Spatial crime distribution and prediction for sporting events using social media. Issue 9 (1st September 2020)

• Record Type: Journal Article
• Authors: Ristea, Alina; Al Boni, Mohammad; Resch, Bernd; Gerber, Matthew S.; Leitner, Michael
• Is Part Of: International journal of geographical information science. Volume 34:Issue 9(2020)
• Page Start: 1708
• Page End: 1739
• Publication Date: 2020-09-01
• Subjects: Crime prediction -- local kernel density estimation -- violent tweets; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2020.1719495
• Languages: English
• ISSNs: 1365-8816

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.