Island Baselines
County-level statistics from federal sources via Google Data Commons, routed through the spatial backbone to Hawaiʻi's 33 traditional moku districts. SDG codes shown here are editorial classifications, not graph-derived — they indicate thematic relevance but do not represent measured SDG alignment. Graph-grounded SDG measurement requires locally contributed research with geocoded records. Select an island to explore.
Island-Effect Resolution
These indicators are published at county resolution (5 counties ≈ 5 islands). Within a county, all moku share identical values — the platform does not fabricate sub-county disaggregation. The data shows island-wide effects: broad trends like farm count, rainfall, and unemployment that characterize an island rather than variation between its districts. Moku-specific metrics require geocoded research contributions with coordinates or H3 indices that resolve to individual districts.
About these baselines
These indicators are fetched from an external API (Google Data Commons) drawing on public federal sources (USDA, Census, BLS, NOAA, EIA, UN SDG) at county, state, or national resolution. They are not stored in the governance graph. The spatial backbone provides moku-to-county routing, zone cell counts, and area context, but the indicator values themselves remain external API data. All moku within a county share identical values — indicator disaggregation to the moku level requires locally contributed, geocoded research.
Community Research: Quality of Life & Well-Being (2024)
Dr. John P. Barile's Social Science Research Institute (SSRI) at UH Manoa, through the Health Policy Initiative and the Governor's Office of Wellness & Resilience, surveyed 8,000+ residents across 6 social determinant domains — health, economic stability, education, neighborhood quality, disaster preparedness, and worker well-being. Published at county resolution (same as the federal statistics above), the QOL data is presented here as static arrays — it is not stored in the governance graph. SDG codes shown are editorial classifications indicating thematic relevance to SDG 2, 8, 11, and 13. The original survey also addresses outcomes related to SDG 3 (Health), SDG 4 (Education), and SDG 16 (Peace & Justice), which are outside the current platform scope.
The underlying survey collected responses at 66 named neighborhoods — many of which map to specific moku. Neighborhood-level microdata from the lab would be the first dataset to bridge the county-to-moku resolution gap for social determinant indicators.
Contribute Moku-Level Research
Planning professionals and research partners can bridge the gap between these county-level statistics and district-level governance by contributing geocoded datasets. Contributed records are spatially assigned to moku districts and linked to SDGGoal nodes via graph edges — the only path to verified SDG measurement in the platform. Research topics:
SDG Alignment: Editorial vs. Graph-Grounded
The SDG codes shown on this page are editorial classifications — thematic labels assigned in application config to indicate which Sustainable Development Goals each indicator relates to. They are not derived from graph edges and do not represent measured alignment.
Graph-grounded SDG measurement exists for locally contributed research data: geocoded records are assigned to moku districts via H3 zone cells, then linked to SDGGoal nodes through MEASURES_SDG edges. This path — from ResearchRecord through the spatial backbone to SDGGoal — is the only SDG alignment in the platform backed by graph relationships. Contributing geocoded research is the mechanism that bridges these county statistics to verified SDG measurement.