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Built environment and Property Crime in Seattle, 1998-2000: A Bayesian Analysis
Stephen A Matthews1, Tse-Chuan Yang2, Karen L Hayslett-McCall3
1Department of Sociology, Pennsylvania State University, University Park, PA 16802, USA.
Environment & Planning A
|April 17, 2014
Summary
Spatial analysis of Seattle property crime reveals significant clustering. Built environment factors, particularly highways, strongly predict auto theft and burglary, aiding crime prevention strategies.
Area of Science:
- Criminology
- Geographic Information Science (GIS)
- Spatial Statistics
Background:
- Crime research increasingly utilizes spatial perspectives.
- Georeferenced data and GIS tools facilitate spatial crime analysis.
- Understanding spatial crime patterns is crucial for effective prevention.
Purpose of the Study:
- To analyze the spatial patterning and predictors of property crime in Seattle (1998-2000).
- To investigate the influence of built environment variables on crime distribution.
- To model specific property crime types including burglary, theft, auto theft, and arson.
Main Methods:
- Exploratory Spatial Data Analysis (ESDA) to identify crime clusters.
- Bayesian spatial Poisson models implemented in WinBUGS.
- Integration of geographic information systems (GIS) for data visualization and analysis.
Main Results:
- ESDA confirmed significant spatial clustering of property crime in Seattle.
- Built environment variables were identified as significant predictors of property crime.
- Proximity to highways was a key predictor for auto theft and residential burglary.
Conclusions:
- Spatial analysis effectively reveals property crime patterns.
- Built environment characteristics significantly influence crime distribution.
- Targeted interventions in areas with specific built environment features can enhance crime prevention.
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