This article investigates what causes an urban environment to be perceived as safe using Stockholm, the capital of Sweden, as the study area. The study integrates AI-generated safety scores from street view images, image segmentation techniques and conventional and crowdsourced data using Geographical Information Systems (GIS) and regression models. After accounting for income, crime and other area characteristics, the models reveal that areas with lower safety scores primarily consist of areas with a relatively large percentage of roads in industrial and/or interstitial mixed residential areas. Conversely, higher safety scores are found in large but distinct combinations of buildings, vegetation and open sky, from detached single-family housing to inner city high-density built areas. To enhance safety in an area, good contextual knowledge of the area is fundamental to prioritize interventions in interstitial mixed residential zones where roads and highways may be the dominant features.