Knowledge graph links geospatial data to study place-based government
Geospatial AI, Dataverse Metadata, and the Study of Place-Based Government
Artificial Intelligence
Summary
Many research datasets at Harvard Dataverse include location information, but this data is often just typed in without a clear structure, making it hard to search by place. The authors built a big network that connects these datasets with topics, places, and publications to make them easier to explore together. They found that most datasets are connected in one large group, even if their location details differ. The study also shows challenges like different names for the same place and points out that most data focus on U.S. cities. This work sets the stage for using AI to improve location data and link discussions to specific places.
What this means in practice
- •For government data teams: Combine and search government-related datasets by location to better understand place-based policy data connections.
- •For geospatial software developers: Create AI tools that automatically clean and link geographic names in datasets to improve metadata quality and usability.
Authors
Danny EBanks, Devika Jain
Abstract
Harvard Dataverse hosts over 150,000 research datasets, but the geographic information those datasets carry is entered as free text by depositors and has never been assembled into a searchable structure. We construct a knowledge graph from the repository's public data and metadata, organizing 102,650 datasets within a 215,985-node network of 528,003 edges linking datasets to keywords, publications, subjects, journals, and locations. Of those datasets, 43,991 (42.9 percent) carry at least one geospatial field, geographic coverage, geographic unit, or a bounding box and 96.9 percent of all nodes sit in a single connected component, so datasets remain reachable from one another even when their geospatial metadata share nothing in common. A conservative keyword search identifies 7,654 geospatially tagged datasets (17.4 percent) as directly policy-relevant, with elections and legislatures the largest cluster, followed by government administration, health policy, transportation, and education. Five datasets illustrate how this metadata behaves across policy domains and spatial scales, and an extended use case shows how community language models, stance detection with geographic aggregation, and partisan language bridging tools can attach discourse to place. The central obstacle is place resolution: the same location appears as many disconnected nodes. We argue that the graph provides a concrete setting for developing AI-driven metadata enrichment and entity resolution, and we document its coverage skew toward American, city-level data.