TY - JOUR
T1 - Analyzing local spatio-temporal patterns of police calls-for-service using Bayesian integrated nested laplace approximation
AU - Luan, Hui
AU - Quick, Matthew
AU - Law, Jane
N1 - Funding Information:
This research was supported by the Social Sciences and Humanities Resource Council of Canada Grant 767-2013-1540. Hui Luan is grateful to the Chinese Scholarship Council (CSC) for supporting his doctoral research at the University ofWaterloo.
PY - 2016/9
Y1 - 2016/9
N2 - 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.
AB - 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.
KW - Bayesian
KW - Integrated Nested Laplace Approximation (INLA)
KW - Law enforcement
KW - Police calls-for-service
KW - Spatio-temporal
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U2 - 10.3390/ijgi5090162
DO - 10.3390/ijgi5090162
M3 - Article
AN - SCOPUS:84994235445
SN - 2220-9964
VL - 5
JO - ISPRS International Journal of Geo-Information
JF - ISPRS International Journal of Geo-Information
IS - 9
M1 - 162
ER -