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Identifying Optimal Scales for Spatio-temporal Crime Clusters

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 Green open access

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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.

Type: Proceedings paper
Title: Identifying Optimal Scales for Spatio-temporal Crime Clusters
Event: Geographical Information Science Research- UK (GISRUK) 2020
Location: London
Dates: 21 July 2020 - 23 July 2020
Open access status: An open access version is available from UCL Discovery
Publisher version: http://london.gisruk.org/gisruk2020_proceedings/GI...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Spatio-temporal clustering, crime hotspot, near-repeat victimisation, density-based, clustering, ST-OPTICS
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10114785
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