Understanding Spatiotemporal Patterns of Multiple Crime Types with a Geovisual Analytics Approach
Comprehensive crime data sets have been collected over time, which contain the location and time of different crime types such as aggravated assault or burglary. To understand the patterns and trends in such data, existing mapping and analysis methods oft
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Understanding Spatiotemporal Patterns of Multiple Crime Types with a Geovisual Analytics Approach Diansheng Guo and Jiang Wu
Abstract Comprehensive crime data sets have been collected over time, which contain the location and time of different crime types such as aggravated assault or burglary. To understand the patterns and trends in such data, existing mapping and analysis methods often focus on one selected perspective (e.g., temporal trend or spatial distribution). It is a more challenging task to discover and understand complex crime patterns that involve multiple perspectives such as spatio-temporal trends of different crime types. In this Chapter we used a data mining and visual analytics approach to analyze the crime data of Philadelphia, PA, which has all the crimes reported from January 2007 to June 2011. Specifically, the adopted approach is a space-time and multivariate visualization system (VIS-STAMP) and the analysis examines the spatial and temporal patterns across six crime types, including aggravated assault, robbery, burglary, stolen-vehicles, rape and homicide. The geovisual analytic tool provides the capability to visualize multiple dimensions simultaneously and be able to discover interesting information through a variety of combined perspectives. Keywords Spatial-temporal analysis • Crime analysis • Visual analytics • Data mining • Multivariate mapping
D. Guo (*) • J. Wu Department of Geography, University of South Carolina, 709 Bull Street, Room 127, Columbia, SC, 29208, USA e-mail: [email protected] M. Leitner (ed.), Crime Modeling and Mapping Using Geospatial Technologies, Geotechnologies and the Environment 8, DOI 10.1007/978-94-007-4997-9_16, © Springer Science+Business Media Dordrecht 2013
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D. Guo and J. Wu
Introduction
With the increasing academic interests in place-based crime theories since late twentieth century (Anselin et al. 2000), a large body of literature has discussed the relationship between spatial locations and crimes. Crime analyses span across a wide range of topics, such as identifying the crime concentration in a study area (Chainey and Ratcliffe 2005; Craglia et al. 2000; Eck et al. 2005; Murray et al. 2001; Ratcliffe and McCullagh 1998; Wu and Grubesic 2010), discovering the underlying social/ physical factors or built environment that may account for spatial patterns of crime activities (Gorman et al. 2001), investigating theoretical roots of how space exerts influences on the crime pattern (Messner and Anselin 2004), establishing effective models used for law enforcement and legitimate prevention programs (Hunt et al. 2008; Ratcliffe 2004), and developing methodologies for spatial and statistical analyses of crime incident data (Anselin et al. 2000; Bernasco and Elffers 2010; Levine 2006). Crime data may be divided into two major categories based on spatial representation: point data (with point locations of crime incidents) and areal data (crime incident counts aggregated to predefined boundaries). Point crime data can be converted (aggregated) to ar
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