Spatial crime distribution and prediction for sporting events using social media |
| |
| Authors: | Alina Ristea Mohammad Al Boni Bernd Resch Matthew S Gerber Michael Leitner |
| |
| Institution: | 1. Department of Geoinformatics, Doctoral College GIScience, University of Salzburg , Salzburg, Austria;2. Boston Area Research Initiative, School of Public Policy and Urban Affairs, Northeastern University , Boston, MA, USA a.ristea@northeastern.edu https://orcid.org/0000-0003-2682-1416;4. Product and Analytics, CyberCube , San Francisco, CA, USA;5. Department of Geoinformatics, University of Salzburg , Salzburg, Austria;6. Center for Geographic Analysis, Harvard University , Cambridge, MA, USA https://orcid.org/0000-0002-2233-6926;7. Department of Systems and Information Engineering, University of Virginia , Charlottesville, VA, USA;8. Department of Geography and Anthropology, Louisiana State University , Baton Rouge, LA, USA https://orcid.org/0000-0002-1204-0822 |
| |
| Abstract: | ABSTRACT 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. |
| |
| Keywords: | Crime prediction local kernel density estimation violent tweets |
|
|
![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0003-2682-1416;4. Product and Analytics, CyberCube , San Francisco, CA, USA;5. Department of Geoinformatics, University of Salzburg , Salzburg, Austria;6. Center for Geographic Analysis, Harvard University , Cambridge, MA, USA