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.

There is evidence that the built environment has an influence on street vitality. However, previous studies seldom assess the direct, indirect, and total effect of multiple environmental elements at the city level. In this study, the features of the street vitality on Xiamen Island are described based on the location-based service Big Data. Xiamen Island is the central urban area of Xiamen, one of the national central cities in China. With the help of multi-source data such as street view images, the condition of design that is difficult to effectively measure with traditional data can be better explored in detail on a macro scale. The built environment is measured through a 5D system at the city level, including Density, Diversity, Design, Destination accessibility, and Distance to transit. Spatial panel Durbin models are constructed to analyze the influence of the built environment on the street vitality on weekdays and weekends, and the direct, indirect, and total effects are evaluated. Results indicate that at the city level, the built environment plays a significant role in promoting street vitality. Functional density is not statistically significant. Most of the elements have spatial effects, except for several indicators in the condition of the design. Compared with the conclusions of previous studies, some indicators have different effects on different spatial scales. For instance, on the micro scale, greening can enhance the attractiveness of streets. However, on the macro scale, too much greening brings fewer functions along the street, which inhibits the street vitality. The condition of design has the greatest effect, followed by destination accessibility. The differences in the influences of weekdays and weekends are mainly caused by commuting behaviors. Most of the built environment elements have stronger effects on weekends, indicating that people interact with the environment more easily during this period.

Previous studies substantiate built environment influences street vitality. However, most of them focus on whether the built environmental elements have an influence on the street vitality, and ignore the spatiotemporal heterogeneity of the influences at the district scale. Using multisource big data, we comprehensively measure the street vitalities of different periods and the built environment in different dimensions on Xiamen Island. Geographically and temporally weighted regression (GTWR) models are constructed to systematically analyze the spatiotemporal heterogeneity of the influences of the built environment on the street vitality. Results show that the influence of function remains constant over time. Transit has the strongest effect on the improvement of street vitality during peak hours. The impact of design is strongest in the evening. The effect of accessibility gradually strengthened over time, reaching the highest in the evening. In terms of the spatial dimension, the heterogeneity brought about by the new and old urban areas is significant. The spatial heterogeneity of design’s influences is prominently brought about by large green lands and landscape streets. Density, bus station and spatial scale have strong temporal and spatial stability. Length is the most unstable during the weekdays. In order to maintain street vitality and form sustainable traffic, differentiated strategies of vitality enhancement should be formulated according to the locations and attributes of the streets.