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A holistic human‐centered design integrating scalable CI and GeoAI is proposed that enables users to effectively tackle the full spectrum of spatial decision challenges, from clearly defined problems to those of high complexity and ambiguity.
It is found that datasets with more missing metadata receive fewer downstream citations and have fewer resolvable connections to other datasets, suggesting that geographic metadata is a more reliable basis for cross-disciplinary interoperability than keyword vocabularies, which often remain discipline-specific.
It is argued that the graph provides a concrete setting for developing AI-driven metadata enrichment and entity resolution, and its coverage skew toward American, city-level data.
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