Next Article in Journal
The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization
Previous Article in Journal
Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(2), 43; https://doi.org/10.3390/ijgi7020043

Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets

1
Doctoral College GIScience, Department of Geoinformatics-Z_GIS, University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria
2
Computing and Mathematical Sciences—Institute for Security and Crime Science, University of Waikato, Gate 1 Knighton Road, Private Bag 3105, Hamilton 3240, New Zealand
3
Center for Geographic Analysis, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138, USA
4
Department of Geography and Anthropology, Louisiana State University, E-104 Howe-Russell-Kniffen Geoscience Complex, Baton Rouge, LA 70803, USA
*
Author to whom correspondence should be addressed.
Received: 30 November 2017 / Revised: 19 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
Full-Text   |   PDF [4174 KB, uploaded 31 January 2018]   |  

Abstract

Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime. View Full-Text
Keywords: spatial crime analysis; Twitter mining; football related crime and disorder; spatial correlation; explanatory analytics spatial crime analysis; Twitter mining; football related crime and disorder; spatial correlation; explanatory analytics
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ristea, A.; Kurland, J.; Resch, B.; Leitner, M.; Langford, C. Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets. ISPRS Int. J. Geo-Inf. 2018, 7, 43.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top