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Spatio-temporal mixed membership models for criminal activity

Published version
Peer-reviewed

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Authors

Virtanen, S 

Abstract

jats:titleAbstract</jats:title> jats:pWe suggest a probabilistic approach to study crime data in London and highlight the benefits of defining a statistical joint crime distribution model which provides insights into urban criminal activity. This is achieved by developing a hierarchical mixture model for observations, crime occurrences over a geographical study area, that are grouped according to multiple time stamps and crime categories. The mixture components correspond to spatial crime distributions over the study area and the goal is to infer, based on the observations, how and to what degree the latent distributions are shared across the groups.</jats:p>

Description

Keywords

Bayesian statistics, high-dimensional data, latent factor models, multi-view modelling, spatial and temporal methods

Journal Title

Journal of the Royal Statistical Society. Series A: Statistics in Society

Conference Name

Journal ISSN

0964-1998
1467-985X

Volume Title

184

Publisher

Oxford University Press (OUP)
Sponsorship
EPSRC (EP/P020720/2)
EPSRC (EP/R018413/2)
Engineering and Physical Sciences Research Council (EP/R034710/1)
Royal Academy of Engineering (RAEng) (RCSRF\1718\6\34)