Spatial crime distribution and prediction for sporting events using social media

Alina Ristea,Bernd Resch,Matthew S. Gerber,Mohammad Al Boni,Michael Leitner

doi:10.1080/13658816.2020.1719495

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.

Highlights

With massive social data available, research directions are spreading and changing views of politics, health, education, social and behavioral sciences
While the crime prediction models outlined in the literature include historical crime data, demographics, socio-economic, and built environment data as explanatory variables, this study proposes the integration of geo-located Twitter data and a subset of violent tweets as dynamic data for higher predictive accuracy
Findings from this study suggest that using Twitter data can have a significant influence on building predictive models for seven crime types, when used in conjunction with their time stamps, into spatial prediction models

Summary

Introduction

With massive social data available, research directions are spreading and changing views of politics, health, education, social and behavioral sciences. Research shows that sporting events attract a high volume of people in specific activity nodes, such as sporting arenas or in pubs or bars to watch the games. These activity nodes can be criminogenic places, defined as crime attractors or generators (Brantingham and Brantingham 1993, 1995). A higher number of people use transportation routes that separate them from their normal routine trajectories All these changes have an influence on specific crime types because of fan behavior (Montolio and Planells 2016) or hooliganism (Caruso and Di Domizio 2013)

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