An analysis of spatial vitality distribution and formation mechanisms in historical urban areas based on multi-source big data: A case study of Changsha

Abstract

As global urbanization accelerates, the spatial vitality of historical urban areas has become a critical issue in urban regeneration and sustainable development. Some existing spatial vitality evaluation frameworks fail to integrate multiple dimensions effectively, limiting their capacity to capture the dynamic complexity of these areas comprehensively. This study utilizes multi-source big data and deep learning technologies to propose a new multidimensional evaluation system for spatial vitality, improving existing models and systematically analyzing distribution patterns and formation mechanisms. The research results show that: (1) The spatial vitality of Changsha's historical urban area exhibits a distinct “core-periphery” pattern. Core commercial zones show high vitality due to functional concentration, whereas peripheral areas have weaker vitality because of lower physical space quality and limited functional diversity. (2) Through correlation and principal component analyses, five key factors influencing spatial vitality were identified: emotional perception, visual aesthetics, spatial attractiveness, Functionality and Structure, and traffic conditions. (3) Bivariate spatial autocorrelation analysis further revealed spatial clustering effects between spatial vitality and its key factors, emphasizing the potential for enhancing functional diversity and optimizing road traffic conditions in core areas. The study's findings offer scientific guidance for urban regeneration and policy-making, particularly in optimizing spatial layouts, enhancing vitality, and fostering the coordinated development of cultural heritage protection, providing valuable insights for other developing countries.

Keywords

Street view images / Deep learning / Sentiment prediction / Historical urban areas / Spatial vitality

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