Machine Learning for Predicting Place Attachment in Public Spaces of Historic Districts: Identifying Key Factors in Heritage-Led Urban Renewal

Heritage-led renewal can improve the built environment of historic districts, but physical upgrading may not be accompanied by better public-space experiences or stronger place attachment among residents. However, comparative evidence remains limited on how different aspects of public-space experience relate to Place Identity (PI) and Place Dependence (PD). Taking Nantou Ancient City in Shenzhen as a case study, this research combined a survey of 780 residents with field observations and used Random Forest, XGBoost and out-of-fold SHAP to assess the relative predictive importance of Public-Space Governance, Perceived Accessibility and Social Interaction for PI and PD. The results show that (1) PI was most strongly associated with Social Interaction in XGBoost, which had the highest domain-level importance (46.5%), while Collective Participation, Informal Governance, Strong-Tie Interaction and Formal Governance were the leading dimensions; (2) PD was more closely related to functional and use conditions, with Perceived Accessibility showing the highest domain-level importance (44.5%), and with Perceived Physical Accessibility, Formal Governance and Perceived Social Accessibility the three highest-ranked dimensions for PD; and (3) Public-Space Governance showed substantial relative predictive importance for both PI and PD. Random Forest showed the same domain rankings as XGBoost for both outcomes and demonstrated high consistency at the indicator level. These results indicate that PI and PD are associated with different aspects of public space experience, but the causal relationship still lies beyond the scope of this cross-sectional study.