Do Vibrant Places Promote Active Living? Analyzing Local Vibrancy, Running Activity, and Real Estate Prices in Beijing

Abstract

Although extensive research has investigated urban vibrancy as a critical indicator for spatial planning, urban design, and economic development, the unclear relationship between local vibrancy and active living needs to be clarified and requires more in-depth analysis. This study localizes urban vibrancy at both hyper-local and neighborhood scales by integrating high-resolution, large-scale, and heterogeneous urban datasets and analyzing interactions among variables representing vibrancy's environmental, economic, and social aspects. We utilize publicly available urban open data, Points of Interest requested from API, and leisure running trajectories acquired through data mining to investigate the spatial distribution of various vibrancy indicators and how they interact with physical activity at the local scale. Based on these variables, we then construct linear regression models and Geographically Weighted Regression (GWR) models to test and estimate how local vibrancy and physical activity relate to residential real estate characteristics. The results reveal the strong impact of urban form on local vibrancy but not physical activeness. At the neighborhood level, all vibrancy factors are statistically significant to local residential real estate prices but with different interactions based on location. Our study highlights the importance of accounting for locality and different physical, environmental, social, and economic factors when analyzing and interpreting urban vibrancy at a granular scale within a city.

Keywords: neighborhood development; physical activity; urban analytics; urban vibrancy.

Figure 1

The research framework for this study.

Figure 2

Visualization of extracted daily running trajectories (n = 18,683) data within Beijing’s sixth ring road on 15 August 2022.

Figure 3

Localized physical activity and vibrancy estimation based on 1 square kilometer grid.

Figure 4

Aggregated physical activity and vibrancy measures at the sub-district level.

Figure 5

Time series of average hourly patterns of physical activity grouped by live/work mix and PoI density.

Figure 6

Real estate compounds (n = 6678) within sixth ring road in Beijing with the spatial grid (n = 9251) and sub-districts (n = 167).

Figure 7

Local coefficient distribution and spatial patterns for vibrancy indicators and physical activity at neighborhood scale.

Figure 8

Querying residential apartment compounds with mismatched vibrancy characteristics. (a) Most residential with the lowest facility; (b) highest activeness with the lowest space quality.