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Burglary in London: insights from statistical heterogeneous spatial point processes

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Title: Burglary in London: insights from statistical heterogeneous spatial point processes
Authors: Povala, J
Virtanen, S
Girolami, M
Item Type: Journal Article
Abstract: To obtain operational insights regarding the crime of burglary in London we consider the estimation of effects of covariates on the intensity of spatial point patterns. By taking into account localised properties of criminal behaviour, we propose a spatial extension to model-based clustering methods from the mixture modelling literature. The proposed Bayesian model is a finite mixture of Poisson generalised linear models such that each location is probabilistically assigned to one of the clusters. Each cluster is characterised by the regression coefficients which we subsequently use to interpret the localised effects of the covariates. Using a blocking structure of the study region, our approach allows specifying spatial dependence between nearby locations. We estimate the proposed model using Markov Chain Monte Carlo methods and provide a Python implementation.
Issue Date: 1-Nov-2020
Date of Acceptance: 12-Jun-2020
URI: http://hdl.handle.net/10044/1/80306
DOI: 10.1111/rssc.12431
ISSN: 0035-9254
Publisher: Wiley
Start Page: 1067
End Page: 1090
Journal / Book Title: Journal of the Royal Statistical Society Series C: Applied Statistics
Volume: 69
Issue: 5
Copyright Statement: © 2020 Royal Statistical Society. This is the peer reviewed version of the following article, which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12431. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/P020720/1
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Bayesian mixture model
Burglary
Crime model
Poisson process
Spatial heterogeneity
Spatial statistics
GAUSSIAN COX PROCESSES
CRIME
CLUSTERS
Spatial Analysis
Crime
Poisson process
spatial heterogeneity
spatial statistics
burglary
Bayesian mixture model
stat.AP
stat.AP
stat.CO
0104 Statistics
Statistics & Probability
Notes: 23 pages, 9 figures
Publication Status: Published
Online Publication Date: 2020-08-05
Appears in Collections:Mathematics
Faculty of Natural Sciences