Chen, T; Cheng, T; (2020) Identifying Optimal Scales for Spatio-temporal Crime Clusters. In: Proceedings of the 28th Geographical Information Science Research conference: GISRUK 2020. (pp. p. 91). GISRUK

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Abstract

The spatial and temporal scales are not only two essential parameters for the spatio-temporal clustering algorithm to generate the crime clusters but are significantly helpful for determining the interventive distance at space and time in place-based crime prevention. This study presents the issue of identifying the optimal spatial-temporal scale when examining the micro-level crime clusters approached by density-based spatio-temporal clustering methods. The approach comprises adopting a clustering evaluation index to examine the performance of different clustering results from a range of space and time values iteration. For this purpose, two types of density-based clustering algorithms called ST-DBSCAN and ST-OPTICS are compared to determine the optimal scales for space-time crime clusters. A case study is demonstrated using individual crime records of burglary from Vancouver, Canada in 2010. Several derived results are significant. First, appropriate scales – 500m and 3 days can be distinctively determined by clustering algorithm ST-OPTICS from our tested parameters. Second, the narrowed scales were found in this study significantly for spatio-temporal crime clusters, which can help to develop a more focused and specific policing tactics.

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