Burglary in London: insights from statistical heterogeneous spatial point processes
File(s)
Accepted version
Supporting information
Author(s)
Povala, Jan
Virtanen, Seppo
Girolami, Mark
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.
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.
Date Issued
2020-11-01
Date Acceptance
2020-06-12
Citation
Journal of the Royal Statistical Society Series C: Applied Statistics, 2020, 69 (5), pp.1067-1090
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
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://arxiv.org/abs/1910.05212
Grant Number
EP/P020720/1
Subjects
Spatial Analysis
Crime
Poisson process
spatial heterogeneity
spatial statistics
burglary
Bayesian mixture model
Notes
23 pages, 9 figures
Publication Status
Published
Date Publish Online
2020-08-05