Abstract
Abstract We 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.
Highlights
Increasing numbers of the population concentrate in cities
We develop and apply useful and important improvements for the ubiquitous crime mapping approach that is currently widely in use both in academic research and in practice, highlighting that our work has high impact for research and society
Our model builds on the hotspot property of crime, near-repeat pattern theory and regression modelling
Summary
Increasing numbers of the population concentrate in cities. Understanding the behaviour of crime and underlying urban structures that explain city-based crime is of high relevance for improving societal well being, city planning, developing smart cities and policy making. Crime mapping forms the backbone of data-driven crime modelling for exploring and explaining crime, aiming to infer crime distributions or risk surfaces, based on the occurrences. Some of these techniques have been deployed by police (Perry, 2013), leading to significant decreases in crime (Braga et al, 2014). We collect spatio-temporal crime occurrence data from the UK police for London for five crime categories including burglary, robbery, violence and sexual offences, vehicle crime and criminal damage and arson, as defined by the data provider. Actual crime occurrences are discretised based on offence categorisation as well as spatial and temporal partitions by the data provider. We see that crime is sparse and clusters into hot and cold spots, indicated
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