Exploring the Spatial Impact of Multisource Data on Urban Vitality: A Causal Machine Learning Method

Abstract

Identifying urban vitality is the key to optimizing the urban structure. Previous studies on urban multisource data and urban vitality often assume that they follow a predefined (linear or nonlinear in terms of parameters) relationship, and few studies have explored the causality of urban multisource data on urban vitality. The existing machine learning methods often pay attention to the correlation in the data and ignore the causality. With the continuous emergence of new needs, its disadvantages gradually begin to appear and face a series of urgent problems in interpretability, robustness, and fairness. In this paper, we use a combination of causal inference and machine learning to deeply explore and analyze the causal effects of multisource data on the 16 administrative districts of Shanghai, taking Shanghai as an example. The analysis results show that each data indicator has different degrees of influence on the urban vitality of the 16 administrative districts of Shanghai, resulting in different heterogeneous effects, and through the analysis result, each administrative district can better optimize urban resources and improve urban vitality according to its situation. This discovery guides urban planning and has enlightenment significance for cities seeking construction facility investment and facility construction-oriented development.