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The goal of Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners. Cityscape is open to all relevant disciplines, including architecture, consumer research, demography, economics, engineering, ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies.

Cityscape is published three times a year by the Office of Policy Development and Research (PD&R) of the U.S. Department of Housing and Urban Development.


 
  • Urban Problems and Spatial Methods
  • Volume 17, Number 1
  • Managing Editor: Mark D. Shroder
  • Associate Editor: Michelle P. Matuga
 

Linking Public Health, Social Capital, and Environmental Stress to Crime Using a Spatially Dependent Model

Greg Rybarczyk
Alex Maguffee
University of Michigan-Flint


Daniel Kruger
University of Michigan


 

This article reports the findings from a localized spatial modeling approach and visual assessment of crime determinants in Flint, Michigan. Factors pertaining to socioeconomic condition, public health, social capital, environmental stress, and neighborhood context were analyzed spatially and statistically using exploratory data analysis, exploratory spatial data analysis (ESDA), ordinary least squares regression (OLS), and geographically weighted regression (GWR). The ESDA indicated that elevated crime densities clustered in legacy residential areas, suggesting the need for a spatially explicit model. The OLS model was able to explain 46 percent of the variation in the model, although the GWR model proved superior, explaining approximately 56 percent. The GWR results largely supported the OLS results, while providing additional insights into the directionality, magnitude, and spatial variation of localized predictors of crime. The factors that contributed positively to crime rates may provide policymakers and law enforcement officials with nuanced information needed for targeted crime-reduction/prevention strategies.


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