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.