Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B2, 47-52, 2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
25 Jul 2012
T. Cheng and D. Williams Dept. of Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT, UK
Keywords: Exploratory space-time analysis, visualization, crime analysis, data mining, space-time cube, spatial scan statistics, self-organizing map, multi-resolution Abstract. Crime continues to cast a shadow over citizen well-being in big cities today, while also imposing huge economic and social costs. Timely understanding of how criminality emerges and how crime patterns evolve is crucial to anticipating crime, dealing with it when it occurs and developing public confidence in the police service. Every day, about 10,000 crime incidents are reported by citizens, recorded and geo-referenced in the London Metropolitan Police Service Computer Aided Dispatch (CAD) database. The unique nature of this dataset allows the patterns to be explored at particularly fine temporal granularity and at multiple spatial resolutions. This study provides a framework for the exploratory spatio-temporal analysis of crime patterns that combines visual inquiry tools (interactive animations, space-time cubes and map matrices) with cluster analysis (spatial-temporal scan statistics and the self-organizing map). This framework is tested on the CAD dataset for the London Borough of Camden in March 2010. Patterns of crime through space and time are discovered and the clustering methods were evaluated on their ability to facilitate the discovery and interpretation of these patterns.
Conference paper (PDF, 1245 KB)

Citation: Cheng, T. and Williams, D.: SPACE-TIME ANALYSIS OF CRIME PATTERNS IN CENTRAL LONDON, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B2, 47-52,, 2012.

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