Document Type

Article

Publication Date

1-1-2011

Abstract

The exploration of spatial relationships between crime incidents, the socio-economic characteristics of neighborhoods, as well as physical and structural compositions of the urban landscape is an ongoing research issue in Geographic Information Science. Spatial data mining tools improve the ability to gain knowledge from geographic data and help to understand spatio-temporal processes that contribute to the presence or absence of criminal offenses. However, most of the currently available tools focus either on the spatial, the temporal, or a combination of both aspects. But crime has a spatial and temporal component in a multidimensional attribute space. Therefore, it is reasonable to combine all these aspects within one analytical framework. This paper presents such a methodology to explore crime patterns and their spatial and temporal behavior within their socio-economic and environmental neighborhoods. The framework consists of three complementary techniques: A spatio-temporal scan statistic to detect crime hotspots, a growing Self-Organizing Map (SOM) to analyze attribute properties of the neighborhoods, and mapping of crime hotspot trajectories onto different SOM visualizations. The case study uses burglary locations from Houston, Texas, from August to October 2005.

Publication Source (Journal or Book title)

Mitteilungen der Osterreichischen Geographischen Gesellschaft

Number

563

First Page

291

Last Page

304

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