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.
Example visualization
Compare derived boundaries vs official administrative data:
What you get
Each municipal unit is delivered as a structured spatial dataset:
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Complete dataset containing all municipalities as GeoJSON features.
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Individual GeoJSON files, one per municipality, extracted from the full dataset for convenience.
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Lookup file mapping municipality identifiers to individual files.
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Dataset metadata, including generation date and parameters.
How it works (high level)
This system is built from three core steps:
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Spatial normalization
All input data is converted into a deterministic spatial grid.
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Density + influence modeling
Address concentration is treated as a field rather than a point set.
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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
Data access
Available formats:
- GeoJSON
License (simple version)
Use it freely inside your business, systems, and analysis workflows. You can modify it and build on top of it. Just don’t resell or redistribute the raw dataset, or include it in a competing data product.
Sources
Adapted from Statistics Canada, National Address Register, 2025-12-19. This does not constitute an endorsement by Statistics Canada of this product.
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.