Island Baselines
Island-effect baselines from Google Data Commons, mapped through the spatial graph to Hawaiʻi's 33 traditional moku districts. These county-level indicators reflect island-wide trends — moku-specific metrics require locally contributed research. 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
Island-effect baselines are drawn from public federal sources (USDA, Census, BLS, NOAA, EIA, UN SDG) via Google Data Commons at county, state, or national resolution. All moku within a county inherit identical indicator values. The spatial graph provides zone cell counts and area context, but indicator disaggregation requires locally contributed research data.
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 baselines above), this dataset aligns to SDG 2, 3, 4, 8, 11, 13, and 16.
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 island-effect baselines and district-level governance by contributing geocoded datasets. Contributions auto-map to SDG classifications through the platform's topic taxonomy:
SDG Classification Bridge
Local datasets flow through the governance graph: geocoded records are assigned to moku districts, then linked to the SDG goals they measure. This measurement path complements the strategic SDG alignment from community projects — both converge at the same sustainability goals, enabling comparison between what communities target and what research measures on the ground.