Space-time analysis of auto burglary patterns in a fast-growing small city

Ling Wu, Xinyue Ye, David Webb

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Auto burglary is a rising concern for small cities that rely so highly on revenues from malls and shopping. The fear of auto burglary dispels possible business partners, shoppers, and workers. This piece of research was conducted in Shenandoah, a small city near Houston, Texas. Shenandoah has been experiencing a fast- growing economy and annexation process. The research highlights the potency of space-time analysis for the local police department. The paper describes the temporal trends of auto burglary offenses. Hot spots of auto burglary offenses in different time periods are identified, which provide a clue for police to prioritize limited resources. In addition, this project analyzes the repeatability of auto burglary incidents in the same locations. The space-time analysis reveals that, once an auto burglary incident occurred, one week is the optimal time period for police to actively patrol, or adopt other preventive strategies on the same location to deter the potential follow-up auto burglaries.

Original languageEnglish (US)
Pages (from-to)69-86
Number of pages18
JournalInternational Journal of Applied Geospatial Research
Volume3
Issue number4
DOIs
StatePublished - Oct 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Earth and Planetary Sciences (miscellaneous)

Keywords

  • Auto Burglary
  • Burglary Patterns
  • Cluster
  • Hot Spots
  • Space-Time Analysis

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