How Historical and Cultural Resources Shape Street Vitality in Historic Urban Areas: Interpretable Machine Learning Evidence from Multi-Source Data in Chengdu

Historic urban areas are important repositories of urban history, cultural memory, and local identity. Yet how historical and cultural resources (HCRs) are associated with street vitality, particularly in comparison with streetscape factors, remains insufficiently understood. Taking Chengdu’s historic urban area as a case study, this study integrates streetscape images, point-of-interest (POI) data, and HCR data. It employs machine learning with model interpretation techniques to identify key predictors and nonlinear relationships. The study evaluates the associations of HCRs with street vitality in terms of density, hierarchy, and accessibility, while also assessing streetscape factors within the same analytical framework Results show that HCRs are among the most important explanatory variables associated with street vitality. General HCRs are associated with broadly distributed everyday vitality, whereas high-tier HCRs show stronger localized associations with vitality patterns. The association between high-tier HCRs and street vitality exhibits a clear distance-decay pattern, with the strongest positive associations observed within approximately 650 m. Streetscape factors also exhibit threshold effects: the positive association between vegetation and vitality levels off above a visual proportion of approximately 0.25, whereas building visual proportions between 0.20 and 0.40 are associated with greater vitality. This study broadens the analytical framework for street vitality by incorporating HCRs as key explanatory factors and examining their associations with vitality patterns.