Uncovering dynamic textual topics that explain crime

Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding...

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Autor principal:	Seppo Virtanen
Formato:	article
Lenguaje:	EN
Publicado:	The Royal Society 2021
Materias:	topic modelling matrix factorization temporal/dynamic methods crime analysis Science Q
Acceso en línea:	https://doaj.org/article/326deb10c5ea4b6c99c11fb5c0ceb0f4
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spelling	oai:doaj.org-article:326deb10c5ea4b6c99c11fb5c0ceb0f42021-12-01T08:05:34ZUncovering dynamic textual topics that explain crime10.1098/rsos.2107502054-5703https://doaj.org/article/326deb10c5ea4b6c99c11fb5c0ceb0f42021-12-01T00:00:00Zhttps://royalsocietypublishing.org/doi/10.1098/rsos.210750https://doaj.org/toc/2054-5703Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.Seppo VirtanenThe Royal Societyarticletopic modellingmatrix factorizationtemporal/dynamic methodscrime analysisScienceQENRoyal Society Open Science, Vol 8, Iss 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	topic modelling matrix factorization temporal/dynamic methods crime analysis Science Q
spellingShingle	topic modelling matrix factorization temporal/dynamic methods crime analysis Science Q Seppo Virtanen Uncovering dynamic textual topics that explain crime
description	Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain. Some of these techniques may provide insights for understanding crime and disorder, especially, via interpreting the weights for the spatial covariates based on regression modelling. However, to date, the use of temporal covariates for the time domain has not played a significant role in the analysis. In this work, we collect time-stamped crime-related news articles, infer crime topics or themes based on the collection and associate the topics with the historical numeric crime counts. We provide a proof-of-concept study, where instead of adopting spatial covariates, we focus on temporal (or dynamic) covariates and assess their utility. We present a novel joint model tailored for the crime articles and counts such that the temporal covariates (latent variables, more generally) are inferred based on the data sources. We apply the model for violent crime in London.
format	article
author	Seppo Virtanen
author_facet	Seppo Virtanen
author_sort	Seppo Virtanen
title	Uncovering dynamic textual topics that explain crime
title_short	Uncovering dynamic textual topics that explain crime
title_full	Uncovering dynamic textual topics that explain crime
title_fullStr	Uncovering dynamic textual topics that explain crime
title_full_unstemmed	Uncovering dynamic textual topics that explain crime
title_sort	uncovering dynamic textual topics that explain crime
publisher	The Royal Society
publishDate	2021
url	https://doaj.org/article/326deb10c5ea4b6c99c11fb5c0ceb0f4
work_keys_str_mv	AT seppovirtanen uncoveringdynamictextualtopicsthatexplaincrime
_version_	1718405403772977152

Uncovering dynamic textual topics that explain crime

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