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ISPRS Int. J. Geo-Inf. 2016, 5(9), 162; doi:10.3390/ijgi5090162

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

1,* , 1,* and 1,2
1
School of Planning, Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
2
School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
*
Authors to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
Received: 28 April 2016 / Revised: 23 August 2016 / Accepted: 1 September 2016 / Published: 9 September 2016
(This article belongs to the Special Issue Frontiers in Spatial and Spatiotemporal Crime Analytics)
View Full-Text   |   Download PDF [4598 KB, uploaded 9 September 2016]   |  

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. View Full-Text
Keywords: spatio-temporal; law enforcement; police calls-for-service; Bayesian; Integrated Nested Laplace Approximation (INLA) spatio-temporal; law enforcement; police calls-for-service; Bayesian; Integrated Nested Laplace Approximation (INLA)
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MDPI and ACS Style

Luan, H.; Quick, M.; Law, J. Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation. ISPRS Int. J. Geo-Inf. 2016, 5, 162.

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