Exploring Spatial Heterogeneity of Local Factors of Crime Events with Spatial-Temporal Weighted Regression
Date Issued
2011
Date
2011
Author(s)
Yu, Po-Hui
Abstract
For a more effective understanding of spatial-temporal dynamic of criminal factors and hotspots in local-scale built environment, this study employs multi-dimensional kernel density estimation (KDE) and extended weighted regression (STWR) to uncover future possibility of detecting the displacement of hotspots and factors in a context of time geography.
Due to the spatial autocorrelation and heterogeneity of spatial data, geographically weighted regression (GWR) has been proven as a significant approach to address the spatial heterogeneity. However, the cross-sectional nature of GWR constrains it to explore the multi-dimensional phenomena simultaneously. Thus, this study develops a temporal variant of GWR to detect the spatial-temporal heterogeneity of structural measures in space-time cube. Using a geocoded database of residential burglary in Da-an district of Taipei City from 1999 to 2008, this study examine that proposed framework allowing interactively 3-D geovisualization of hotspots by volume rendering. This thesis also represents the spatial heterogeneity of estimations of social structural measures by spatial-temporal weighted regression.
Emphasizing the supplementary aspect of this embedded framework, the author concludes that interactive spatial-temporal data analysis and weighted regression could extract and interpret the spatial-temporal heterogeneity of residential burglary as well as uncover the possibility of detecting criminal displacement in future study.
Subjects
Spatial-Temporal
Weighted regression
Spatial Heterogeneity
Residential burglary
Criminal displacement
SDGs
Type
thesis
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