Reframing Historical GIS: From Tools and Infrastructure Toward Value-Oriented Knowledge Production

Historical geographic information systems (HGIS) have evolved from auxiliary tools for digitizing historical materials and displaying maps into infrastructural research environments that connect multidisciplinary forms of historical spatial knowledge production. Yet infrastructure alone does not fully capture the field’s value-oriented epistemic goals. Building on existing research on HGIS and historical spatial data infrastructures (HSDIs), this integrative literature review proposes Historical Geomatics as an agenda-setting heuristic framework organized around the core question of how HGIS can restructure knowledge production in historical geography. It synthesizes the field’s knowledge traditions, workflows, analytical paradigms, and future directions. First, it clarifies that HGIS function across research traditions as a tool, a method, and an environment that can use HSDIs to organize heterogeneous historical materials. Second, it conceptualizes spatialization as a continuous workflow of spatial element recognition, geographic attribute assignment, standardized modeling, and validation and revision. Standardization and explicit uncertainty representation are treated as prerequisites for research quality, while platform-based and public HGISs extend the lifecycle of historical spatial data. Third, the review groups existing scholarship into four analytical paradigms: spatiotemporal reconstruction; urban morphology and spatial structure; networks, mobility, and social space; and place, landscape, and memory. Finally, it examines how geospatial artificial intelligence (GeoAI) is reshaping HGIS knowledge production and argues that Historical Geomatics may serve as an agenda-setting heuristic for the next stage of HGIS. The principal opportunities lie not in accumulating additional cases, but in strengthening HSDIs, improving multimodal automation, representing uncertainty explicitly, and rebalancing space and place, models and narratives, and technical efficiency and historical context.