Geography as the Organizing Grammar of Geospatial Models

Abstract

GeoAI increasingly produces reusable Earth representation s, physical forecasts, multimodal systems, and reasoning a gents. Their progress exposes two distinct limitations. Geo graphic completeness asks whether the represented world ex tends beyond readily observed land surfaces and atmospher ic fields to oceans, biogeography, economies, institutions, and human agency. Geographic intelligence asks whether model outputs preserve place, scale, relations, process, un certainty, and the limits of valid inference. The first con cerns what exists in a model; the second concerns what may responsibly be claimed about it. This critical integrative review argues that geography provides the organizing gram mar that connects these dimensions. Seven structural gaps, propositions, and corresponding review questions translate the argument into testable requirements. The resulting re search agenda advances Living Geospatial Models as federat ed, continually updated systems that couple specialist Earth and human-domain models through shared geographic iden tity, support, relations, provenance, and uncertainty. The objective is not a single universal network, but geospatial intelligence that can explain connections and change, antic ipate plausible futures, and support accountable decisions across places and scales.