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Spatial crime distribution and prediction for sporting events using social media

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-econ...

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Autores principales: Ristea, Alina, Al Boni, Mohammad, Resch, Bernd, Gerber, Matthew S., Leitner, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455052/
https://www.ncbi.nlm.nih.gov/pubmed/32939153
http://dx.doi.org/10.1080/13658816.2020.1719495
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author Ristea, Alina
Al Boni, Mohammad
Resch, Bernd
Gerber, Matthew S.
Leitner, Michael
author_facet Ristea, Alina
Al Boni, Mohammad
Resch, Bernd
Gerber, Matthew S.
Leitner, Michael
author_sort Ristea, Alina
collection PubMed
description Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.
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spelling pubmed-74550522020-09-14 Spatial crime distribution and prediction for sporting events using social media Ristea, Alina Al Boni, Mohammad Resch, Bernd Gerber, Matthew S. Leitner, Michael Int J Geogr Inf Sci Research Articles Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement. Taylor & Francis 2020-02-06 /pmc/articles/PMC7455052/ /pubmed/32939153 http://dx.doi.org/10.1080/13658816.2020.1719495 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Ristea, Alina
Al Boni, Mohammad
Resch, Bernd
Gerber, Matthew S.
Leitner, Michael
Spatial crime distribution and prediction for sporting events using social media
title Spatial crime distribution and prediction for sporting events using social media
title_full Spatial crime distribution and prediction for sporting events using social media
title_fullStr Spatial crime distribution and prediction for sporting events using social media
title_full_unstemmed Spatial crime distribution and prediction for sporting events using social media
title_short Spatial crime distribution and prediction for sporting events using social media
title_sort spatial crime distribution and prediction for sporting events using social media
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7455052/
https://www.ncbi.nlm.nih.gov/pubmed/32939153
http://dx.doi.org/10.1080/13658816.2020.1719495
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