Data-Driven Approach to Assess Street Safety: Large-Scale Analysis of the Microscopic Design

Abstract

Safety is an important quality of street space that affects people’s psychological state and behavior in many ways. Previous large-scale assessment of street safety focuses more on social and physical factors and has less correlation with spatial design, especially the microscopic design. Limited by data and methods, street safety assessment related to microscopic design is mostly conducted on the small scale. Based on multisource big data, this study conducts a data-driven approach to assess the safety of street microscope design on a large scale from the perspective of individual perception. An assessment system including four dimensions of walkability, spatial enclosure, visual permeability, and vitality is constructed, which reflects the individual perceptions of the street space. Intraclass correlation coefficient (ICC) and location-based service (LBS) data are used to verify the effectiveness of the assessment method. The results show that multisource big data can effectively measure the physical elements and design features of streets, reflecting street users’ perception of vision, function, architecture, and street form, as well as the spatial selectivity based on their judgment of safety. The measurement of multidimensional connotations and the fusion of multiple data mining technologies promote the accuracy and effectiveness of the assessment method. Street safety presents the spatial distribution of high-value aggregation and low-value dispersion. Street safety is relatively low in areas with a large scale, lack of street interface, large amount of transit traffic, and high-density vegetation cover. The proposed method and the obtained results can be a reference for humanized street design and sustainable urban traffic planning and management.