Spatial pattern and influencing factors of fear of crime and their differences with those of criminal activities:Application of deep learning algorithm to street view images in Beijing
Research on crime geography has traditionally focused on objective criminal activities and neglected subjective perceptions such as fear of crime,which is its major shortcoming.Using the deep learning algorithm of image regression,we analyzed the level of fear of crime under different streetscape environments on a large scale in the central urban area of Beijing.This approach compensates for the limitations of social surveys in terms of spatial coverage,spatial resolution,and reliability and validity of measures.Our indigenous deep learning models also make up for the lack of established models that rely on western city street view images and overseas labelers.Our study shows that,first,the spatial pattern of fear of crime has a circular,multi-cluster,and radial structure,and its level gradually increases from the city center to the suburbs.In contrast,the density/number of theft and violent crimes have the spatial distribution with the opposite trend.Second,according to the relationship between the spatial distribution of fear of crime and criminal activities in general,we find a low match between subjective and objective security in the city center,with the objective situation being more dangerous than the subjective perception;in the suburban areas,the degree of match between the two increases;to the outer suburbs,these two still have a low degree of match,but the objective situation is safer than the subjective perception.Third,the built and social environmental factors that influence subjective and objective security are not always identical.A high-density and highly mixed environment would reduce fear of crime,but may accelerate crime.To reduce both fear and crime,it is recommended to add cul-de-sacs,improve the sense of enclosure,increase the amount of greenery,and eliminate various types of physical disorder.As for the effect of social disorganization,concentrated disadvantaged communities tend to have high levels of fear and are more prone to violent crime;population mobility can help reduce fear of crime;residential heterogeneity would exacerbate criminal behavior.Our findings help to clarify the differential explanatory power of classical crime geography theory for subjective and objective security,which in turn facilitates a comprehensive assessment of the security consequences of environmental intervention policies.
fear of crimebuilt environmentsocial environmentstreet view imagedeep learning algorithm
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