How to Scrape UN SDG Indicators

The UN SDG Global Database combines many reporting programs, units, sources, and disaggregations. A value without that context is easy to misuse. The UN SDG Indicators Scraper exports official observations while retaining goals, targets, indicator numbers, units, nature, dimensions, source, and footnotes.

Query series codes and M49 areas

Begin with one known series and one area, inspect its attributes and dimensions, then scale. This reveals the real observation grain before a large run creates ambiguous duplicates. Record why each series belongs in the project and which disaggregations are acceptable.

{"seriesCodes":["SI_POV_DAY1"],"areaCodes":["840","356"],"yearFrom":2015,"yearTo":2026,"maxResultsPerSeries":100}

The example requests an international-poverty series for the United States and India. Each row includes the numeric value plus the original display value, country name, reporting year, official goal mapping, unit, data nature, disaggregation dimensions, bounds where supplied, and upstream source.

Do not treat missing country-year combinations as zero. Iterate over the required years, preserve sparse results, and report coverage separately from performance. The Nature attribute can distinguish country, estimated, modeled, or global-monitoring data; keep it visible when comparing observations.

For production ingestion, key records on series, area, time period, and the full dimension set. Footnotes can explain breaks, estimation methods, or access dates and should not be discarded. Store raw official rows separately from normalized scores or ESG ratings. That separation keeps downstream methodology reviewable and lets new UN releases update the evidence layer without silently rewriting a proprietary conclusion.