The spatiotemporal pattern of violent crime at multiple spatial scales: A Bayesian cross-classified modelling approach

Authors: Matthew Quick*, Arizona State University
Topics: Quantitative Methods, Spatial Analysis & Modeling
Keywords: spatial analysis, crime pattern, spatiotemporal, Bayesian modeling, multilevel model
Session Type: Paper
Day: 4/8/2020
Start / End Time: 1:45 PM / 3:00 PM
Room: Tower Court A, Sheraton, IM Pei Tower, Second Floor Level
Presentation File: No File Uploaded


Characteristics of the urban environment influence where and when crime events occur, however past studies often analyze cross-sectional data for one spatial scale and do not account for the processes and policies that influence crime across multiple scales. This research applies a Bayesian cross-classified multilevel modelling approach to examine the spatiotemporal patterning of violent crime in the Region of Waterloo, Ontario, Canada. Violent crime is measured at the small-area scale and small-areas are nested in three higher-level areas that have overlapping boundaries: neighborhoods, electoral wards, and patrol zones. Violent crime is found to be positively associated with population size, residential instability, the central business district, and commercial, government-institutional, and recreational land uses within small-areas Violent crime is also shown to be negatively associated with civic engagement within electoral wards. Combined, the three higher-level units explain approximately fifteen percent of the total spatiotemporal variation of violent crime. This research advances understanding of the multiscale processes influencing spatiotemporal crime patterns and provides area-specific information for policy-makers in urban planning, local government, and law enforcement.

Abstract Information

This abstract is already part of a session. View the session here.

To access contact information login