country_name stringclasses 44
values | country_iso3 stringclasses 44
values | year int64 2k 2.02k | Yearly burned area across all land types float64 0 41.2M |
|---|---|---|---|
Algeria | DZA | 2,002 | 35,139.633 |
Algeria | DZA | 2,003 | 90,564.51 |
Algeria | DZA | 2,004 | 109,111 |
Algeria | DZA | 2,005 | 111,343.46 |
Algeria | DZA | 2,006 | 49,800.824 |
Algeria | DZA | 2,007 | 196,434.17 |
Algeria | DZA | 2,008 | 98,013.16 |
Algeria | DZA | 2,009 | 129,095.734 |
Algeria | DZA | 2,010 | 80,411.15 |
Algeria | DZA | 2,011 | 63,002.324 |
Algeria | DZA | 2,012 | 280,043.72 |
Algeria | DZA | 2,013 | 31,554.832 |
Algeria | DZA | 2,014 | 113,361.26 |
Algeria | DZA | 2,015 | 46,945.86 |
Algeria | DZA | 2,016 | 44,455.816 |
Algeria | DZA | 2,017 | 124,587.9 |
Algeria | DZA | 2,018 | 5,473.799 |
Algeria | DZA | 2,019 | 70,579.78 |
Algeria | DZA | 2,020 | 114,778 |
Algeria | DZA | 2,021 | 144,164.77 |
Algeria | DZA | 2,022 | 52,526.984 |
Algeria | DZA | 2,023 | 60,319.09 |
Algeria | DZA | 2,024 | 4,228.777 |
Angola | AGO | 2,002 | 35,153,256 |
Angola | AGO | 2,003 | 41,133,132 |
Angola | AGO | 2,004 | 41,219,188 |
Angola | AGO | 2,005 | 40,519,380 |
Angola | AGO | 2,006 | 35,724,956 |
Angola | AGO | 2,007 | 39,203,800 |
Angola | AGO | 2,008 | 36,649,404 |
Angola | AGO | 2,009 | 36,465,228 |
Angola | AGO | 2,010 | 38,623,470 |
Angola | AGO | 2,011 | 39,672,720 |
Angola | AGO | 2,012 | 37,129,276 |
Angola | AGO | 2,013 | 37,770,030 |
Angola | AGO | 2,014 | 35,341,340 |
Angola | AGO | 2,015 | 36,954,390 |
Angola | AGO | 2,016 | 36,643,332 |
Angola | AGO | 2,017 | 37,779,990 |
Angola | AGO | 2,018 | 31,783,092 |
Angola | AGO | 2,019 | 37,250,988 |
Angola | AGO | 2,020 | 33,826,000 |
Angola | AGO | 2,021 | 34,489,336 |
Angola | AGO | 2,022 | 32,812,144 |
Angola | AGO | 2,023 | 35,726,010 |
Angola | AGO | 2,024 | 37,843,060 |
Benin | BEN | 2,002 | 2,851,504.5 |
Benin | BEN | 2,003 | 2,589,835.5 |
Benin | BEN | 2,004 | 2,083,906.4 |
Benin | BEN | 2,005 | 3,556,078.5 |
Benin | BEN | 2,006 | 2,407,976.5 |
Benin | BEN | 2,007 | 2,584,962.8 |
Benin | BEN | 2,008 | 1,855,938.9 |
Benin | BEN | 2,009 | 1,906,362.2 |
Benin | BEN | 2,010 | 1,787,656 |
Benin | BEN | 2,011 | 2,023,673.1 |
Benin | BEN | 2,012 | 1,745,003.2 |
Benin | BEN | 2,013 | 1,941,952.6 |
Benin | BEN | 2,014 | 1,638,425.2 |
Benin | BEN | 2,015 | 1,597,704.5 |
Benin | BEN | 2,016 | 1,809,272.1 |
Benin | BEN | 2,017 | 1,504,006 |
Benin | BEN | 2,018 | 1,289,604.9 |
Benin | BEN | 2,019 | 1,493,852.6 |
Benin | BEN | 2,020 | 1,438,256 |
Benin | BEN | 2,021 | 1,436,410 |
Benin | BEN | 2,022 | 1,239,975.9 |
Benin | BEN | 2,023 | 1,280,331.6 |
Benin | BEN | 2,024 | 1,089,392.8 |
Botswana | BWA | 2,002 | 8,172,292 |
Botswana | BWA | 2,003 | 1,401,098.6 |
Botswana | BWA | 2,004 | 1,787,741.9 |
Botswana | BWA | 2,005 | 2,796,551.8 |
Botswana | BWA | 2,006 | 5,170,741 |
Botswana | BWA | 2,007 | 2,843,819.5 |
Botswana | BWA | 2,008 | 10,985,115 |
Botswana | BWA | 2,009 | 2,165,906 |
Botswana | BWA | 2,010 | 13,384,119 |
Botswana | BWA | 2,011 | 16,518,221 |
Botswana | BWA | 2,012 | 4,967,030 |
Botswana | BWA | 2,013 | 4,047,346.2 |
Botswana | BWA | 2,014 | 2,858,931.5 |
Botswana | BWA | 2,015 | 1,737,769.2 |
Botswana | BWA | 2,016 | 1,470,884.1 |
Botswana | BWA | 2,017 | 4,821,148 |
Botswana | BWA | 2,018 | 2,755,144 |
Botswana | BWA | 2,019 | 1,179,227.5 |
Botswana | BWA | 2,020 | 3,215,028.8 |
Botswana | BWA | 2,021 | 10,595,294 |
Botswana | BWA | 2,022 | 7,466,472.5 |
Botswana | BWA | 2,023 | 10,573,742 |
Botswana | BWA | 2,024 | 2,191,343.2 |
Burkina Faso | BFA | 2,002 | 3,859,283.8 |
Burkina Faso | BFA | 2,003 | 3,479,810.2 |
Burkina Faso | BFA | 2,004 | 3,761,313.8 |
Burkina Faso | BFA | 2,005 | 4,018,367.2 |
Burkina Faso | BFA | 2,006 | 2,884,690.8 |
Burkina Faso | BFA | 2,007 | 3,097,417.5 |
Burkina Faso | BFA | 2,008 | 2,572,598.5 |
Burkina Faso | BFA | 2,009 | 2,919,444 |
Annual Area Burnt By Wildfires Gwis | Africa (Our World in Data)
🌍 1,242 observations · 54 Africa countries · 2002–2024 · Repackaged by Electric Sheep Africa
TL;DR
This dataset contains 1,242 observations of Annual Area Burnt By Wildfires Gwis data across 54 Africa countries, spanning 2002–2024.
About the source
- Source: Our World in Data
- Publisher: Our World in Data
- License: cc-by-4.0
- Topic: Annual Area Burnt By Wildfires Gwis
Geographic coverage
54 Africa countries · top rows shown below, sorted by row count:
| Country | Rows | First year | Last year |
|---|---|---|---|
AGO |
23 | 2002 | 2024 |
BDI |
23 | 2002 | 2024 |
BEN |
23 | 2002 | 2024 |
BFA |
23 | 2002 | 2024 |
BWA |
23 | 2002 | 2024 |
CAF |
23 | 2002 | 2024 |
CIV |
23 | 2002 | 2024 |
CMR |
23 | 2002 | 2024 |
COD |
23 | 2002 | 2024 |
COG |
23 | 2002 | 2024 |
COM |
23 | 2002 | 2024 |
CPV |
23 | 2002 | 2024 |
DJI |
23 | 2002 | 2024 |
DZA |
23 | 2002 | 2024 |
EGY |
23 | 2002 | 2024 |
| ... | 39 more countries |
Schema
| Column | Type | Description | Example |
|---|---|---|---|
country_name |
string |
— | Algeria |
country_iso3 |
string |
— | DZA |
year |
int64 |
— | 2002 |
Yearly burned area across all land types |
float64 |
— | 35139.633 |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-owid-annual-area-burnt-by-wildfires-gwis")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
kenya = df[df["country_iso3"] == "KEN"]
Time-series for a single indicator
sample = df.sort_values("year")
sample.plot(x="year", y="Yearly burned area across all land types")
Citation
@misc{africa_owid_annual_area_burnt_by_wildfires_gwis_2024,
title = {Annual Area Burnt By Wildfires Gwis | Africa (Our World in Data)},
author = {Our World in Data},
year = {2024},
url = {https://ourworldindata.org/grapher/annual-area-burnt-by-wildfires-gwis},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-owid-annual-area-burnt-by-wildfires-gwis}}
}
License
Released under cc-by-4.0.
Original data © Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.
About Electric Sheep
Electric Sheep Africa is part of the Electric Sheep mission: a unified, ML-ready data layer for Africa on HuggingFace. We pull data from authoritative open sources, normalize the schemas, package as Parquet, and publish with consistent dataset cards so researchers and developers can use load_dataset() to start working in seconds.
Browse the full collection: huggingface.co/electricsheepafrica
Provenance: ingested 2026-06-01 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/annual-area-burnt-by-wildfires-gwis
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