Enriching geospatial data with computer vision to identify urban environment determinants of social interactions

Abstract. Characteristics of urban space (co-)determine human behaviour, including their social interaction patterns. However, despite numerous studies that have examined how the urban space impacts social interactions, their relationships are still poorly understood. Recent developments in computer vision and machine learning fields offer promising new ways to analyse and collect data on social interactions. This study proposes a new computer vision-based approach to study how the urban space impacts social interactions. The proposed method uses pre-trained object detection models to detect social interactions (including their geo-locations) from street-view imagery. After that, it regresses urban space characteristics – which are also detected using object detection models – on social interactions. For this study, 294,852 street-level images from three Dutch cities are analysed. Results from linear regression analysis show that for these three Dutch cities people tend to meet in places with a strong presence of recreational attractions and bicycles. Also, the results of data collection and image processing can be used to identify the areas most likely to produce social interactions in urban space to conduct urban studies.