Revitalizing historic districts: Identifying built environment predictors for street vibrancy based on urban sensor data

Vibrancy is indispensable and beneficial for revitalization of historic districts. Hence, identifying built environment predictors for vibrancy is of great interest to urban practitioners and policy makers. However, it is challenging. On the one hand, there is no consensus in selection of appropriate proxy for vibrancy. On the other hand, the built environment is multidimensional, but limited studies examined its impacts on vibrancy from different dimensions simultaneously. The Baitasi Area is a typical historic district in Beijing, China. In this study, on the basis of a long-term repeatedly measured dataset generated from the Citygrid sensors, we investigated the spatiotemporal distribution of street vibrancy in Baitasi Area and examined its built environment predictors in two seasons (i.e., summer/autumn and winter), with pedestrian volume as the proxy for vibrancy and built environment portrayed from four different dimensions (i.e., morphology, configuration, function, and landscape). We found that (1) the street vibrancy in Baitasi Area is temporally relatively evenly distributed, but with higher spatial concentration; (2) microclimate and built environment are more significant in winter than in summer/autumn; (3) street morphology and configuration features are more significant predictors than street function and landscape features; (4) generally, streets with higher point of interest (POI) diversity, higher buildings, and stronger network connection tend to have higher vibrancy. This study provides decision makers with insights in revitalizing historic districts.

Automated summary

The study investigated the spatiotemporal distribution of street vibrancy and its built environment predictors in the Baitasi Area of Beijing, China, during summer/autumn and winter seasons. It found that street vibrancy in this area is relatively evenly distributed over time but more spatially concentrated, with microclimate and built environment playing a larger role in winter. Street morphology and configuration features were found to be more significant predictors of vibrancy than street function and landscape features, with higher diversity in points of interest, taller buildings, and stronger network connections correlating with higher vibrancy levels. This research provides valuable insights for decision makers aiming to revitalize historic districts.