Which countries have no airport in the registry?

PythonbeginnerSpatial analysis

A civil-aviation data unit publishes a per-country count from this registry and wants it as a script that re-runs when the registry is refreshed. The interesting rows are the countries that come out at zero: some really have no listed airport, and some have one that the coarse coastline puts in the sea. The count has to keep every country so both kinds are visible.

Write a script that produces a per-country airport table.

The datasets are mounted where the script runs:

/data/countries.geojson — country and dependency polygons
/data/airports.geojson — 893 airports

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

iso_a3, name
airports — airports whose point the country polygon contains

Assign airports to countries by geometry. Do not write a file — the runner reads result.

The input files

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

column	type	meaning
`country_id`	string	CO- plus the ISO 3166-1 alpha-3 code
`name`	string	Short name
`name_long`	string	Long name
`iso_a3`	string	ISO 3166-1 alpha-3; -99 where Natural Earth assigns none
`iso_a2`	string	ISO 3166-1 alpha-2; -99 where Natural Earth assigns none
`continent`	string	Continent
`subregion`	string	UN subregion
`pop_est`	number	Population estimate (persons)
`pop_year`	integer	Year of the estimate
`gdp_md`	number	GDP, millions of US dollars (USD m)
`gdp_year`	integer	Year of the GDP figure
`economy`	string	Natural Earth economy class
`income_group`	string	World Bank income group

/data/airports.geojson — EPSG:4326; the geometry is in geometry (Point).

column	type	meaning
`airport_id`	string	AP- plus the Natural Earth id
`name`	string	Airport name
`iata_code`	string	Three-letter IATA code; null for a few
`gps_code`	string	Four-character ICAO code; null for a few
`type`	string	major \| mid \| small \| military \| spaceport, and combinations such as 'major and military'
`location`	string	What the point marks: terminal \| ramp \| runway \| parking \| freight \| approximate
`scalerank`	integer	Natural Earth prominence, 2 (largest) to 9

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/countries.geojson · /data/airports.geojson
Required properties: iso_a3, name, airports

Objectives

Read both layers from /data and join points to polygons with a spatial predicate
Count per country without losing the countries that count zero
Assign the finished GeoDataFrame, one row per country, to result

Constraints

These are where the teaching is. Read them twice.

Use a spatial relationship to assign airports to countries
Every country appears, including those with zero
result carries the country geometry with iso_a3, name and airports

Graded requirements

Assigns airports to countries with a spatial predicate
One row per country
Carries iso_a3, name and airports
Airports contained by any country

Points-per-polygon over a coarse boundary layer is where analysts first meet generalisation, and keeping the zeros is what lets them see it.

Python

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Files: /data/countries.geojson, /data/airports.geojson · assign result

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What will be checked

4 scored, 0 informational

Assigns airports to countries with a spatial predicate
20%
One row per country
30%
Carries iso_a3, name and airports
20%
Airports contained by any country
30%