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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Article number103305
Journal / PublicationCities
Online published16 Jun 2021
Publication statusPublished - Oct 2021


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.

Research Area(s)

  • Beijing, China, Built environment, Historic district, Pedestrian volume, Urban sensor data, Urban vibrancy