Using Google Street View imagery to capture micro built environment characteristics in drug places, compared with street robbery

• Published in: Computers, environment and urban systems Vol. 88; p. 101631
• Main Authors: Zhou, Hanlin, Liu, Lin, Lan, Minxuan, Zhu, Weili, Song, Guangwen, Jing, Fengrui, Zhong, Yanran, Su, Zihan, Gu, Xin
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2021; Elsevier Science Ltd
• Subjects: Accessibility; Algorithms; Built environment; Crime; Drug-related calls for service; Imagery; Micro built environment; Regression models; Street robbery; Street view imagery; Traffic safety; Traffic signals; Traffic signs; Urban environments; Street robbery; Drug-related calls for service; Street view imagery; Micro built environment
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2021.101631

The drug-related problem poses a serious threat to human health and safety. Previous studies have associated drug places with factors related to place management and accessibility, often at several scattered places, as data at the micro level are hard to obtain at a city-wide scale. Google Street View imagery presents a new source for deriving micro built environment characteristics, including place management and accessibility in larger areas. In this study, we calculate an overall safety score by the Streetscore algorithm and extract physical elements at the address location by the Pyramid Scene Parsing Network (PSPNet) model from every Google Street View image. Additionally, to distinguish drug activities from other types of crime, we compare drug-related calls for service (CFS) data with street robbery incident data. We build the binary logistic regression models to assess the impact of the micro built environment variables on drug activities after controlling for other criminological elements pertaining to drug places. Results show that the safety score, traffic lights, and poles make statistically significant and negative (or deterring) impacts on drug activities, whilst traffic signs and roads make statistically significant and positive (or contributing) impacts. The positive impact of buildings is also notable as its p-value is slightly over 0.05. This study provides evidence at the micro level that less place management and higher accessibility can increase the risk of drug activities. These street-view variables may be generally applicable to other types of crime research in the context of the micro built environment. •Utilize the street view imagery to study drug activities•Compare and discern differences in the micro built environment characteristics between drug activities and street robberies•Reveal that less place management and higher accessibility increase the risk of drug activities at the micro level•Confirm that variables derived from the street view imagery can effectively represent built environments for crime research.