mokunet
Observations & Indicators

Community knowledge the map alone cannot hold.

The shared map gives everything a place, but it can't hold what only a community knows. Two paths add that knowledge: field observations from community researchers, and moku-level statistics that sharpen federal baselines. Both are tied to a place on the map — neither changes it.

Explore the Research CommonsSee the shared map
3
Contribution Types
Traceable
SDG Measurement
Federal + local
Baseline Source
Open review
Contribution Path

Observations: the Research Commons

Geocoded environmental samples — water quality, soil tests, species surveys — are contributed through the Research Commons, a public GitHub repository with an open review workflow.

1
Add your data
A contributor copies the public research repository and adds their dataset, along with a short description of what it is, where it was gathered, and what it measures.
2
Submit for review
Submitting it kicks off automatic checks — that the data is well-formed, the coordinates are valid, and the description is complete.
3
Accepted & added
Once accepted, the system adds the contribution and its records, links them to the right SDG goals, and places each record on the shared map.
The only fully traceable SDG measurement path.When a research record says it measures SDG 6 (Clean Water), that claim is backed by a traceable chain — the contribution, the records it contains, the sites those records observed, and the moku those sites belong to — not just an editorial label.

Three contribution types

The contribution type a researcher chooses decides which details are required and how the record is read.

Observation

Field-collected environmental samples
Example: Water quality at Keehi Lagoon

Indicator

Local statistics broken down to the moku level
Example: Food access by moku district

Spatial overlay

Boundary or classification layers
Example: Monitoring site network
Topic areas:Land & environmentWaterBiodiversityAgricultureCoastalClimateForestryFood safetyInfrastructureDemographicsCommunity wellbeing

Indicators: refining federal baselines

Island baseline statistics sourced from federal data — demographics, employment, food access, energy, and other measures organized by SDG pillar — provide useful context but are limited to the county level, which in Hawaiʻi means island-level at best.

From county level to moku level

Indicator contributions through the Research Commons link community-collected local data to the matching federal numbers. That bridges the gap between county-level and moku-level, giving people a sharper picture of conditions in their area.

How both paths reach the map

Neither observations nor indicators change the map. Both follow the same simple process — the researcher provides coordinates; the map provides the context.

1
Place the coordinates
Each record's coordinates are matched to a cell on the shared grid.
2
Find it on the map
That cell is located on the shared map.
3
Pick up the context
The map links the record to the moku that contains it and every zone that overlaps.
A water-quality sample taken at a specific GPS coordinate automatically picks up its full context — which moku it belongs to, whether it falls within a conservation reserve or farmland, and which schools or facilities are nearby.

Measurements that stay traceable

Observation records are tied to specific monitoring locations — water sampling points, soil test stations, air quality monitors. A connected, community-managed monitoring network across moku is being built.

From any SDG goal, you can trace which contributions measure it, where those measurements were observed, and which moku holds those places — a reading a community takes counts, and stays traceable to who gathered it and where.
The Research Commons is open to anyone with data to share. Field observations and moku-level indicators both strengthen the picture for a moku without ever rewriting the official map underneath.