Exploring large-scale spatial distribution of fear of crime by integrating small sample surveys and massive street view images. (May 2023)

• Record Type: Journal Article
• Authors: Jing, Fengrui; Liu, Lin; Zhou, Suhong; Li, Zhenlong; Song, Jiangyu; Wang, Linsen; Ma, Ruofei; Li, Xiaoming
• Other Names: O'Clery Neave guest-editor.; Duque Juan Carlos guest-editor.; Alvanides Seraphim guest-editor.; Schwanen Tim guest-editor.
• Is Part Of: Environment & planning. Volume 50:Number 4(2023)
• Page Start: 1104
• Page End: 1120
• Publication Date: 2023-05
• Subjects: Street view imagery -- machine learning -- Guangzhou -- fear of crime -- mapping; City planning -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.11605
• DOI: 10.1177/23998083221135608
• Languages: English
• ISSNs: 2399-8083

Abstract

A tremendous amount of research use questionnaires to obtain individuals' fear of crime and aggregate it to the neighborhood level to measure the spatial distribution of fear of crime. However, the cost of using questionnaires to measure the large-scale spatial distribution of fear of crime is high. The built environment is known to influence people's perceptions, including fear of crime. This study develops a machine learning model to link built environment extracted from street view images to fear of crime obtained from questionnaires, and then applies this model to extrapolate fear of crime for neighborhoods without the questionnaires. Using massive street view images and a survey among 1, 741 residents in 80 neighborhoods in Guangzhou, China, this study developed a novel systematic approach to measuring large-scale spatial fear of crime at the neighborhood level for 1, 753 neighborhoods. This is the first study to measure fear of crime at the neighborhood level for a metropolitan area of nearly 20 million people. The integration of survey data and street view images provides an opportunity to develop a more effective way to measure the spatial distribution of fear of crime. This approach could be applied to map other types of perceptions at a spatial resolution of the neighborhood level.