Ruth Peterson Christopher Browning Catherine Calder Lauren Krivo Mei-Po Kwan Ohio State University
Urban areas in the U.S. are characterized by the uneven distribution of social groups across geographic space as evidenced in pervasive levels of residential segregation by race, ethnicity, and economic status. This segregation is associated with crime and other deleterious conditions for communities with high concentrations of disadvantaged and minority residents, but carries benefits for more advantaged and White localities. These differential outcomes of segregation have been interpreted as evidence that the spatial isolation of minority neighborhoods produces social problems by creating and reinforcing social and structural isolation. However, most crime studies rely on measures that do not take into account the location of segregated areas relative to one another. As such, they leave unanswered the question of how the inherently spatial nature of segregation is connected with the geographic distribution of criminal activity. This research draws on studies of segregation and its consequences, analyses of geographic variation in crime, and aggregate and multilevel crime research to develop a model that brings space into the analysis of crime. Our broad question is: How does racial, ethnic, and associated economic segregation affect the geographic distribution of crime? Answering this general question entails addressing four specific objectives: (1) developing more refined spatially-based measures of locally segregated (along a variety of dimensions) areas using the best Geographical Information Systems (GIS) data and techniques; (2) identifying what aspects of the physical infrastructure of local communities affect crime; (3) determining how the effects on crime of nearby localities vary across neighborhoods within cities; and (4) evaluating how the effects of local segregation on neighborhood crime varies across cities. Thus, the intellectual merit of the study consists of the light it will shed on the linkages among city-wide segregation, local segregation, and neighborhood crime through meeting these objectives.
The project will analyze data for 10 large U.S. cities for circa 2000: Austin, Boston, Chicago, Columbus, Fort Worth, Jacksonville, Milwaukee, Oklahoma City, Phoenix, and Portland. The 10 cities cover a range of Black-White segregation, incorporate regional variation, and have relatively large Hispanic and/or Black populations. National Neighborhood Crime Study (NNCS) data will be combined with GIS-based measures of local segregation and physical and ecological characteristics of areas derived from GIS databases on digital transportation networks and parcel- and building-level land use, and other public sources. State-of-the-art GIS analyses and hierarchical Bayesian spatial statistical modeling techniques will be used to evaluate our theoretical model. The modeling strategy is designed to highlight the substantive spatial effects of different types of local segregation (e.g., by race, Hispanic origin, economic status) on crime, while controlling for residual spatial dependence in crime caused by unmeasured conditions. The hierarchical Bayesian approach also permits the within-city and between-city substantive issues to be explored in an integrated manner that accounts for residual spatial dependence.
Broader Impacts. Substantively, the study will shed light on how a major social process (i.e., segregation) fosters inequality in the prevalence of crime among diverse groups within cities. Beyond its substantive impact, this work will: (1) integrate thinking in three different fields about the sources of neighborhood crime, laying a foundation for future advances in interdisciplinary work on this important policy topic; (2) develop methodological strategies that will serve as models for research in a variety of fields seeking to integrate multiple types of spatial data and concepts; (3) make available to the scientific community newly refined GIS-based measures of local segregation and neighborhood physical and ecological characteristics as supplements to the NNCS; and, (4) provide an attractive topic and diverse mentors for undergraduate and graduate students of color and from economically disadvantaged backgrounds seeking to enhance their ability to conduct research.
