Quantifying geographic variations in associations between alcohol distribution and violence: a comparison of geographically weighted regression and spatially varying coefficient models |
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| Authors: | Lance A Waller Li Zhu Carol A Gotway Dennis M Gorman Paul J Gruenewald |
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| Institution: | (1) Department of Biostatistics, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA;(2) Department of Epidemiology and Biostatistics, School of Rural Public Health, Texas A&M University, 1266 TAMU, SRPH Building, College Station, TX 77843, USA;(3) National Center for Environmental Health, US Centers for Disease Control and Prevention, Mailstop E17, 1600 Clifton Road NE, Atlanta, GA 30333, USA;(4) Prevention Research Center, Pacific Institute for Research and Evaluation, 1995 University Avenue, Suite 450, Berkeley, CA 94704, USA |
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| Abstract: | Past studies consistently indicate measurable local associations between alcohol distribution and the incidence of violence.
These results, coupled with measurements of spatial correlation, reveal the importance of spatial analysis in the study of
the interaction of alcohol and violence. While studies increasingly incorporate spatial correlation among model residuals
to improve precision and reduce bias, to date, most analyses assume associations that are constant and independent of location,
an assumption coming under increasing scrutiny in the quantitative geography literature. In this paper, we review and contrast
two approaches for the estimation of and inference for spatially heterogeneous effects (i.e., associative factors whose impacts on the outcome of interest vary throughout geographic space). Specifically,
we provide an in-depth comparison of “geographically weighted regression” models (allowing covariate effects to vary in space
but only allowing relatively ad hoc inference) with “variable coefficient” models (allowing varying effects via spatial random
fields and providing model-based estimation and inference, but requiring more advanced computational techniques). We compare
the approaches with respect to underlying conceptual structures, computational implementation, and inferential output. We
apply both approaches to violent crime, illegal drug arrest, and alcohol distribution data from Houston, Texas and compare
results in light of the differing methodological structures of the two approaches. |
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