The Nonlinear Relationship and Synergistic Effects between Built Environment and Urban Vitality at the Neighborhood Scale: A Case Study of Guangzhou’s Central Urban Area

Abstract

Investigating urban vitality and comprehending the influence mechanisms of the built environment is essential for achieving sustainable urban growth and improving the quality of life for residents. Current research has rarely addressed the nonlinear relationships and synergistic effects between urban vitality and the built environment at the neighborhood scale. This oversight may overlook the influence of key neighborhoods and overestimate or underestimate the influence of different factors on urban vitality. Using Guangzhou’s central urban area as a case study, this research develops a comprehensive urban vitality assessment system that includes economic, social, cultural, and ecological dimensions, utilizing multi-source data such as POI, Dazhong Dianping, Baidu heatmap, and NDVI. Additionally, the XGBoost-SHAP model is applied to uncover the nonlinear impacts of different built environment factors on neighborhood vitality. The findings reveal that: (1) urban vitality diminishes progressively from the center to the periphery; (2) proximity to Zhujiang New Town is the most critical factor for neighborhood vitality (with a contribution of 0.039), while functional diversity and public facility accessibility are also significant (with contributions ranging from 0.033 to 0.009); (3) built environment factors exert nonlinear influences on neighborhood vitality, notably with a threshold effect for subway station accessibility (feature value of 0.1); (4) there are notable synergistic effects among different built environment dimensions. For example, neighborhoods close to Zhujiang New Town (feature value below 0.12) with high POI density (feature value above 0.04) experience significant positive synergistic effects. These findings can inform targeted policy recommendations for precise urban planning.

Keywords

• neighborhood vitality
• nonlinear relationships
• synergistic effects
• built environment
• XGBoost-SHAP model