In the digital era, data collaboration constitutes a critical trend in urban planning and design. It is of paramount importance in addressing contemporary issues related to the misinterpretation, misapplication, and misunderstanding of spatial genes, as well as facilitating data sharing and value creation associated with spatial genes. In this paper, targeting the complex problems of multiple entities and threads in spatial gene research and practice through, initially, a literature review, the correlation process between urban planning and data collaboration is examined, the spatial gene concept and the background of its proposal are expounded, and the challenges confronted in spatial-gene data collaboration are analyzed. Then, with an elaboration of the data value chain concept, a data collaboration framework for spatial-gene research and practice is constructed, specifically encompassing three main links: convergence, mining, and application. Finally, from the three aspects of data collection and storage, data analysis and processing, and data circulation and sharing, technical implementation paths and suggestions are put forward. We firmly contend that through the establishment of the framework, it is anticipated to promote data collaboration among multiple entities, enhance the efficiency and scientificity of urban design, and thereby facilitate the preservation of cultural diversity and the sustainable development of cities.

In the field of digital architectural heritage, the mining of tacit local knowledge embedded in architectural heritage is considered essential for the preservation, inheritance, and application of regional architectural characteristics. Local knowledge can be formally represented through semantic models, by which the automated mining of tacit information can be facilitated. However, due to the incomplete preservation of physical buildings and the fragmented nature of historical records, local knowledge is often represented as semantic fragments. Consequently, existing semantic models are still challenged in terms of knowledge integration and reasoning. In this study, a knowledge graph was developed for representing local knowledge, in which fragmented local semantics were aligned at both the ontological and entity levels. Subsequently, implicit local knowledge mining is achieved through meta-path centrality propagation combined with expert evaluation on a graph visualization platform. The method was applied to eight historical buildings in a case study. The knowledge graph quality assessment results indicate excellent ontology utilization and property utilization. The knowledge mining results demonstrate that graph-based expert evaluation successfully enables knowledge Feature Ranking and knowledge Extinction Warning.