Predicting city safety perception based on visual image content
This addresses the need for objective safety assessment in cities, which has social and economic implications, but it is incremental as it applies existing methods to a specific domain.
The paper tackles the problem of measuring safety perception in urban environments by using image processing and machine learning to detect patterns that affect citizens' perceptions, achieving high accuracy.
Safety perception measurement has been a subject of interest in many cities of the world. This is due to its social relevance, and to its effect on some local economic activities. Even though people safety perception is a subjective topic, sometimes it is possible to find out common patterns given a restricted geographical and sociocultural context. This paper presents an approach that makes use of image processing and machine learning techniques to detect with high accuracy urban environment patterns that could affect citizen's safety perception.