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Layer Methodology

Planting Priority

A weighted restoration-priority score combining heat, canopy deficit, imperviousness, and population density.

Derived6 cities

Map Role

A derived restoration score for where planting could deliver the most combined benefit.

What It Shows

This layer scores each dissemination area for planting priority by combining thermal stress, canopy deficit, imperviousness, and population density. It is designed to surface where new planting could deliver the strongest combined urban benefit.

Sources

  • Heat Islands layer
  • Impervious Surface layer
  • Urban Forest Inventory for tree-density context
  • Census dissemination areas and population fields

Method

  1. 1.Use dissemination areas as the base geography.
  2. 2.Assign heat score from the heat-island class at the area centroid.
  3. 3.Estimate canopy deficit from tree density against a target of roughly 40 trees per hectare.
  4. 4.Combine heat, canopy deficit, imperviousness, and population density using weights of 30%, 30%, 20%, and 20%.

Refresh Cadence

Derived snapshot. Refresh when the upstream heat, tree, impervious, or census inputs are updated.

Coverage

Six-city dissemination areas with one priority score and component scores per polygon.

Key Fields

priority_score

Composite 0 to 100 planting-priority score

priority_class

Priority class from low to critical

heat_score

Heat contribution to the composite

canopy_deficit

Tree-density deficit contribution to the composite

imperv_score

Imperviousness contribution to the composite

pop_score

Population-density contribution to the composite

Caveats

  • Weights are heuristic and tuned for prioritization rather than causal inference.
  • Canopy deficit depends on available inventory coverage and will miss private canopy.
  • Population density is a coarse social proxy and should be read with local context.