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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
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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. |
format | Online Article Text |
id | pubmed-7455052 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
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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