hsr-density-maps

Mapping population density around existing and proposed high-speed rail stations.

Live page: https://mappingmashups.github.io/hsr-density-maps/

Is North American sprawl the real problem for high-speed rail? Metro populations along US corridors look competitive with Europe and Asia. This project checks how many of those people actually live near the stations, using one global population grid and fixed radii (5 / 10 / 20 km, plus 50 km as a metro-scale reference).

Setup

pip install -r requirements.txt
./fetch_data.sh          # Natural Earth outlines: enough to redraw maps from the committed out/hex/
./fetch_data.sh --all    # plus Kontur Population (2.4 GB): needed to recompute populations

out/ holds the computed numbers (JSON/CSV) and the hexagon extract, so you can redraw everything without the big download. Rendered images in out/ are git-ignored; the published copies are WebP files in docs/.

Pipeline

step	script	output
Station list (14 corridors, ~130 stops)	stations.py	–
Population within R of each station	popcalc.py kontur · popcalc.py hrsl	out/pop_kontur.json, out/pop_hrsl.json
Tables	summarize.py	out/stations.csv, out/corridors.csv
Hexagons near stations (for footprints)	extract_hex.py (~1 min)	out/hex/<corridor>.parquet
Same-scale maps	plot_maps.py	out/map_nested.*, out/map_{5,10,20,50}km.* (PNG + SVG)
Station footprint atlases	plot_atlas.py	out/atlas/<corridor>.png, out/atlas_cities.png
Oblique "population as height" view	build_prisms.py	out/prisms.json, out/ground/<corridor>.png (drawn in the browser)
Summary charts	plot_summary.py	out/chart_per_km.png, out/chart_centralisation.png
Web page	build_page.py (template in page/template.html)	docs/ → GitHub Pages (main branch, /docs)

To recompute one corridor after editing stations.py: python popcalc.py kontur <id> and python popcalc.py hrsl <id>, then extract_hex.py, summarize.py, plot_maps.py, plot_atlas.py, build_prisms.py, plot_summary.py, build_page.py. Add a line to LOG in build_page.py for each update; the page shows it as a change log. Then commit docs/ (plus any changed code or out/ data) and push; Pages redeploys automatically.

Data

• Kontur Population 2023-11-01 (primary). Global H3 resolution-8 hexagons (~0.74 km², ~460 m edge). It's a fusion of GHSL, Meta HRSL, Microsoft building footprints and Copernicus land cover. It's free and on S3: geodata-eu-central-1-kontur-public.s3.amazonaws.com/kontur_datasets/kontur_population_20231101.gpkg.gz (2.4 GB gz → 6.7 GB GeoPackage, stored in data/, not committed). CC BY 4.0.
• Meta / CIESIN HRSL (cross-check). 1 arc-second (~30 m) COGs on AWS Open Data (s3://dataforgood-fb-data/hrsl-cogs/hrsl_general/). It's read remotely through /vsicurl/ windowed reads, so nothing needs downloading. It has no Canada coverage, so it can't be the uniform base. Where both datasets cover a station they agree within a few percent; see stations.csv.
• Natural Earth 10 m admin-0 / admin-1 outlines for the map backgrounds.

The WorldPop stats API and GHSL (JRC, 100 m) would be good alternatives; they weren't reachable from the environment this was first built in. The 5 km figures in particular deserve a cross-check against GHSL-POP 100 m.

Method

• A cell counts for a station if its centre is within R km (haversine).
• Dedup: where station circles overlap (Tokyo/Shinagawa, Taipei/Banqiao, LA basin, NYC/Newark), each cell is credited only to its nearest station on the corridor. All corridor totals and map circles use dedup values. Raw per-station values are in stations.csv.
• Maps: each corridor is in its own azimuthal-equidistant projection centred on the corridor and rotated so the route's principal axis is vertical (north arrow in each panel; ROT_OVERRIDE in plot_maps.py forces an angle). 100 km is the same length in every panel. Circle area is proportional to population, and each page says how many people 1 km² of circle represents.
• Footprints: dashed rings mark the real radius at map scale. Where rings overlap they're split along Voronoi lines (halfway between stations), the same rule as the dedup counts (catchment_lines in plot_maps.py). Atlas discs keep each station's full 20 km and mark the split with a dashed line and a pale wash. Inside them, Kontur hexagons are shaded in four density classes (300 / 1,500 / 5,000 / 15,000 people per km²); 300 and 1,500 are the GHSL urban-cluster and urban-centre cut-offs. FOOTPRINT in plot_maps.py also has a two-class "builtup" option. The Kontur GeoPackage has no spatial index, so extract_hex.py selects cells by H3 ID instead of by bounding box.

Caveats

• Planned-route station sites are approximations: city centre or the most-cited candidate site. Results at 5 km are sensitive to this. For example, Texas Central's Houston terminal is at Northwest Mall, not downtown, and that's part of why Houston scores low.
• Windsor–Québec combines the Alto project (Toronto–Québec City) with the older Windsor–Toronto proposal. The Toronto–Peterborough–Ottawa alignment is assumed.
• SF–LA follows CHSRA Phase 1, San Francisco to Anaheim.
• "Share of 50 km population within 10 km" is size-biased: small isolated cities score high automatically. That chart is limited to metros with ≥ 2.5 M people within 50 km.