Mokunet

Learn

Every feature in Mokunet resolves to a place. A spatial backbone built from 18 authoritative datasets anchors all governance — projects, programs, research, and compliance — to Hawaiʻi's 33 traditional moku districts. What you can see, do, and contribute depends on your role and where you work.

Key Guidance, Standards & Specifications

Community goals (ISO 37101) guide lifecycle projects (ISO 14040) whose records are governed for authenticity and integrity (ISO 15489-1), while GS1 enables supply chain traceability across facilities — together driving measurable district outcomes.

ISO 14040
ISO 15489-1
ISO 15686
ISO 37101
buildingSMART IDS
GS1
SDGs

Gateway topic — SDG codes direct programs and projects into district-level data workflows

How It Works

Three layers define how data flows through the system — from authoritative spatial sources, through role-scoped access, to community-contributed observations.

Layer 1

Spatial Backbone

18 authoritative datasets, one read-only spatial model

The foundation is a spatial dataset assembled from 18 state and federal sources — moku boundaries, agricultural land, conservation reserves, wetlands, schools, highways, and more. Every zone is geocoded and linked to one of 33 moku districts. The backbone is immutable: the platform reads it but never writes spatial data.

33
Moku Districts
19,720
Grid Cells
40,000+
Zone Overlays
18
Source Datasets
Layer 2

Role-Based Perspectives

Your profile and moku determine what you see

When you subscribe, a short interview assigns your profile type based on your primary work — planning, design, facilities, food systems, procurement, or land stewardship. Combined with your moku affiliation, this determines which zones, features, and governance relationships are relevant to you. Two people in the same system see different things because the backbone scopes what each role can act on.

7
Profile Types
6
Project Types
4
Lifecycle Stages
9
SDG Goals
Layer 3

Observations & Indicators

Community-sourced data that the backbone alone cannot provide

Two external pathways add data that the backbone alone cannot provide. Observations are geocoded environmental samples (water quality, soil tests, species surveys) contributed through the Research Commons. Indicators are sub-county statistics that refine county-level federal baselines. Both are spatially resolved to the backbone — neither modifies it.

3
Observation Types
Verified
SDG Measurement
Federal + local
Baseline Source
Open review
Contribution Path

Current Platform Status

Mokunet is under active development. Here is what is live today and what is coming next.

Live

  • Spatial backbone (33 moku, 18 datasets)
  • Unified project system (6 types, lifecycle stages)
  • Subscriber onboarding with role + moku assignment
  • Producer sync from bGoodFarms
  • Operational Support Guide
  • IDS Editor with project persistence
  • Research Commons ingestion pipeline

In Progress

  • Subscriber registry (your projects, roles, and affiliations)
  • Monitoring network aggregation
  • Observations warehouse for sample storage
  • External API for partners and developers
  • Lifecycle assessment data

Ask About a Place

The backbone is not just a map — it is a queryable model. Every moku district, zone overlay, project, and research record is connected through structured relationships. The goal is a semantic interface where you describe what you need in plain language and the system finds the answer across the backbone.

What water quality data exists near agricultural land in Ewa?

How the backbone resolves this

1. Locate

Resolve “Ewa” to the moku district and its 598 backbone cells

2. Intersect

Find cells where agricultural zones (IAL, ag baseline) overlap with environment monitoring sites

3. Retrieve

Return research records observed at those sites, with SDG linkage and contributor provenance

Behind this query: structured lookups across the spatial backbone produce precise results by following the relationships between districts, zones, and records. As project and observation data grows, these query patterns become a training set — teaching the system which questions lead to which answers. The same relationships that govern projects today will power natural-language spatial queries tomorrow.

SDG Alignment

Programs and projects map to these UN Sustainable Development Goals, aligned with the Hawaiʻi 2050 Sustainability Plan and Aloha+ Challenge.

SDG 2: Zero Hunger
Zero HungerFood Systems
SDG 6: Clean Water
Clean WaterWater Resources
SDG 7: Affordable Energy
Affordable EnergyEnergy
SDG 8: Decent Work
Decent WorkEconomic Development
SDG 11: Sustainable Cities
Sustainable CitiesCommunity Planning
SDG 12: Responsible Consumption
Responsible ConsumptionSustainable Tourism
SDG 13: Climate Action
Climate ActionClimate Resilience
SDG 14: Life Below Water
Life Below WaterOcean Resources
SDG 15: Life on Land
Life on LandEnvironmental Stewardship