An Integrated Framework for Timeline Mapping Using Local Chronicles: A Case Study of Educational Equity in Dengfeng, China

Reconstructing long-term spatiotemporal processes from unstructured historical records presents a fundamental challenge in historical Geographic Information Systems (GIS). Local chronicles contain rich information regarding the timing, location, and context of historical events. However, their narrative form hinders systematic spatiotemporal interpretation and timeline mapping. To address this challenge, this paper proposes an integrated framework for timeline mapping using local chronicles, which includes (1) a dimension-importance evaluation method to identify the most relevant analytical dimension for visualization, (2) a large language model (LLM)-based workflow for extracting, matching, and clustering historical events from unstructured texts with respect to the identified analytical dimension, and (3) a multi-perspective timeline mapping approach to reconstruct and visualize spatiotemporal narratives. The framework is demonstrated through a case study in Dengfeng, China, where historical educational records from local chronicles are employed to reconstruct the long-term spatiotemporal evolution of educational equity. The case study demonstrates that (a) structural balance is identified as the most relevant analytical dimension, guiding subsequent event mining; (b) the LLM-based workflow extracts and structures 721 historical events, with 415 events matched to the identified dimension and clustered into three thematic groups of institutional plurality, school system reform, and spatial reorganization; and (c) the multi-perspective timeline maps generated from these clusters effectively reveal a long-term, coherent process of structural reconfiguration, illustrating the framework’s capacity to transform narrative historical records into interpretable spatiotemporal narratives. The proposed framework provides a transferable historical GIS workflow for timeline mapping using local chronicles and offers a practical approach for reconstructing spatiotemporal processes from unstructured historical texts in various geographical contexts.