Exploring non-linear influences of urban environment on urban vitality using multi-source geographic big data

Abstract

Urban vitality (UV) is a crucial indicator of the prosperity, livability, and sustainability of cities, yet its complex and non-linear relationship with the urban environment (UE) remains underexplored. Therefore, taking Nanjing as the study area, this study integrated multi-source data of mobile phone signaling, POI, street view images (SVI), and building footprints, etc. First, SVI features were extracted by the DeepLab V3+ model to reflect human perception visually, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed to quantitatively assess UV from three dimensions of population activity, space quality, and zonal connection. Then, the Geographically Weighted Random Forest (GWRF) and Gaussian Constraint Line (GCL) methods were combined to explore the non-linear influences and threshold effects of 16 built-up and ecological environmental indices on UV. The results demonstrated that: (1) UV in Nanjing exhibited a clear gradient decline from the center to the periphery, with the highest UV in central regions such as Xuanwu and Qinhuai districts, accounting for over 20%, and the lowest UV in fringe regions such as Gaochun and Luhe districts. (2) All UE indices exhibited pronounced spatial heterogeneity of non-linear influences on UV by pseudo t-values from GWRF, where four indices (e.g. building height and transportation pollution) displayed predominantly positive effects and three indices (e.g. building quality and carbon emission) showed mainly negative effects. (3) Except for building age, the other 15 UE indices exhibited significant inverted-U threshold effects, with high reliability and robustness by acceptable R2 and χ2 values, while thresholds of building height, land development intensity, and land surface temperature were 14.54 m, 0.63, and 22.19°C, respectively. Further experiments showed high segmentation accuracy of SVI features with an F1-score larger than 0.68 to significantly improve UV assessment, and synergistic effects among UE indices. This proposed framework exploring the UE-UV non-linear relationship could help to realize scientific spatial planning and sustainable urban management.