Rating places and crime prevention: Exploring user-generated ratings to assess place management

• Published in: Computers, environment and urban systems Vol. 109; p. 102088
• Main Authors: Snaphaan, Thom, Hardyns, Wim, Pauwels, Lieven J.R., Bowers, Kate
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-04-2024
• Subjects: Big data; Crime concentrations; Google Places; Place management; Situational crime prevention; Google Places; Place management; Big data; Situational crime prevention; Crime concentrations
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2024.102088

This study assesses how the quality of place management (measured with user-generated ratings from Google Places) is related to crime occurrences at specific settings and whether specific crime types are related to specific types of places. In 50 randomly sampled neighborhoods in Ghent (Belgium) and London (United Kingdom), we analyzed Google Places data as a proxy measure for the quality of place management at the street segment level. We used hurdle models to examine the effects for both the prevalence and frequency of crime at micro places, and to deal with excess zeros in the data. User-generated ratings of places provide a useful place-level indicator for place management that are related to crime. However, contextual differences are found between Ghent and London. For London, the results suggests that higher quality of place management has a protective effect on crime occurrences at the street segment level. This study indicates the importance of exploring new and emerging data sources as unique measurement opportunities to enhance insight in crime prevention mechanisms, and also acknowledges its limitations. For the first time from a large-scale empirical perspective, this study suggests that improving place management at specific places might be an effective intervention to guard against crime.
•This is the first study that assesses place management as a mechanism for crime prevention by means of Google Places data.•Google Places provide detailed, fine-grained and large-scale georeferenced data on non-residential facilities•Google Places' ratings provide an abstract but apparently valid measure of place management•This study introduces a sound computational method to extract Google Places data for larger areas