Virtanen, S and Girolami, M (2021) Spatio‐temporal mixed membership models for criminal activity. Journal of the Royal Statistical Society Series A: Statistics in Society, 184 (4). pp. 1220-1244. ISSN 0964-1998
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Bayesian statistics, high-dimensional data, latent factor models, multi-view modelling, spatial and temporal methods |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Nov 2020 15:23 |
Last Modified: | 25 Jun 2023 22:30 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/rssa.12642 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168352 |