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 (8,300 square kilometres) in Northern England. A total of 60,100 images were automatically collected and analysed across the area where 5,288 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 (8,300 square kilometres), resulting in an overlap between specific features and multiple crime incidents. Furthermore, 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-discipline 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.
|Publication status||Accepted/In press - 28 Jun 2020|