PlaidSheep Municipal Geoshapes

High-resolution, derived municipal boundaries built from real-world spatial signals rather than administrative drawings.

These datasets represent how places actually behave in space—not just how they are defined on paper.

What this is

Municipal Geoshapes are computed boundary regions generated from address density, spatial clustering, and influence fields.

Instead of relying solely on official administrative polygons, these shapes model:

• Where human activity concentrates
• How settlements expand and merge
• Where functional boundaries naturally form

The result is a data-driven approximation of municipal extent and influence.

What you get

Each municipal unit is delivered as a structured spatial dataset:

• GeoJSON polygon(s)
• Grid-based representation (geohash-aligned)
• Optional influence density field
• Stable unique identifier per municipality
• Optional overlap / transition zones (where applicable)

Example record structure

{
  "id": "municipality_id",
  "name": "Airdrie",
  "geometry": { "type": "Polygon", "coordinates": [] },
  "metadata": {
    "source_model": "address-density-influence-v1",
    "resolution": "geohash-7",
    "confidence": 0.92
  }
}

How it works (high level)

This system is built from three core steps:

1. Spatial normalization

   All input data is converted into a deterministic spatial grid.

2. Density + influence modeling

   Address concentration is treated as a field rather than a point set.

3. Boundary extraction

   Boundaries are derived from:

   • clustering peaks
   • gradient falloff
   • overlap resolution between competing municipal influences

The result is a functional boundary, not just a legal one.

What makes this different

Most municipal datasets are:

• static
• administratively defined
• inconsistent across jurisdictions

PlaidSheep geoshapes are:

• derived from real spatial distribution
• consistent across regions
• comparable between countries and data sources
• designed for computation, not visualization

This makes them useful for:

• spatial joins
• coverage modeling
• service area analysis
• exposure/risk systems
• demographic inference

Use cases

• Service area definition (utilities, logistics, insurance)
• Spatial indexing for address-level datasets
• Market penetration analysis
• Regional aggregation of risk or demand signals
• Cross-jurisdictional comparisons

Important note

These shapes are not legal boundaries.

They are:

computational representations of municipal influence and settlement structure.

They may:

• differ from official boundaries
• include transitional or ambiguous zones
• evolve with data updates or resolution changes

Example visualization

Compare derived boundaries vs official administrative data:

Data access

Available formats:

• GeoJSON
• Flat grid export (geohash indexed)
• CSV boundary summaries
• API access (optional)

About PlaidSheep

PlaidSheep builds spatial data systems that focus on structure, overlap, and real-world signal density rather than static representation.

We treat geography as a computational field, not a drawn map.

Notes for integrators

Recommended for systems that require:

• deterministic spatial joins
• scalable regional aggregation
• consistent cross-source geography normalization

NOTE

Not intended as a cartographic replacement for official boundary datasets.