World Economy Map

A disk-usage-analyzer style sunburst (like Ubuntu's Baobab) for the world economy.

Live: https://pratikguru998.github.io/world-economy-map/

• Ring 1 — economies, sized by nominal GDP (2024, IMF WEO)
• Ring 2 — industries, sized by share of value added
• Ring 3 — sub-industries (e.g. Manufacturing → Autos, Pharma, Electronics…)
• Ring 4 — companies in each sub-industry, sized by annual revenue (up to 30 per slice, the rest grouped as "+N more")
• India's agriculture and food go one level further: crops → rice, wheat, vegetables, pulses…; livestock → milk, meat, eggs; fishing; forestry; and food manufacturing → dairy, edible oils, sugar, beverages… each with a one-line fact and the companies behind it (MoSPI value of output, 2023-24).

Click a slice to zoom in, click the centre to go back, hover for details. Switch between By country (World → Country → Industry → Company) and By industry (World → Industry → Country → Company), or search for any country, industry or company.

A note on the numbers

Figures are approximate and rounded, for orientation only. Company revenue is gross sales, not value added, so large firms can exceed their industry's GDP share; those slices are scaled down to fit and marked ⚠. "Rest of …" slices stand for smaller firms, public bodies and households.

Data

• data.js — GDP, industry shares, the sub-industry taxonomy, national-accounts sub-industry shares for the five largest economies, and hand-curated companies.
• companies.js — generated: ~3,200 companies (name, revenue in USD bn, sub-industry, source, year), merged from screener.in (India's ~300 largest listed companies by sales, with NSE industries), the curated list, Wikipedia's "largest companies in …" tables and Wikidata revenue statements, converted to USD, classified into sub-industries and de-duplicated.

Rebuild companies.js:

python3 scripts/fetch_sources.py /tmp/wem-raw
python3 scripts/build_companies.py /tmp/wem-raw

India comes from screener.in: run scripts/fetch_screener.js in the browser console while logged in to screener.in and save the copied JSON as /tmp/wem-raw/screener_india.json before building. Consolidated sales are adjusted for listed subsidiaries in scripts/screener_adjustments.json; industries map to sub-industries via scripts/screener_industries.json.

Fix a misclassified company in scripts/sub_overrides.json ("Name": "sub" or "Name": ["sector", "sub"]).

Licences

Company data derived from Wikipedia is available under CC BY-SA 4.0; Wikidata is CC0. GDP figures are from the IMF World Economic Outlook.

Run locally

python3 -m http.server 8000