Analyzing local spatio-temporal patterns of police calls-for-service using Bayesian integrated nested laplace approximation

Hui Luan, Matthew Quick, Jane Law

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00-03:59) and highest in the evening (20:00-21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

Original languageEnglish (US)
Article number162
JournalISPRS International Journal of Geo-Information
Volume5
Issue number9
DOIs
StatePublished - Sep 2016
Externally publishedYes

Keywords

  • Bayesian
  • Integrated Nested Laplace Approximation (INLA)
  • Law enforcement
  • Police calls-for-service
  • Spatio-temporal

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Analyzing local spatio-temporal patterns of police calls-for-service using Bayesian integrated nested laplace approximation'. Together they form a unique fingerprint.

Cite this