Built environment attributes and crime: an automated machine learning approach

Journal article


Dakin, Kyle, Parkinson, Simon, Saad, Kahn, Monchuck, Leanne and Pease, Ken 2020. Built environment attributes and crime: an automated machine learning approach. Crime Science. https://doi.org/10.1186/s40163-020-00122-9
AuthorsDakin, Kyle, Parkinson, Simon, Saad, Kahn, Monchuck, Leanne and Pease, Ken
Abstract

This 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.

Keywords: Crime prevention, Supervised machine learning, Feature recognition, Crime analytics
Year2020
JournalCrime Science
PublisherBMC
ISSN2193-7680
Digital Object Identifier (DOI)https://doi.org/10.1186/s40163-020-00122-9
Web address (URL)http://hdl.handle.net/10545/624980
http://creativecommons.org/licenses/by/4.0/
hdl:10545/624980
Publication dates08 Jul 2020
Publication process dates
Deposited10 Jul 2020, 15:55
Accepted02 Jun 2020
Rights

Attribution 4.0 International

ContributorsUniversity of Huddersfield and University of Derby
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