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dc.contributor.authorDakin, Kyle
dc.contributor.authorParkinson, Simon
dc.contributor.authorSaad, Kahn
dc.contributor.authorMonchuck, Leanne
dc.contributor.authorPease, Ken
dc.date.accessioned2020-07-10T15:55:17Z
dc.date.available2020-07-10T15:55:17Z
dc.date.issued2020-07-08
dc.identifier.citationDakin, K., Xie, W., Parkinson, S., Khan, S., Monchuk, L. and Pease, K., (2020). 'Built environment attributes and crime: An automated machine learning approach'. Crime Science, 12, pp. 1-17.en_US
dc.identifier.doi10.1186/s40163-020-00122-9
dc.identifier.urihttp://hdl.handle.net/10545/624980
dc.description.abstractThis paper presents the development of an automated machine learning approach to gain an understanding of the built environment and its relationship to crime. This involves the automatic capture of street-level photographs using Google Street View (GSV), followed by the use of supervised machine learning techniques (specifically image feature recognition) to recognise features of the built environment. In this exploratory proof-of-concept work, 8 key features (building, door, fence, streetlight, tree, window, hedge, and garage) are considered and a worked case-study is demonstrated for a small geographical area (8300 square kilometres) in Northern England. A total of 60,100 images were automatically collected and analysed across the area where 5288 crime incidents were reported over a twelve- month period. Dependency between features and crime incidents are measured; however, no strong correlation has been identified. This is unsurprisingly considering the high number of crime incidents in a small geographic region (8300 square kilometres), resulting in an overlap between specific features and multiple crime incidents. Further- more, due to the unknown precise location of crime instances, an approximation technique is developed to survey a crime’s local proximity. Despite the absence of a strong correlation, this paper presents a first-of-a-kind cross-disci- pline approach to attempt and use computation techniques to produce new empirical knowledge. There are many avenues of future research in this fertile and important area.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherBMCen_US
dc.relation.urlhttps://crimesciencejournal.biomedcentral.com/articles/10.1186/s40163-020-00122-9#citeasen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject: Crime prevention, Supervised machine learning, Feature recognition, Crime analyticsen_US
dc.titleBuilt environment attributes and crime: an automated machine learning approachen_US
dc.typeArticleen_US
dc.identifier.eissn2193-7680
dc.contributor.departmentUniversity of Huddersfielden_US
dc.contributor.departmentUniversity of Derbyen_US
dc.identifier.journalCrime Scienceen_US
dcterms.dateAccepted2020-06-02
dc.author.detail786018en_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International