Visualising crime clusters in a space-time cube: An exploratory data-analysis approach using space-time kernel density estimation and scan statistics

Tomoki Nakaya, Keiji Yano

Research output: Contribution to journalArticlepeer-review

163 Citations (Scopus)

Abstract

For an effective interpretation of spatio-temporal patterns of crime clusters/hotspots, we explore the possibility of three-dimensional mapping of crime events in a space-time cube with the aid of space-time variants of kernel density estimation and scan statistics. Using the crime occurrence dataset of snatch-and-run offences in Kyoto City from 2003 to 2004, we confirm that the proposed methodology enables simultaneous visualisation of the geographical extent and duration of crime clusters, by which stable and transient space-time crime clusters can be intuitively differentiated. Also, the combined use of the two statistical techniques revealed temporal inter-cluster associations showing that transient clusters alternatively appeared in a pair of hotspot regions, suggesting a new type of "displacement" phenomenon of crime. Highlighting the complementary aspects of the two space-time statistical approaches, we conclude that combining these approaches in a space-time cube display is particularly valuable for a spatio-temporal exploratory data analysis of clusters to extract new knowledge of crime epidemiology from a data set of space-time crime events.

Original languageEnglish
Pages (from-to)223-239
Number of pages17
JournalTransactions in GIS
Volume14
Issue number3
DOIs
Publication statusPublished - 2010 Jun 1
Externally publishedYes

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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