Integrating near repeat and social network approaches to analyze crime patterns

Tao Hu, Xinyue Ye, Lian Duan, Xinyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Crime does not occur randomly or uniformly across time, space, or social groups. The near-repeat phenomenon states that the risk of repeat victimization would increase at the nearby locations within a certain time period. The empirical evidence of near-repeat patterns has been widely reported in many different crime types across the world. Meanwhile, the growing ability to connect anywhere and anytime has led to the increasing use of social network analytics. In particular, social network analytics have been applied to the studies of criminal networks. This paper integrates near repeat and social network approaches on exploring spatiotemporal crime patterns. The spatiotemporal burglary data in five boroughs (the Bronx, Brooklyn, Staten Island, Manhattan and Queen) of NYC were analyzed and compared based on average clustering coefficient, degree centrality, closeness centrality, and closeness centrality. Furthermore, the implications and limitations of the findings are presented.

Original languageEnglish (US)
Title of host publication2017 25th International Conference on Geoinformatics, GeoInformatics 2017
PublisherIEEE Computer Society
Volume2017-August
ISBN (Electronic)9781538622667
DOIs
StatePublished - Oct 30 2017
Externally publishedYes
Event25th International Conference on Geoinformatics, GeoInformatics 2017 - Buffalo, United States
Duration: Aug 2 2017Aug 4 2017

Conference

Conference25th International Conference on Geoinformatics, GeoInformatics 2017
Country/TerritoryUnited States
CityBuffalo
Period8/2/178/4/17

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Geography, Planning and Development
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • buglary
  • crime analysis
  • near repeat
  • social network

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