Using Geographically Weighted Regression to Explore Local Crime Patterns

Using Geographically Weighted Regression to Explore Local Crime Patterns The present research examines a structural model of violent crime in Portland, Oregon, exploring spatial patterns of both crime and its covariates. Using standard structural measures drawn from an opportunity framework, the study provides results from a global ordinary least squares model, assumed to fit for all locations within the study area. Geographically weighted regression (GWR) is then introduced as an alternative to such traditional approaches to modeling crime. The GWR procedure estimates a local model, producing a set of mappable parameter estimates and t-values of significance that vary over space. Several structural measures are found to have relationships with crime that vary significantly with location. Results indicate that a mixed model— with both spatially varying and fixed parameters—may provide the most accurate model of crime. The present study demonstrates the utility of GWR for exploring local processes that drive crime levels and examining misspecification of a global model of urban violence. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Social Science Computer Review SAGE

Using Geographically Weighted Regression to Explore Local Crime Patterns

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Publisher
SAGE
Copyright
Copyright © by SAGE Publications
ISSN
0894-4393
eISSN
1552-8286
D.O.I.
10.1177/0894439307298925
Publisher site
See Article on Publisher Site

Abstract

The present research examines a structural model of violent crime in Portland, Oregon, exploring spatial patterns of both crime and its covariates. Using standard structural measures drawn from an opportunity framework, the study provides results from a global ordinary least squares model, assumed to fit for all locations within the study area. Geographically weighted regression (GWR) is then introduced as an alternative to such traditional approaches to modeling crime. The GWR procedure estimates a local model, producing a set of mappable parameter estimates and t-values of significance that vary over space. Several structural measures are found to have relationships with crime that vary significantly with location. Results indicate that a mixed model— with both spatially varying and fixed parameters—may provide the most accurate model of crime. The present study demonstrates the utility of GWR for exploring local processes that drive crime levels and examining misspecification of a global model of urban violence.

Journal

Social Science Computer ReviewSAGE

Published: May 1, 2007

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