Population-Weighted Geography Crosswalk

Overview

The weighting pipeline uses a population-weighted crosswalk to map PUMAs onto user-defined target zones. Because the source and target boundaries rarely align, Census blocks are rasterized into a population grid, PUMAs are rasterized into a label grid, and exactextract performs zonal cross-tabulation against the target polygons.

The implementation lives in src/processing/weighting/data_prep/crosswalk.py.

flowchart LR
    B["Census Blocks"] --> B1["Rasterize population"] --> B2["Population grid"]
    P["PUMAs"] --> P1["Rasterize IDs"] --> P2["Label grid"]
    Z["Target Zones\n(polygons)"]

    B2 --> X["exactextract\n(zonal stats)"]
    P2 --> X
    Z --> X

    X --> C["Crosswalk"]

Calculation Details

1. Rasterization

Two arrays are burned onto a common grid (CRS: EPSG:5070, NAD83 CONUS Albers) at a configurable cell size (default 250 m):

Array	Dtype	Cell value
Weight (pop)	float32	Block population distributed evenly across the cells each block covers: cell_pop = block_pop / n_cells
Source label	int32	Integer ID of the source zone covering that cell (0 = no data)

2. Zonal Cross-Tabulation (exactextract)

Both rasters are written to temporary GeoTIFFs and passed to exactextract together with the target-zone polygon layer:

exact_extract(
    [weight_path, source_path],
    target_gdf,
    ["values", "coverage"],
    include_cols=["target_id"],
)

For each target zone polygon z, exactextract returns per-cell arrays of:

• v_i_weight: weight value of cell i
• v_i_source: integer source-zone label of cell i
• c_i: coverage fraction of cell i by polygon z

In plain terms, each cell contributes weight value * polygon coverage fraction.

3. Population by Source × Target Zone

Within each target zone z, cells are grouped by source label s, and the coverage-weighted population is summed across all matching cells.

4. Allocation Weights

Each source zone's population is then normalized across target zones:

• allocation_weight(s, z) = Pop(s, z) / total source population

By construction, the allocation weights sum to 1.0 across target zones for each source zone.

Worked Example

Assume a 4 x 4 grid of equal-sized cells, with each populated cell contributing 10 people.

• Source A occupies the left 8 cells, so its total population is 80.
• Source B occupies the right 8 cells, so its total population is 80.
• Zone L captures about 75% of Source A.
• Zone M captures the remaining 25% of Source A and 25% of Source B.
• Zone R captures the remaining 75% of Source B.

That gives the following source-by-target populations:

Source	Target zone	Population contribution
A	L	60
A	M	20
B	M	20
B	R	60

Now normalize within each source zone:

For Source A:

• allocation_weight(A, L) = 60 / 80 = 0.75
• allocation_weight(A, M) = 20 / 80 = 0.25

For Source B:

• allocation_weight(B, M) = 20 / 80 = 0.25
• allocation_weight(B, R) = 60 / 80 = 0.75

So the final crosswalk rows would look like this:

source_id	target_id	allocation_weight
A	L	0.75
A	M	0.25
B	M	0.25
B	R	0.75

This is the key idea: each source geography is split across target zones in proportion to the coverage-weighted population captured in each target polygon.

Settings and code

The crosswalk is set by the geography block of Compute Weights. The PUMA-specific wrapper is in processing.weighting.data_prep; the generic build_crosswalk, which works for any source, target and weight polygons, is in utils.