A Study of the Non-Linear Relationship Between Urban Morphology and Vitality in Heritage Areas Based on Multi-Source Data and Machine Learning: A Case Study of Dalian

Abstract

The preservation of historic heritage not only fosters cultural significance and socio-economic development, but also enhances urban competitiveness. Investigating the vitality of historic urban areas is crucial for measuring their developmental attractiveness, contributing to more effective preservation and planning. However, existing research primarily focuses on urban areas, leaving the applicability of urban form elements to heritage sites and their influence mechanisms unclear. This study employs XGBoost and SHAP, utilizing geographic big data and deep learning techniques, to determine whether the urban form elements impacting the vitality of heritage and urban areas are the same or exhibit different spatial distributions and diurnal variations. Empirical analysis of Dalian reveals significant diurnal variations in the factors affecting vitality, along with distinct key elements for both heritage and urban areas. This study is innovative in being the first to apply deep learning methods to analyze the factors influencing the vitality of Dalian’s heritage areas at the district scale, providing theoretical support for enhancing vitality and promoting urban development.