GEOGRAPHIES OF URBAN CRIME:
AN INTRAURBAN STUDY OF CRIME IN
NASHVILLE, TN; PORTLAND, OR; AND TUCSON, AZ
by
Meagan Elizabeth Cahill
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF GEOGRAPHY AND REGIONAL DEVELOPMENT
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2 0 0 4
TABLE OF CONTENTS
LIST OF FIGURES 7
LIST OF TABLES 9
ABSTRACT 10
CHAPTER 1. INTRODUCTION 12
LL Urban geographies of crime 12
1.2. Situating the research 15
1.3. Theorizing geographies of crime 16
1.3.1. Social control-disorganization 20
1.3.2. Routine activities theory 34
1.3.3. Multicontextual criminal opportunity theory 45
1.3.4. Apphcation of the theory 52
1.3.5. Format of the dissertation 56
CHAPTER 2. PRESENT STUDY 58
2.1. A Review of the Cities Under Study 58
2.2. Data 65
2.3. A global model of crime? Composing and decomposing interurban models
of crime 70
2.4. Alternative measures of crime and crime profiles 72
2.5. Geographically weighted regression in ecological studies of crime .... 74
2.6. Conclusion 77
REFERENCES 81
APPENDIX A. A GLOBAL MODEL OF CRIME? COMPOSING AND DECOMPOSING
INTERURBAN MODELS OF CRIME 87
APPENDIX B. ALTERNATIVE MEASURES OF CRIME AND CRIME PROFILES . . 128
APPENDIX C. GEOGRAPHICALLY WEIGHTED REGRESSION IN ECOLOGICAL STUDIES
OF CRIME 157
ABSTRACT
Understanding the context of crime is key to developing informed policy that will reduce crime in conmmnities. In exploring criminal contexts, this dissertation tests criminal opportunity theory, which integrates social disorganization and routine activity theories. Methodologically, the dissertation presents unique ways of modeling space in crime studies. Analyses are undertaken in three cities, Nashville, TN; Portland, OR; and Tucson, AZ, chosen for their similar crime rates and varied demographic and social characteristics.
This dissertation includes three papers submitted for publication. Crime data were collected for nine crimes over the period 1998-2002. Census data, used to create an array of socioeconomic measures, and land use data were also used in the analyses, presented at the census block group level.
The first paper attempts to determine whether certain structural associations with violence are generalizable across urban areas. The idea is tested by first developing an Ordinary Least Squares model of crime for all three cities, then replicating the results for each city individually. The models provide support for a general relationship between violence and several structural measures, but suggest that the exploration into geographic variation of crime and its covariates both within urban areas and across urban areas should be undertaken.
The second paper explores an alternative to crime rates; location quotients of crime. A comparison of location quotients and rates is provided. The location quotients are then used in a regression modeling framework to determine what influences the crime profile of a place. The results demonstrate the efficacy of simple techniques and how location quotients can be incorporated into statistical models of crime. The models provide modest support for the opportunity framework.
The final paper explores possible spatial variation in crime and its covariates through a local analysis of crime using Geographically Weighted Regression (GWR). Those results are compared to the results of a 'base' global OLS model. Parameter estimate maps confirm the results of the OLS model for the most part and also allow visual inspection of areas where specific measures have a strong influence in the model. This research highlights the importance of considering local context when modeling urban violence.
Discussion
The application of GWR to a model of violence rates and its comparison to an OLS base model has yielded several striking results. Theoretically, the OLS model, while not as robust as hoped, did provide support for the criminal opportunity theory. All ten measures of the three elements of opportunity—targets, offenders, and guardians— were significant and nine were in the expected direction. The ICE measure was the only variable with a counter-intuitive result—a negative coefficient. The GWR results, however, provided insight to the model and revealed that most areas indeed did have a negative parameter estimate; these tended to be areas of concentrated disadvantage. A smaller number of areas were affluent and had positive parameter estimates. While this result is still unexplained, the OLS model masked important variation in the parameter. The GWR results allow the researcher to focus an investigation on those areas where the model is not performing as expected. The GWR results also revealed positive and negative parameter estimates for the concentrated disadvantage measure, but examination of the pattern of concentrated poverty itself revealed that the negative values still supported the theoretical expectations.
Other GWR results for the most part strengthened the OLS findings. However, the spatial significance tests revealed that six of nine parameters demonstrated significant variation over space; i.e., the relationship between those parameters and the violence measure varied across the study area. One way to model this result statistically is to develop a mixed model where some parameters are allowed to vary over space, and are estimated using the GWR methods described above while other parameters in the model are fixed. The fixed parameters would have only one estimate, a global estimate that assumed the relationship between that measure and violence to be equal across space. This type of model would also allow dummy location variables, like the 'west of the Willamette River' variable to be included in the model without creating uninterpretable results.
The hierarchical clustering exercise resulted in seven geographically coherent groups with similar overall models based on the GWR parameter estimates. The average values for each parameter within each group showed that the strongest (positive or negative) parameter estimates clustered together in groups 1 and 5, where the average violence rates were in the low-to-mid range of all seven groups. Smaller values (positive or negative) clustered in groups six and seven, which surprisingly had the highest overall violence rates.
While in support of opportunity theory, the OLS model was not as robust as was hoped, explaining only 46% of the variance in Portland's violent crime rates. The results, especially those for the ICE and heterogeneity variables, indicate that measures not included in the above models could improve the performance of both models. These measures were included as proxies for control or guardianship, and thus reconsidering the measures of guardianship employed in the present study could afford a stronger model. In addition, several of the measures, while significant, had very low parameter estimates, especially single-person households, married families, and population density. These guardianship measures might be too indirect and the model might be improved with more direct measures of control. Introducing alternate or additional measures of opportunity may also improve the model. Specifically, the opportunity theory draws from routine activity theory; a measure of individual behavior that better captures aggregate routine activities in each area would likely improve the model.
Conclusion Generally, the results support the application of GWR in this context as the results provided insight into the spatial patterns of parameter relationships. The GWR model ing exercise thus demonstrated the efficacy of tliis rnetliod for descriptive purposes—for exploring spatial relationsliips between predictor variables and the dependent variable. In addition, the spatial significance Monte-Carlo tests strengthened the argument for at least considering space in studies of violence, if not explicitly including it; here most of the variables did indeed have locally-varying relationships with the violence measure.
GWR can be useful in different types of crime studies. Here, applied in a test of opportunity theory, the exploration of space can help account for differences between communities not captured by standard measures and thus explain causes of crime in different areas. GWR can also be particularly useful in policy studies. Different interventions for crime reduction or prevention may be appropriate in different areas; local attitudes towards types of interventions can vary across an urban area and affect the success of an intervention. Alternatively, GWR can be used to evaluate the success of an intervention already in place by determining areas where the intervention was more successful and why. The method is thus applicable in a range of contexts within the field of criminology.
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