How much of each state is built up?

PythonadvancedSpatial analysis

A land-use commission is comparing states by their built-up share, using the Natural Earth urban-area footprints as a first pass before the expensive satellite product arrives. The commission's own summary summed each footprint's `area_sqkm` into the state that contained its centroid, which put all of the New York metro into New Jersey. You are asked to do it properly: clip every footprint to the state line and measure what actually lies on each side.

Write a script that produces a per-state urban-footprint table.

The datasets are mounted where the script runs:

/data/states.geojson — the 50 states and DC
/data/urban.geojson — 2,143 urban-area footprints worldwide

geopandas, shapely, pyproj, pandas and numpy are available. Assign the finished table to a variable named `result`: a GeoDataFrame with one row per state, carrying the state polygon and

postal — the state
urban_km2 — area of urban footprint inside the state, km²
urban_pct — that as a percentage of the state's area

Area is measured in an equal-area projection — EPSG:5070 (NAD83 / Conus Albers) is the standard one for the United States and stays equal-area for Alaska and Hawaii, if not pretty. A footprint that straddles a state line contributes to both states, each its own part; area_sqkm on the footprint is the whole polygon and is not to be used. Do not write a file — the runner reads result.

The input files

/data/states.geojson — EPSG:4326; the geometry is in geometry (Polygon / MultiPolygon).

column	type	meaning
`state_id`	string	Natural Earth adm1 code
`name`	string	State name
`postal`	string	Two-letter postal abbreviation
`region`	string	Census region: Northeast \| Midwest \| South \| West
`type`	string	State \| Federal District

/data/urban.geojson — EPSG:4326; the geometry is in geometry (Polygon).

column	type	meaning
`urban_id`	string	Footprint identifier, `UA-` and four digits
`area_sqkm`	number	Natural Earth's own area of the whole footprint (km²)
`scalerank`	integer	Prominence, lower is larger

Run executes the script in your browser and shows you its output and a preview. Submit executes it again on the server and grades what result holds.

What to submit

Submit: A Python script that assigns its output to result.
Input files: /data/states.geojson · /data/urban.geojson
Required properties: postal, urban_km2, urban_pct

Objectives

Reproject both layers to an equal-area CRS
Clip the urban footprints to the states with an overlay
Sum clipped area per state and express it as a share of the state's area
Assign the finished GeoDataFrame, one row per state, to result

Constraints

These are where the teaching is. Read them twice.

Use an overlay or intersection — not a centroid assignment and not the footprint's area_sqkm
Measure area in an equal-area projection; Web Mercator and geographic degrees are both wrong
Every state appears, with zero where there is no footprint
result carries the state polygons with postal, urban_km2 and urban_pct

Graded requirements

Clips footprints to states with an overlay
One row per state
Carries postal, urban_km2 and urban_pct
Total urban footprint inside the states
The most built-up unit's share

Clip-and-credit overlays are how land cover, flood extent and any other footprint layer is attributed to the administrative units that report on it.

Python

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Files: /data/states.geojson, /data/urban.geojson · assign result

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What will be checked

5 scored, 0 informational

Clips footprints to states with an overlay
17%
One row per state
17%
Carries postal, urban_km2 and urban_pct
17%
Total urban footprint inside the states
33%
The most built-up unit's share
17%