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dc.contributor.authorRistea, Alinaen_NZ
dc.contributor.authorKurland, Justinen_NZ
dc.contributor.authorResch, Bernden_NZ
dc.contributor.authorLeitner, Michaelen_NZ
dc.contributor.authorLangford, Chaden_NZ
dc.date.accessioned2018-03-20T22:08:57Z
dc.date.available2018en_NZ
dc.date.available2018-03-20T22:08:57Z
dc.date.issued2018en_NZ
dc.identifier.citationRistea, A., Kurland, J., Resch, B., Leitner, M., & Langford, C. (2018). Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets. ISPRS International Journal of Geo-Information, 7(2). https://doi.org/10.3390/ijgi7020043en
dc.identifier.issn2220-9964en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/11739
dc.description.abstractCrowd-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.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherMDPI AGen_NZ
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.subjectcomputer scienceen_NZ
dc.subjectspatial crime analysisen_NZ
dc.subjectTwitter miningen_NZ
dc.subjectfootball related crime and disorderen_NZ
dc.subjectspatial correlationen_NZ
dc.subjectexplanatory analyticsen_NZ
dc.titleEstimating the spatial distribution of crime events around a football stadium from georeferenced tweetsen_NZ
dc.typeJournal Article
dc.identifier.doi10.3390/ijgi7020043en_NZ
dc.relation.isPartOfISPRS International Journal of Geo-Informationen_NZ
pubs.elements-id219830
pubs.issue2en_NZ
pubs.volume7en_NZ
uow.identifier.article-no43


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