdotal evidence outlined by Reding (2009), who described the connection of a declining economy and the increase in methamphetamine use. More 1

13 substantial evidence for the criteria used in this predictive model can be found in the literature review section of this proposal. Research Question This project answers the following question. Can a GIS model be created to identify areas of high risk for drug crimes in Burlington, Iowa? The high-risk locations will be based upon the following factors: demographics (age, homeless, public assistance income, and education) and the density of property crimes within a census block group. The study will statistically test these factors using regression analysis to identify correlation between the five variables listed above and the number of reported drug crimes. The model will then be tested using the Spearman s correlation coefficient to validate its results. Depending upon the results of this project, the information can then be published for use by the local law enforcement in support of resource allocation to the high-risk areas. Justification The clandestine nature of drug use makes locating and policing drug markets extremely difficult. With a national average of 2.3 officers per 1,000 residents, it is difficult for most police departments to locate all drug markets within their area of responsibility (Bureau of Justice Statistics, 2013). The use of GIS can assist local law enforcement with resource allocation by identifying high-risk locations for drug arrests. 2

14 Chapter 2: Literature Review The research for this project can be grouped into three main categories. Those categories are crime theory, crime mapping, and drug abuser profiling. Understanding these three topics will lead to the credibility of the GIS model used in this project. Crime Theory The first category of research this project will address is general crime theory. Much of this research is independent of GIS theory and may not apply to spatial modeling, but it will provide a foundation for the spatial modeling aspect of the model. By applying the general crime theory to a GIS, an analyst can use specific census demographics of a likely criminal to help predict where criminal activity is going to occur. For example, Agnew s general strain theory suggests that crimes are committed by society members that do not have equal means to achieve universally applied goals (Chainey and Ratcliff, 2005). Since all members of a society are striving to achieve similar goals, those that have a negative life event are put at a disadvantage and that stress leads to deviant behavior. This can only be countered if a proper coping mechanism is developed (Lo et al., 2008). Even though this theory does not have a direct spatial aspect associated with it, it still supports the relationship between demographics and crime used in this project. One example of crime theory that can be applied to GIS modeling is environmental criminology. This place-based study looks specifically at the idea of space and how it influences deviant behavior and victimization. Environmental criminology categorizes different locations and tries to explain the influence the space has on 3

15 criminal/victim behavior (Cozens, 2011). Although this theory mainly looks at the social aspects of crime, it can still support the spatial-criminal correlation. One application of environmental criminology to display the spatial-criminal correlation is routine activity theory. This theory assumes that both offenders and victims have a typical daily routine. The convergence of these two routines and the absence of a capable guardian in a given place and time are core elements of crime (Groff, 2007). Offenders have places they live, shop, or work. According to this theory, crimes are usually committed within buffer zones around these locations. This buffer concept is explained in more detail in the next paragraph. The victims also have an area from which they tend to carry out their daily activities. When a suitable target s routine overlaps these areas, a crime opportunity is presented (Cozens, 2011). Crime usually occurs within buffer zones because offenders operate in areas that they are comfortable with, but not so comfortable that people in the general area will recognize them (Chainey and Ratcliff, 2005). For example, a car thief is not going to steal a car from their next-door neighbor because they are afraid the neighbor may see him or her following the crime. But they are less likely to randomly run into someone they know a block or two away. Therefore they are more likely to steal from victims in that area. One criticism of this theory is that it doesn t pay much attention to offender motivations. If an offender is highly motivated, he or she may travel outside of their daily routine to commit a crime (Groff, 2007). This is a valid concern to consider when applying the theory, and crime analysts should expect some level of error in models based on this theory due to offender motivations. The routine activity theory builds upon the concept that all crimes include four 4

16 dimensions. These dimensions are outlined by Brantingham and Brantingham (1984) as the law, the victim, the offender, and the location (Chainey and Ratcliff, 2005). Every crime must break some type of law or else it is just a violation of a social code. The crime must also be committed by one person, which harms another. Finally the crime has to occur at a specific location and at a specific time. The routine activity theory can be applied to several different laws by looking at the other three dimensions. This theory first looks at the offender and his or her activities. The offender has certain buffer zones that are optimal for committing crimes. Even though these zones are primarily set by the patterns of the offender, they can be altered by someone with influence over the offender. This person, or handler, could be a parent that can encourage the offender to not commit the crime, or the handler could be a criminal supervisor encouraging the offender s deviant behavior (Chainey and Ratcliff, 2005). The victim s space can be altered by others as well. If a suitable target is traveling into an offender s buffer zone with a police officer or other guardian, they are less likely to become a victim of a crime. Finally, the location of the crime can influence the crime opportunity if it has a place manager. This is most commonly seen as a security guard at a business or residential area. The presence of this security guard decreases the likelihood of crime (Chainey and Ratcliff, 2005). In short, the routine activity theory looks at the overlapping area where the offender s and victim s patterns of life overlap. This theory can be summarized as Equation (1) adapted from Chainey and Ratcliff (2005). The equation can be summarized by saying: when an offender (influenced by others) encounters a target void of a guardian 5

17 in a suitable area void of a place manager, an opportunity for a crime is present. (Offender +/- Handler) + (Target - Guardian) + (Place Manager) = Crime Opportunity. Equation (1) However, the buffer described above has its limitations. This is applied in greater detail in crime pattern theory. This theory looks at the spatial pattern of crimes to answer the question of where the next offense is likely to occur. This is the basis for much geographic profiling and is based on the concept of the least effort principle. The theory states that offenders are not likely to travel great distances to commit crimes because it needlessly expends resources. According to this principle, the majority of criminals will offend within the general vicinity of their home (Chainey and Ratcliff, 2005). Few criminals will travel significant distances to commit a crime. One study found that the average journey to crime distance for burglaries in one constable, a British police jurisdiction, was about three miles. The research found that this average was considered high due to a few burglaries that criminals traveled a great distance to commit (Chainey and Ratcliff, 2005). Although distance to crime can vary based on crime type, method, location, and time of day, research still suggests that crime usually occurs near an offender s home (Levine and Block, 2011). An offender s home is not the only location that can influence the likelihood of a crime. Work, friends, and shopping can also play an important role when studying crime patterns (Cozens, 2011). One application of this theory is seen in the GIS study done on the Norwegian serial pedophile, Erik Andersen, nicknamed the pocket man. When all the initial events were plotted, two clusters formed. These two clusters were located in the towns where Andersen lived and worked (Rose, 2010). 6

18 No matter the specific type of offender node (i.e. home, work, shopping center), it is clear that there is a connection between offenders and crime locations (Randerson, 2004). This is seen in a study conducted by Bowers and Johnson (2005), who found that most burglaries occurred 400 meters from each other. If crime analysts are interested in identifying areas of high crime concentration, then they need to know where previous crime events have occurred (Canter, 2010). One technique in crime pattern analysis is to draw a circle that contains the two most distant crimes and use that area as a geographic profile. When this technique was used by Canter and Larkin in a 1993 study of sexual offenders, they found the offender s residence fell within the circle 87% of the time (Chainey and Ratcliff, 2005). Another technique in crime pattern analysis is called centrography. This technique involves the creation of a mean center point by averaging the X and Y coordinate values of all crimes within a data set. This average point can then serve as a starting point for further geographic profiling (Chainey and Ratcliff, 2005). Crime Mapping The process of spatially enabling crime data is not a new concept. Criminologists have been displaying crime data on a map since the 1800s. (Weisburd and McEwen, 2010). It is, however, still a novel concept in many police departments. It was not common in many police departments until two recent events occurred, specifically the broad acceptance of the routine activity theory described above and the rapid growth of GIS. Prior to these two events, using crime maps to forecast or predict crime was not practical due to the ineffective scales necessary for police application (Gorr and Harries, 2003). 7

19 Crime mapping today, however, is very common within most major police departments. Crime maps can be categorized into three broad categories: dot maps, areal or choropleth maps, and contour maps. Dot maps, which are often called pushpin maps, are maps that simply display the point locations of where a crime was committed. When working with the point data, an analyst must be mindful of the spatial resolution of the data set. Depending on the police department, these maps can plot crimes anywhere from specific rooms of a house to parts of a city. These maps can be very useful for the most basic geospatial analysis, visual inspection (Brantingham and Brantingham, 1984), but, when used alone, are considered outdated and limited compared to more advanced GIS functions (Ratcliff, 2004). Areal and contour maps allow for greater analysis. Areal maps are created by aggregating point data into different area units. These units can be spatially uniform like a fish net grid or spatially irregular like census tracts. By aggregating the point data, it allows the analyst more options in regards to spatial statistics (Brantingham and Brantingham, 1984). Spatial statistics applied to areal maps allow criminologists to compare crime distributions to underlying factors like unemployment, race, and economic data (Ratcliff, 2004). The additional options in spatial statistics come at a cost. By reducing the spatial resolution of the data, obvious data clusters can be broken up and reduced. This often occurs due to the fact that criminals do not factor in most political boundaries when committing an offense (Brantingham and Brantingham, 1984). One way to map crime data without struggling with the boundary issue is to create a contour map. Contour maps assume that the point data is continuous and estimates map values for unsampled points based on known points (Brantingham and Brantingham, 8

20 1984). A suitable way to display crime data as a continuous density surface is the kernel density estimation method (Chainey and Ratcliff, 2005). Contour maps created using the kernel density function can greatly improve the value of traditional pushpin maps. These density maps also help protect the victims by reducing the point data to an estimated area (Ratcliff, 2004). The kernel density method differs from the traditional point density method by factoring in the distance a point is to the cell rather than simply counting the number of events within a cell. This is done in three steps. First, a grid is created over the point layer. Next, a function is calculated for each grid square based upon a specified radius. Then, final grid values are calculated by adding the values for each cell estimate (Chainey and Ratcliff, 2005). The kernel density function requires two inputs: search area and cell size. In the past, limited research on the selection of these two parameters led many researchers to find this type of crime mapping arbitrary and unsuitable for criminology (Ratcliff, 2004). Continued process improvements, however, have helped standardize the proper selection of these two options. The more contentious of the two parameters is the selection of a search radius. One commonly accepted method for selecting a search radius is to calculate the average nearest neighbor for each point in the dataset. This calculation is based upon the average distance between each point and the first closest, second closest, or nth closest point. These first, second, and nth values are called orders. It is recommended that low orders be used for fine detail crime mapping and high orders be used for generalized crime mapping (Chainey and Ratcliff, 2005). 9

21 The second kernel density parameter is the selection of the cell size. As with the search radius selection, smaller cell size values work better for finer resolution maps and more accurately display continuous data. One constraint to consider while selecting small cell size values is the increase in processing time and larger file size. Larger cell size values will not have the same problems with processing time and file size, but they tend to be visually coarser and are normally only suitable for smaller scale maps (Chainey and Ratcliff, 2005). No matter the method used to map the data, researchers must pay attention to how the data points are collected. One criticism of using police data in crime mapping is that it does not include all the crime within an area. Some crimes never get reported to the police due to fear, embarrassment, or apathy. Instead, many researchers suggest surveys be used to more accurately capture the amount of crime in an area (Freisthler et al., 2005). This project does not have the resources to conduct such surveys and will use police reported crime data as an acceptable sample. Drug Abuser Profiling This section looks at each of the five criteria that will be used in the GIS model. These factors are age, homelessness, income, education, and property crime. This section of the literature review supports the rationale of including these criteria in the model. The first criterion is that younger populations tend to have a high user rate for illicit narcotics. There have been many articles published on the average age of a drug user, and although they all point to a young demographic, they don t all agree on the median age. The Menard and Huizinga report suggests that drug use peaks around age 20 (Lo et. al, 2008). Another study on the treatment of drug abusers found that users over 30 10

22 years in age have a lower readdiction rate. This same study also found that older users tended to quit on their own due to the burn out effect (Copemann and Shaw, 1975). The second criterion supported by literature is that more drug use occurs in areas with a greater homeless population. This is based on a study conducted in Barcelona, Spain that found over 85% of all the drug abusers were self-described as homeless (Delas et. al., 2010). Although this study is in a different geographic area, it supports the overall anecdotal evidence that drug abuse is somehow connected to homelessness. A proper census count of the homeless population is difficult to analyze using the Census Bureau s statistics. In previous census publications a category called noninstitutionalized was used to try to capture the homeless population not occupying a housing unit. This category is commonly seen as inaccurate, and the methodology was phased out for the 2000 Census for a more localized approach. This measurement has also proved unreliable and does not result in an accurate count of the homeless population (Peters and MacDonald, 2004). In the 2010 census the bureau used a factor called emergency and transitional shelters to account for homelessness. This factor accounts for 2.6 percent of the 8 million people categorized as group quarters and represents the portion of the population sleeping in shelters (U.S. Census Bureau, 2012). The emergency and transitional shelters factor was not used as a variable for this model because it counts personnel displaced by a disaster. These people would not otherwise be homeless and would end up skewing the model. Since an accurate homeless population is unavailable, a surrogate criterion such as vacant housing units must be used. 11

23 Although it may seem easy to define a vacant housing unit, it is not readily apparent when working with Census data. Before looking at the idea of vacancy, one must first understand the concept of a housing unit. One statistic that commonly gets confused with housing units is that of living quarters. Living quarters are all kinds of living conditions, which include homeless shelters, tents, vans, and the general idea of living on the street. A housing unit, on the other hand, is a structure designed for residence or a separate portion within a structure that serves as someone else s living space (Peters and MacDonald, 2004). Although living quarters is a more inclusive statistic, it can be much more difficult to calculate. The concept of vacancy can also prove to be a difficult qualifier as well. This is because many people own structures that are used for recreational purposes (i.e. recreational vehicles, house boats, vans, or tents) that could also be used as a residence. If these structures are not being used as a permanent residence, they are not counted as housing units. In contrast, if someone has a permanent structure like a vacation home or cabin, it is counted as a housing unit. Oddly enough, these structures are still counted as vacant even if they are occupied during the census data collection. In order to remove uncertainty, most vacant housing units are categorized into six themes: for rent, for sale, sold but not occupied, rented but not occupied, recreational use, and other, which includes abandoned property (Peters and MacDonald, 2004). This research uses vacant housing units for two reasons. The first reason is that an accurate methodology to measure homeless populations is still under debate (Quigley et al., 2002). The second reason vacant housing units is used is due to the concept of vacancy, which is an important factor to consider because of the draw of drug users to 12

24 vacant properties. This correlation is drawn from the broken window theory that suggests as buildings are allowed to lay vacant and deteriorate, by having broken windows for example, they further invite civil disorder and crime (Wilson and Kelling, 1982). The third criterion used in this project is poverty. The routine activity drug markets described by Eck (2010) earlier in this paper can be found in areas of increased poverty, among other factors (Freisthler et al., 2005). This association is seen in a study of the economy of an area and crime. As travel times to employment opportunities increase, so does the likelihood a person will commit a crime. This factor supports the idea that drug markets replace the employment gap left by failing legitimate businesses (Wang and Minor, 2002). The correlation between unemployment and crime is also seen in the 2000 Rengert and Wasilchick study that found that many offenders quit lawful employment to allow for more time to pursue criminal opportunities, or in many cases, they never had a full time job. In these cases, the correlation between the location of the offender s home and the crime is much stronger because their spatial view tends to be much smaller (Chainey and Ratcliff, 2005). The fourth criterion examined is that of drug users falling within the demographic of the less educated. This connection is seen in the conclusion of Taxman and McEwen (2010) when they mentioned that by using school attendance or other pertinent information, organizations are able to analyze drug crimes differently. This is not the only article connecting academic achievement to drug crimes. The connection between education and drug use is seen in many other articles. Chatterji (2006) recognized the correlation between drug use and poor academic 13

25 achievement. He found that drug use is tied to academic failure, which can be quantified in multiple ways. His research found that drug use was correlated to drop out rates, lower scores on standardized tests, and worse general academic performance. Although this article does not identify which is the cause and which is the effect of the correlation, it definitely supports the correlation (Chatterji, 2006). This is not a unique concept; the link between low educational achievement and drug use is seen in many other sources (Freisthler et al., 2005; Copemann and Shaw, 1975). The fifth criterion is supported by the research of Delas et al. (2010), who examined the connection between illicit behavior and drug use. Not only did the survey find correlation between homelessness and drug use, but it also reported that 46.3% of the drug abusers relied on petty crime as their primary source of income. This was the highest percentage of any single income variable. The next highest percentage of main income was financial aid, which accounts for only 7.4% of the surveyed population (Delas et al., 2010). These findings provide justification for including property crime as the final criterion in the GIS model. 14

26 Chapter 3: Conceptual Framework and Methodology Study Area The area of interest for this research is the city of Burlington, Iowa. Burlington is a Mississippi River town, located in the southeast corner of predominantly rural Iowa (Figure 1). The city of Burlington encompasses approximately 39 square kilometers and has a population of approximately 28,000 people. This makes Burlington a significant Iowa town considering the total population of the state is only 3 million people. Burlington also serves as Des Moines County s county seat accounting for approximately 70% of the county s total population (Greater Burlington Partnership (GBP), 2011). Schoenerger et. al. (2006) identified differences between rural and urban drug users, rural being defined as population centers with less than 20,000 people and urban being defined as population centers greater than 50,000 people. This article left a 30,000 person gray area between the two categories (Schoenerger et. al., 2006). Since the study area of Burlington has a population of proximately 28,000 people it will be considered a rural community for this model (GBP, 2011). 15

27 Figure 1. Study Area The county s median household income is $34,730 with a majority of jobs found in the trade, transportation, and utilities industries. The largest employer in Burlington is 16

28 Great River Medical Center, employing 1,780 people, which more than doubles the next largest employer, the Iowa Army Ammunition Plant. Burlington s population occupies 19,000 dwelling units, which maintain an average rent and home value of $349 and $92,901 respectively (GBP, 2011). Burlington is also a prime drug trade route as it is located equidistant from Chicago and St. Louis, falling 240 and 265 miles to each, respectively, and is serviced by air, bus, rail, and barge ports. The police department has only 52 full time officers to police its 28,000 residents (GBP, 2011). This ratio of 1.8 officers per 1,000 residents is well below the national average of 2.3 officers per 1,000 residents (BJS, 2013). Since the ratio is low, resource allocation can be a key issue. The majority of crimes in Burlington are burglary and assault (GBP, 2011). This city was selected for several reasons. One reason is that Burlington is an urban land class surrounded by predominately rural Iowa and Illinois land classes. This gives the city a mixed influence. Another reason is the fact that the Burlington Police Department was willing to share its arrest data to the public for research. Data Sources This study used five data sources. The first data source described here is an Esriproduced file that outlines the census block groups for Des Moines County, Iowa, with attributes from the 2010 census. Although the dataset has several attributes of census data, this study focused on two: the median age per census block group, and the number of vacant housing units per census block group. This data was used as opposed to census data directly from the U.S. Census Bureau because Esri s processing made the information easier to use (Esri, 2010). 17

29 The second source is American Community Survey data obtained from the College of William and Mary and Minnesota Population Center (2011). Although American Community Survey data is produced by the U.S. Census Bureau, the Minnesota Population Center processed the data to make it more user friendly. This data has many attributes as well, but this research only used the fields that dealt with public assistance income and educational attainment for the population 25 years and older. Although this dataset was published in 2011, it still temporally aligns with the rest of the data used in this research as it was created from the five year 2010 American Community Survey data collected from 2006 to The third and fourth data sources are both from the Burlington Police Department. The first of these is the property crime arrests in Burlington, Iowa from July 26, 2010 to January 6, 2011 (Burlington Police Department (BPD), 2011a). Although this data set is broken into different types of property crimes, total property crime arrests were used in this analysis. The second data source provided by the Burlington Police Department is similar to the data described above, only this is an Excel file of drug crime arrests in Burlington, Iowa from January 12, 2010 to January 6, 2011 (BPD, 2011b). Even though this second data set was not used in the GIS model, it was used to confirm the model s validity through the use of statistical testing. This will be covered in more detail during the methodology section of this paper. The final data source used in this study is a boundary outlining the official city limits of Burlington, IA. This was obtained from the Des Moines County GIS Department (DMCGD, 2010). This file provided a reference and analysis extent for most of the operations required in this study. 18

30 The data sets listed above were used to build a model for the prediction of areas at varying risk for drug crimes. The next few sections of this paper outline the methodology for this study. The sections cover the preparation of the data, an explanation of the variable regression analysis, overlay analysis, and model validation. Methodology Data Preparation Prior to the start of the analysis, the data sources required some manipulation. The Esri census dataset contained block groups of every state in the U.S. This dataset was reduced to only the block groups that intersected the Burlington city limits. The only exception to this process was one block group that is part of Illinois. Since this overlap of the city limits and the Illinois block group occurred in the Mississippi river, it was removed. This left 35 block groups for this research. These block groups were then used to isolate the drug abuser profile of a younger, homeless, low income, under-educated, resident susceptible to committing a crime. The first factor that required preparation was homelessness, which was quantified using the number of vacant houses in each census block group, and normalized to account for the size of the block groups. The number of vacant housing units per cell was not calculated during the raster analysis since the pixel values were converted to the same 1 5 values during the reclassification process, which is explained in more detail later. The next model variable to prepare was the lower income demographic. This was prepared using the American Community Survey (ACS) tables from the Minnesota Population Center. The ACS data provided a raw count of the number of people that received public assistance as part of their income in each census block group. The public 19

31 assistance count field was normalized by the block group total population in order to account for census block groups with larger overall populations. The next attribute was the under-educated population. Figure 2 shows the ACS table for educational attainment of the population 25 years and older. This criterion was broken up into several fields based on sex and education level achieved. In order to better quantify the information all values for both genders that had no high school diploma or less were added together and normalized by the block group population 25 years and over to account for census block groups with a larger overall population. Figure 2. ACS Table The final factor that needed data preparation was the property crime data. Both the property and drug crime tables acquired from the Burlington Police Department were stored in similar fashion. The described addresses were used for location information, 20

32 but no actual spatial location. The ArcGIS 10.0 U.S. Streets address locator was used to geocode the addresses to create spatial layers. Any repeated addresses were plotted coincidentally and treated as unique records. Once geocoded, the crime data was used to create three other layers. These layers were a point file of all police recorded drug crime, a point file of all police recorded property crime, and a kernel density raster based upon the property crimes. Although the majority of the addresses were matched at a high confidence, a few addresses on both tables needed correction. The drug crime table contained total 248 records and the geocoding process returned with 5 ambiguous (2%) and 36 unmatched (15%) results needed some additional attention. Almost all ambiguous or unmatched results were due to the officer included the letters BLK in the report. This signified that the crime occurred on a certain block of a street, as opposed to the exact address. Since the spatial resolution for most of the data for this project was at the block group level, removing the word BLK from the address returned a location close enough to be used in this research. There were a few addresses that included apartment building and room locations that returned ambiguous results. The same logic was applied by removing the building and room numbers to identify an acceptable geocoding match. Six addresses were recorded as street intersections that did not return a matching location. For these six addresses, Google Maps was used to determine a nearby address for the two streets, and then used in place of the street intersection. There were only two addresses that had to be removed from the drug crime data. These two addresses were listed as South Hill Area and Burl Area. It was determined that these two addresses did not have the spatial fidelity 21

33 to remain in the dataset. The final results of the geocoding were displayed spatially for visual analysis (Figure 3). 22

34 Figure 3. Drug Crimes 23

35 The property crime table, with 489 records, was geocoded in a similar fashion and displayed many of the same issues, but to a lesser extent. There were only two ambiguous (0%) and 16 unmatched (3%) addresses required corrections similar to the drug crime table. Only one record, which was recorded as Burl Area, was dropped. All other addresses were matched using methods described above. (Figure 4) 24

36 Figure 4. Property Crimes 25

37 Following the geocoding, eighteen of the drug crime addresses was identified to be outside city limits and was removed from the research. Once all tables were converted to spatial data, they were all spatially referenced in the WGS 1984 datum, and displayed using the UTM Zone 15N projection. Methodology Regression Analysis Prior to the creation of the high-risk drug crime layer, regression analysis was used to examine the relationship between the factors selected in this study and drug crimes in order to validate the rationale for using the different variables within the drug crime risk model. It is important to remember that when data is aggregated to a polygon area, it degrades the spatial resolution of the point data. That is why this layer was only used for regression analysis, explained using the simple linear regression expressed in Equation (2). Later in this paper the property crime point data will be used in the methodology as opposed to this polygon layer of property crime. Y = a + bx Equation (2) Regression analysis can help explain whether an independent variable, represented by X, influences the value of a dependent variable, represented by Y. Ordinary least squares regression produces a regression equation in which the regression coefficient b, also known as the slope of the line, is the change in the dependent variable for each one unit increase in the independent variable. This based upon the expected value of the Y-intercept, which is the value of Y when X is equal to zero. The Y-intercept in Equation (2) is represented by a. Once the coefficient b was identified, a predicted value for Y was created using Equation (3). 26

38 Predicted Y = (a + bx) Equation (3) The predicted Y represents the optimized value for Y, not accounting for any error in the X and Y relationship. The predicted Y value was important to calculate because it helped identify the residuals, represented as r, between the optimized values for Y, also called Predicted Y, and the observed values for Y. This is represented in Equation (4). r = Y Predicted Y Equation (4) The residuals were important for the calculation of the relative strength of the relationship between X and Y, which is commonly referred to as r-squared. R-squared was computed using Equation (5), which squared all the r values and then added the results. This value represents the proportion of variation in the dependent variable that is explained by the independent variable. An adjusted r-squared value can also be calculated to adjust for the number of explanatory variables within the model. The adjusted r- squared value will be looked out in more detail during the multivariate section of this paper. r-squared = SUM(r) 2 Equation (5) Not only was the r-squared value important for analyzing the strength of the relationship, but also it helped compute how much error existed between the X and Y variables. Equation (6) calculates the standard error, represented as SE, for the regression. The standard error represents the deviation between the observed coordinate and the 27

39 coefficient b line. This deviation can then be used to look up a p-value, or probability value. SE = (r-squared/(n-2)) Equation (6) The probability value was calculated to explain how likely the regression coefficient value occurred by chance. In order to be at least 90% confident that the independent variable explained variation in the dependent variable, the probability value needed to be 0.1 or smaller. The purpose of the regression analysis was to support the variables identified during the literature review, explained earlier in this paper. Following the regression study any independent variables that displayed a regression coefficient value of zero or had a probability value greater than 0.1 would have been excluded from the GIS model. There are five separate bivariate regressions for each dependent variable: median age, homelessness, lower income demographic, under-educated population, and property crime rate. The null hypotheses were that median age, homelessness, lower income demographic, under-educated population, or property crime rate had no influence on the drug crime rate. The alternate hypotheses were that median age, homelessness, lower income demographic, under-educated population, or property crime rate had a statistically significant influence on the drug crimes rate. All independent variables were found to have a coefficient b value greater or less than zero with the expected negative or positive relationship through regression analysis. They also all were statistically significant with a significance level of 0.1 or less. The r- squared value also suggests that they all explain at least 20% of the relationship 28

40 individually, with property crime explaining the most with an adjusted r-squared value of Although 20% may seem like a low r-squared threshold, it is important to consider that these independent variables are analyzed individually. If calculated as a whole five variables, each with.20 r-squared values, have the potential to explain 100% of a X and Y relationship. Since the regression analysis was only used to confirm the independent variables identified from the research, all independent variables were given equal weights in the model. The different variable combinations were analyzed using multivariate regression and are explained in more detail during a later section of the paper. The individual values of each independent variable can be found in Table 1. The field names for all the regression tables correspond with the five independent variables outlined here: Median Age (Age), Vacant Housing Units (Homelessness), Public Assistance Income (lower income demographic), No High School Diploma (under-educated population), and Property Crime (property crime rate). Figures 5 through 9 show scatter diagrams of drug crimes per square kilometer against each of the independent variables. 29

41 Table 1. Regression Results Drug Crimes Per SQKM Median Age Figure 5. Drug Crime by Median Age 30

42 Drug Crimes Per SQKM Number of Vacant Properties per SQKM Figure 6. Drug Crime by Vacant Drug Crimes Per SQKM % of Population 25 and over Receiving Public Assistance Income Figure 7. Drug Crime by Population Receiving Public Assistance Income 31

43 Drug Crimes Per SQKM % of Population 25 and over without a High School Diploma Figure 8. Drug Crime by Population without a High School Diploma Drug Crimes Per SQKM Property Crimes Per SQKM Figure 9. Drug Crime by Property Crime 32

44 Methodology Overlay Analysis With the data prepared and evaluated for correlation, the crime analysis could begin. In short, the analysis involved converting the census block groups into raster layers (age, public assistance income, vacant, education), the generation of a property crime density layer, reclassification of all raster layers, and a raster calculation. The results of the regression analysis did not influence the equation used in the raster calculation as all values had some level of a statistically significant correlation. The sum of the adjusted r-squared values in Table 1 equals 192%, so clearly the model has some redundancy in variables. By processing an equation with equal weights an analytic baseline can be created. The exact equation used is explained in more detail later in this paper. The first step was the creation of the raster layers from the census block groups. All the census block group rasters were converted in similar ways, using the appropriate attribute as a unique input. They were converted based on the value in the center of the cell. The center of the cell method was selected because it created a uniform approach. For example, if the center of the pixel cell was located within a polygon that had a value of 1, then that pixel value became one. The ideal cell size should be based upon the minimum distance between all the property crimes. An exception was made to this because the minimum distance for all property crimes was zero. This was due to multiple crimes occurring at the same location. One example where this happened was a gas station located at 1003 Summer Street, which served as a crime location several times during the study time period. 33

45 Since the minimum distance of zero is invalid, a ten meter cell size was determined. This was due to processing speed and file size limitations associated with rasters that have less than a ten-meter pixel size. Once the census block group conversion was complete, there were four raster layers: education, public assistance income, vacant, and age. These layers were based upon the attributes described in the data preparation section of this project. Figures depict the results of these raster conversions. All of these maps use the Jenks natural breaks method to display the data for each variable. 34

46 Figure 10. Median Age 35

47 Figure 11. Number of Vacant Houses Per Square Kilometer 36

48 Figure 12. Population Receiving Public Assistance Income 37

49 Figure 13. Population without a High School Diploma Like the census data, the property crime layer was converted to a raster layer for comparison. This was done through the creation of a kernel density layer based upon the 38

50 property crime points. A density layer was selected over geographically weighted regression (GWR) or other vector based analysis to keep the spatial resolution of the point data. In order to conduct GWR, the data would have to be aggregated. By using a density layer, a better risk area prediction can occur, because not all layers conformed to the arbitrary census block group boundaries. The kernel density method was selected over a regular point density model because the kernel density method included not only the number of points, but also the proximity of those points when calculating the density. One of the major inputs required for a density model is the selection of the search radius. Although all values used for this input are technically correct, some values are more meaningful than others. In order to select the search radius for the density model, an average nearest neighbor calculation was run on the property crime layer. This calculation figured not only the average distance between all points in the layer, referred to as the observed average nearest neighbor, but also the average distance between all points if they were randomly distributed across the area. The calculation based on random distribution is referred to as the expected average nearest neighbor. The average nearest neighbor calculation in this project was based upon Euclidean distance, which is also referred to as straight-line distance, with an extent set to the area equal to a rectangle that would encompass all the property crimes. The average nearest neighbor function produced an expected value of meters, commonly and an observed value of meters. Limited academic research existed on which value to use in the creation of density layer, but each value would model the data in a different manner. If the data were clustered, the average nearest neighbor value would be smaller. Using this smaller value in the density would only 39

51 model variations within the clusters, and variation between clusters would be lost. However, if the expected average nearest neighbor value was used for the search radius, then the variations within the clusters would be lost, but it would better model the changes outside the cluster. In order to determine if property crimes were clustered the results of the average nearest neighbor tool were used. The tool also calculates clustering using the nearest neighbor index. This function showed that the property crimes were clearly clustered returning a z-score of and a probability of 0. Even though the property crimes were clustered, it still wasn t clear which value should be used. Because of the lack of available research, the kernel density function was run twice. Once using a 64 meter search radius, and again using 132 meters. These two density layers were used in parallel for the rest of the project. The cell size for both density models was the same as the method described in the vector to raster conversion (i.e. 10 meters). Figures 14 and 15 depict the results of the kernel density operations. Both of these maps use the Jenks natural breaks method to display the property crime density. 40

52 Figure 14. Kernel Density Based upon 64 Meter Radius 41

53 Figure 15. Kernel Density Based upon 132 Meter Radius These six layers, the four census block group layers and the two density layers, were then reclassified from their original values into values of one through five. In an 42

54 effort to remain consistent with the classification, all classifications were based on the Jenks natural breaks method. This method was selected because it seemed to more adequately display the data due to the method s attempt to group the data in naturally occurring clusters (Bolstad, 2012). This is important for this study because I am trying to group the variables into distinct categories. During the reclassification process, the original low pixel values were converted into low values, ranging from 1 to 5. This process is explained in more detail in the following paragraph. For example, if the pixel number representing the vacant homes per square kilometer fell within the first Jenks break, it was reclassified to the number one. If the pixel value fell within the second Jenks break, it was reclassified as a two, and so forth. The only exception to this reclassification method was the median age layer. This layer s lowest values were reclassified to the highest risk value because the population most likely to use drugs is a younger demographic. In order to decrease file size and increase processing speeds, an analysis mask was used during the reclassification step to limit processing and output to the city limits of Burlington. Since all values were reclassified into values one through five, splitting polygons during the analysis mask would have little effect on the cell values. Once reclassified, the four demographic layers were added together with the two reclassified property crime density layers, using two separate raster calculations. In both cases this was done using equal weights for all layers. If not all variables would have been found to be statistically significant at the 0.10 significance level in the regression analyses described previously, then those variables that were not statistically significant at the 0.10 significance level would have been removed from the model. 43

55 Since all variables had a statistically significant relationship, each layer was given an equal weight of one in the raster calculations. This was done to create an analytic baseline that could be compared to other variable combinations. The output created from the raster calculations served as the two overall risk layers. The only difference between these two layers is the search radius used in the kernel density creation. Both risk layers had a pixel value range of 6 to 23. Figures display the two risk layers with and without the drug crimes. Figures 18 and 21 zoom into the higher risk areas in central Burlington. In order to compare the results of the 64 meter and 132 meter maps a similar classification method was used. For both sets of maps the data was grouped into the following categories: values 6-9 were low risk, were medium-low risk, were medium risk, were medium-high risk, and were high risk. The categories outlined above were similar to the Jenks natural breaks method. The only exception was in the 64 meter layer. In this layer, the break value of 16 was moved from the medium high class, to the medium class. By moving this value a better comparison can be made to the 64 meter and 132 meter layers. Visually it appears that the higher risk areas seem to have more drug crimes. However, these maps do not state whether these points aligned randomly or if they are statistically significant. Nor do the maps quantify which search radius predicts better within the model. In order to better understand the effectiveness of the model, more rigorous statistical testing was applied. 44

56 Figure 16. Risk Layer Based upon 64 Meter Radius 45

57 Figure 17. Risk Layer Based upon 64 Meter with Drug Crimes 46

58 Figure 18. Zoomed in 64 Meter Radius with Drug Crimes 47

59 Figure 19. Risk Layer Based upon 132 Meter Radius 48

60 Figure 20. Risk Layer Based upon 132 Meter with Drug Crimes 49

61 Figure 21. Zoomed in 132 Meter Radius with Drug Crimes In order to quantify the results of this model, I first applied descriptive statistics. Using the modified Jenks natural breaks classification method described above, I 50

62 computed the total area of each risk layer, total number of drug arrests, and drug arrests per square kilometer. Table 2 displays this information for the risk layer based upon the 64 meter radius. This table helps quantify the results of the model by statistically describing the results. An important statistic is the number of drug arrests per square kilometer. The expected density for square kilometers and 228 arrests is 5.9. That means if the drug arrests were evenly spaced across the entire area, each square kilometer would have approximately six arrests in it. The results for the actual drug arrests are different. The amount of arrests per square kilometer increases as the risk level increases. The low risk level of.70 is well below the expected result of , and the high risk level of is well above the expected result of The medium risk level 6.33 is less than one arrest away from the average 5.9. Another way to quantify the results is that the high risk area accounts for 6% of the area but 42% of the drug arrests, while the high and medium high risk areas account for 13% of the area but 61% of the drug arrests. Although the low and high values seem to follow an increasing trend, the model does display some deviation in the medium risk level. This could exist for numerous reasons, but one explanation could be due to the large horizontal strip of medium classified area in the northwest part of the study area. This area accounts for the Sunnyside Mobile Home Park that was removed and replaced by the Aldo Leopold Elementary School around the time the 2010 census was published. So it is possible that this area over predicted certain census factors that were not representative of the area during the time of the crime data collection. But this error could also exist from other unexplained errors. 51

63 Table 2. Results Based upon 64 Meter Radius Table 3 displays the same information described above for the risk layer based upon the 132 meter radius. These results are not that different from the 64 meter results. Table 3 shows that the high risk areas had an arrest per square kilometer rate of 37.6, which was well above the expected 5.9 arrests per square kilometer. Put another way, the high risk area only accounted for 7% of the study area but accounted for 43% of the drug crime. By looking at the high and medium high areas, you can see that 15% of the total area accounted for 62% of the crime. As with the previous risk layer, the 132 meter layer also has a deviation in the medium risk level. It is assumed that this area exists for the same reason as the 64 meter layer. One interesting fact is that when the model was run using fewer census factors, explained later in this paper, this deviation in the medium risk level was not identified. 52

64 Table 3. Results Based upon 132 Meter Radius Tables 2 and 3 support the concept that this model predicts drug crime areas. But what it does not do is explain if these numbers are statistically significant, or if they just occurred randomly. This was calculated using inferential statistics and is explained in the next section of the paper. Methodology Model Validation The ability of the risk layer to predict drug crimes was examined using the Spearman s rank correlation coefficient. Spearman s rank correlation coefficient measures the strength of the association between two sets of ranked variables. In order to calculate this coefficient, three data preparation steps had to occur: raster to vector conversion, a spatial join, and ordinal rank conversion. The overall risk layers, explained above, were converted to vector polygon layers so they could be compared to the drug crime point file. This was done using the same raster to vector conversion methods explained earlier in this paper. Two spatial joins between the two overall risk layers and 53

65 the drug arrest point layer created a count of how many drug arrests occurred in each risk polygon. The drug crimes were then divided by the total area of the polygons. Once the vector conversion and spatial join were complete, the values were then ranked on ascending order. Each polygon was ranked by its risk layer value. The polygon with the lowest risk layer value was converted to one, the second lowest risk layer value was converted to two, and so forth. Since several polygons had the same risk value, many of the polygons were given a tied rank, which was factored into the calculation of Spearman s rank correlation coefficient. The tie value was based upon the average of the expected ranks of the tied scores. For example, if the first two values were tied, they would have had the average of one and two, which is 1.5. The same process was used to rank the polygons on the normalized drug crime values. The polygon with the lowest amount of drug crimes per square kilometer was assigned a value of one, with the second lowest getting a value of two, and so forth. Ties were handled in the same way as the risk layer scores. The null hypotheses for this correlation analysis is that Spearman s rank correlation coefficient is equal to zero and no relationship exists between the two variables. The alternative hypothesis is that the correlation coefficient does not equal zero and a relationship exists between the two variables. If the null hypothesis is rejected at a 0.1 significance level, I can reasonably say my model has some ability to assess the risk for drug crimes within my study area. 54

66 Chapter 4: Analysis Results and Discussion The Spearman rank results for the overall risk layer were first calculated for the layer based upon 64 meter density parameters. During this analysis it started to become clear that the planned use of the Spearman correlation coefficient was not going to work. This was observed as I calculated the rank score for the tie values. Due to the raster calculation, numerous small polygons with the same risk level were geographically separated from other polygons with the same risk level and counted as a tie during the Spearman rank correlation. Since there were so many ties on the risk level calculation, it paired the drug crime ranks into numerous tied risk levels. For example, the risk level of 21 had 88 different polygons. This meant that 88 values resulted in tied rank values. This led to a Spearman Rank Correlation value of I did not feel that this methodology accurately explained the data, so I altered my calculation method. Instead of calculating each polygon as its own record within the risk layer, I summarized all polygons for each risk level. This gave me eighteen different ranks based upon risk values 6 through 23. I also calculated the number of drug crimes per square kilometer for each summarized risk level. This gave me values of 0 through , which were then converted to ranks 1 through 18. These values are displayed within table 4. Once the ranks were configured, the two variables were graphically displayed in Figure 22 and then tested for correlation using Spearman's test described above. The results for the risk layer based upon the 64-meter density layer produced a Spearman Correlation Coefficient of This value is considered a strong correlation in most 55

67 social sciences, but it still needs to be judged on statistical significance. The critical value at a significance level of 0.01 for n=18 is 0.600, which means there is a less than 1% chance that these rank values occurred randomly. Risk Level Risk Level Rank Table 4. Spearman Rank Based upon 64 Meter Density # of Drug Arrests Square Kilometers Drug Arrests Per SQKM Drug Arrest Per SQKM Rank

68 Figure 22. Spearman Rank Based upon 64 Meter Density I then ran the same calculation on the risk layer based upon the 132 meter density. This resulted in a value of 0.88, which suggests that there is a less than 1% chance that these ranks were randomly organized this way. This allowed me to reject the null hypothesis and say that this model is able to predict at-risk areas for drug crimes at a statistically significant level. Table 5 and Figure 23 display the correlation between the risk level rank and the drug crime rank for the 132-meter density. Figure 23 highlights that the high values (14-18) and low values (1-4) seem to have a stronger correlation than the middle risk rank values (5-13). 57

69 Risk Level Risk Level Rank Table 5. Spearman Rank Based upon 132 Meter Density # of Drug Arrests Square Kilometers Drug Arrests Per SQKM Drug Arrest Per SQKM Rank

70 Figure 23. Spearman Rank Based upon 132 Meter Density The two correlation tests described above help explain the statistical significance between the risk levels and the density of the drug crimes. But what they don t explain is which variables are over or under predicting. For example, the maps on Figures all had little circles created from the property crime density. The drug arrests appear to fall within those circles. This suggests that property crime may be over predicting compared to some of the other independent variables. In order to help refine the model, I went back to my regression analysis and began running multivariate regressions, as opposed to the bivariate regression explained earlier. Analysis Results Multivariate Regression The multivariate regression was first run with all five variables. As with the bivariate regressions, the count of drug crimes for a block group was divided by the total 59

71 square kilometers of the block group and used for the dependent variable. Median age, number of vacant homes (homelessness), public assistance income, education, and count of property crimes divided by block group served as the independent variables. This regression analyses had a similar hypothesis as the bivariate regressions. The null hypothesis was that median age, number of vacant homes, public assistance income, education, and property crime rate had no influence on the drug crime rate. The alternate hypothesis was that median age, number of vacant homes, public assistance income, education, and count of property crimes had a statistically significant influence on the drug crimes rate. Table 6 displays the results of this analysis. This regression produced mixed results when compared to the same statistical standards outlined earlier in this paper. Although the adjusted r-squared value explained 78% of the variation in the dependent variable, other inconsistencies were found with some of the variables. For example, only Public Assistance Income and Property Crime had statistically significant values, and the Age and Public Assistance Income variables had incorrect signs. The incorrect signs meant that the relationship was not what research expected it to be. For the Median Age, research expected it to have a negative relationship to drug use. Or in other words, as Median Age goes up, drug use should go down. 60

72 Table 6. Multivariate Regression 1 Through a process of trial and error, it was discovered that vacant properties and property crime had the strongest multivariate relationship to drug crimes. More current releases of ArcGIS include an exploratory regression tool that runs regression analyses for all possible combinations of the independent variables. This tool could have replaced the trail and error method used in this paper. The relationship between vacant properties and property crimes, to drug crimes only builds upon the results of the bivariate regression outlined in Table 1. Tables 7 through 14 display the results of several multivariate regression analyses using different combinations of independent variables. Table 10 shows that the adjusted r-squared value when using vacant properties and property crime as the independent variables equals These two variables explain slightly more than the first multivariate regression with all five variables. By analyzing these two variables using multivariate regression, as opposed to bivariate regression, one can see how they predict together. 61

73 Table 7. Multivariate Regression 2 Table 8. Multivariate Regression

74 Table 9. Multivariate Regression - 4 Table 10. Multivariate Regression

75 Table 11. Multivariate Regression 6 Table 12. Multivariate Regression 7 64

76 Table 13. Multivariate Regression 8 Table 14. Multivariate Regression 9 65

77 The results of these multivariate regressions indicate that the optimal independent variable combination for the model is vacant properties and property crime. One may notice that the statistical significance of the variables relationship change wildly based upon the other variables they are calculated with. These inconsistencies exist due to the variable redundancy and over or under predictions that occur when analyzing regression variables at the same time. The same methodology described earlier in this paper was applied to the two variables in the creation of risk layers based upon only vacant properties and property crime. Figures display the risk maps based upon these variables using the 64 meter property crime density described earlier in this paper. A similar modified Jenks natural breaks classification method was used for comparison of the 64 meter and 132 meter layers. In order to classify both layers the same, two break values were altered. In the 64 meter layer the break value of 5 was moved from the medium-low to the medium class, while in the 132 meter layer the break value of 6 was moved down from the medium-high to medium class. The resulting classification method grouped the data into the following categories: values 2-3 were low risk, 4 was medium-low risk, 5-6 were medium risk, 7 was medium-high risk, and 8-9 were high risk. As before, it is difficult to quantify the results by looking at a map so Table 15 describes the results of the analysis. This model produces a noticeably high drug arrest density for the high and medium high risk levels, resulting in and 70.3 drug arrests per square kilometer respectively. This is much higher than the results of the 64 meter layer based upon all five independent variables, as shown in Table 2. 66

78 One important item to point out in Table 15 is the drug crime count field. In Table 2 the numbers aligned with the risk levels low to high. In Table 15 however, the drug crime counts do not align with the increasing risk levels. This is especially true for the medium risk level that has the most drug crimes, equaling 116. But the drug crime count is not a normalized field. It does not account for areas that have a larger or smaller population. In other words, areas with a larger population are more likely to have a larger count of drug crimes. One method to account for areas with a larger population is to convert the drug crime counts into drug crime rates. This can be done by dividing the drug crime counts by the total population of the area, or by calculating the number of drug crimes per 1,000 residents. This is the same method used earlier to compare the number of police in the study area. It is hard to compare the 52 officers on the Burlington police force to other cities. But by looking at the number of police per 1,000 residents, it makes for an easier comparison. Unfortunately for this project, an accurate population count for the risk levels was not possible. So another normalizing factor was used. The drug crime counts were divided by the total square kilometers of each risk level. Although this method assumes even population counts across all areas, it provides a better drug crime rate than just the number of arrests within each risk level. This was also observed in the 132 risk layer, seen in Table

79 Figure 24. Risk Layer Based upon 64 Meter Radius 68

80 Figure 25. Risk Layer Based upon 64 Meter with Drug Crimes 69

81 Figure 26. Zoomed in 64 Meter Radius with Drug Crimes 70

82 Table 15. Results Based upon 64 Meter Radius The 132 meter layer required more significant deviation from the Jenks natural breaks method. The 3 break value was moved from the medium-low to low class. The 4 break value was moved from the medium to medium-low class. The 5 and 6 break values were moved from the medium-high to medium class, while the 7 break value was moved down from the high to medium-high class. These changes allow for a direct comparison of the 64 meter and 132 meter layers. Figures 27 through 29 display the results of the two variable model described above using the property crime layer based upon the 132 search radius. As with the previous results, it is difficult to quantify the results based upon a visual inspection of the map. Table 16 displays the descriptive statistics for this variable combination. As with Table 15, Table 16 has a higher drug arrest density value for the higher risk levels, but the 67.5 density for the high risk level in Table 16 isn t as much of a difference as the Table high risk value. 71

83 Figure 27. Risk Layer Based upon 132 Meter Radius 72

84 Figure 28. Risk Layer Based upon 132 Meter with Drug Crimes 73

85 Figure 29. Zoomed in 132 Meter Radius with Drug Crimes 74

86 Table 16. Results Based upon 132 Meter Radius As with the five variable version of the model, Tables 15 and 16 don t explain if these values occurred by chance, or if they are statistically significant. In order to evaluate the data, I applied the Spearman rank correlation again. Using the same method described earlier in this paper I tested both the 64 meter and 132 meter values for correlation. In both cases, the Spearman rank correlation coefficient resulted in a perfect 1.0 value. This states that as the risk rank goes up, the drug arrest density rank goes up the corresponding amount. Tables 17 and 18 and Figures 30 and 31 display the results of these two Spearman rank correlation analyses. Although this suggests that it is a perfect model, it is premature to declare that since the n value of the test is only n = 8 for both tests. 75