NDLI: Exploring non-stationarity of local mechanism of crime events with spatial-temporal weighted regression

Content Provider	IEEE Xplore Digital Library
Author	Po-Hui Yu Jinn-Guey Lay
Copyright Year	2011
Description	Author affiliation: Department of Geography, National Taiwan University No.1, Sec. 4, Roosevelt Rd., Da-an District, Taipei City 10617, Taiwan (R.O.C.) (Po-Hui Yu; Jinn-Guey Lay)
Abstract	For a more effective understanding of dynamic of mechanism and cluster of local crime, this study uses kernel density to reveal abilities of detecting space-time hotspots in the context of time geography. Since spatial data are correlated in nature, geographically weighted regression (GWR) has been proven as an effective tool to address the spatial non-stationarity. Thus, this study adopts temporal variants to detect the spatial-temporal non-stationarity of structural measures simultaneously. Using a geocoded criminal dataset of residential burglary in Da-an District of Taipei City from 1999 to 2007, we examine the proposed framework allowing interactively 3-D visualization of crime hotspots by volume rendering. We also reveal the non-stationarity of estimations of social structural measures by a variant weighted regression approach. Emphasizing the supplementary aspect of our embedded framework, we conclude that 3-D spatial-temporal data analysis and the variant of geographically weighted regression could identify the space-time hotspots as well as extract and interpret the spatial-temporal non-stationarity of mechanism of residential burglary.
Starting Page	7
Ending Page	12
File Size	497315
Page Count	6
File Format	PDF
ISBN	9781424483525
e-ISBN	9781424483518
DOI	10.1109/ICSDM.2011.5968120
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Weight measurement Kernel density Data visualization Estimation Bandwidth Cities and towns Residential burglary Spatial-Temporal Kernel Geographically weighted regression Heteroscedasticity
Content Type	Text
Resource Type	Article

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