Parametric heritage 3D reconstruction using iconographic evidence has the potential to extend modelling data sources and enhance the modelling efficiency. This study presents a novel and preliminary workflow to document, 3D reconstruct and interpret architectural massing typologies in the Song Dynasty of ancient China, integrating iconographic evidence. Multi-source historical images are collected and screened through chronological and appraisal assessments. The extracted massing principles are translated into parametric algorithms using Rhino-Grasshopper, then tested on 3D reconstruction of historical images by student participants. Furthermore, it is applied in an interactive design way through the Biomorpher plugin. The results show that the algorithms successfully enhanced the modelling efficiency, while limitations are also reflected. The interactive design application facilitates scenario comparisons in Song architecture 3D reconstruction and design. This study also sheds light on future applications in architectural conservation, interactive heritage education, and other research aspects in art and architecture.

During the rural [re]vitalization process in China, national strategies required rural public spaces with cultural significance to be identified before planning decision-making. However, places identified as culturally significant by planners and visitors can differ from the ones mostly used and valued by locals. Even if there is a growing interest in integrating local perspectives and experiences in planning, studies seldom discuss and compare openly the adequacy of spatial configuration, cognition and behaviour to support it. This study took Anyi Historic Village Cluster as a case study to empirically investigate rural public spaces with three distinct, yet related approaches: (1) Morphological: spatial network centralities based on space syntax; (2) Cognitive: Lynchian village images with semi-structured interviews; (3) Behavioural: spatiotemporal occupation patterns using Wi-Fi positioning tracking. Significant places valued and used by locals and non-locals were detected with the multi-source data. Furthermore, multivariant regression models managed to characterize the relationship among different aspects of investigated rural public spaces, which also helped diagnose places of interest to prioritize in planning, demonstrating the advantage of integrating the sources of information in practice instead of studying them apart. Results can also assist rural planning on how to identify what to preserve, what to enhance, and how to develop such spaces, without overlooking the local needs or losing the rural identity.