Resumen
The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime.