A climate policy team is about to brief a global 2010-to-2022 CO2-per-capita comparison. The analyst's trend map silently drops places without a value at either date. The team needs a companion map showing where comparison is possible and where the source needs review, so absence is not read as zero change.
Use /data/countries.geojson for country polygons and /data/indicators.geojson for World Bank WDI values carried by representative points. The Python runner mounts these at /data/<dataset id>.geojson; assign a GeoDataFrame to result without writing a file.
Keep every country polygon. Join indicators by iso_a3 and the exact country name (countries.name to indicators.country) so Natural Earth's nonstandard ISO codes cannot multiply rows. Convert co2_pc_2010 and co2_pc_2022 to numeric. Set complete_flag=1 only when both years exist, else 0; missing_flag=1-complete_flag; and map_readiness to comparable when complete, otherwise source review needed. Retain country_id, iso_a3, name, both original values, both flags, class, and polygon geometry in EPSG:4326.
Shade the result by map readiness. A country missing data is a coverage gap, not a zero emissions value. These World Bank country statistics are not observations at the representative points; use the supplied polygons. The source layer is a mix of sovereign states and dependencies at Natural Earth 1:50m scale and is unsuitable for local boundary decisions. Run shows your output and map without grading; Submit grades it.
/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/indicators.geojson — EPSG:4326; the geometry is in geometry (Point).
| column | type | meaning |
|---|---|---|
iso_a3 | string | Natural Earth ISO3 country key |
country | string | Natural Earth country name |
co2_pc_2010 | number | CO2 emissions excluding LULUCF per capita in 2010; null where unavailable (t CO2e/person) |
co2_pc_2022 | number | CO2 emissions excluding LULUCF per capita in 2022; null where unavailable (t CO2e/person) |
These are where the teaching is. Read them twice.
World Bank WDI country indicator EN.GHG.CO2.PC.CE.AR5: https://api.worldbank.org/v2/indicator/EN.GHG.CO2.PC.CE.AR5?format=json . Penn State's cartography curriculum teaches mapmaking for decision support: https://geospatial.psu.edu/geog486 .
Files: /data/countries.geojson, /data/indicators.geojson · assign result
6 scored, 0 informational
Full geography remains in the audit
23%Original country polygons are retained
23%Coverage and original indicator fields
15%Countries with a comparable pair
15%Places needing source review
15%Uses the supplied country polygons
8%
