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electricsheepasia/asia-owid-area-burned-wildfires-by-type

Area Burned Wildfires By Type | Asia (Our World in Data) ๐ŸŒ 1,104 observations ยท 48 Asia countries ยท 2002โ€“2024 ยท Repackaged by Electric Sheep Asia TL;DR This dataset contains 1,104 observations of Area Burned Wildfires By Type data across 48 Asia countries, spanning 2002โ€“2024. About the source Source: Our World in Data Publisher: Our World in Data License: cc-by-4.0 Topic: Area Burned Wildfires By Type Geographic coverage 48โ€ฆ See the full description on the dataset page: https://huggingface.co/datasets/electricsheepasia/asia-owid-area-burned-wildfires-by-type.

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Dataset Card

Area Burned Wildfires By Type | Asia (Our World in Data)

๐ŸŒ 1,104 observations ยท 48 Asia countries ยท 2002โ€“2024 ยท Repackaged by [Electric Sheep Asia](https://huggingface.co/electricsheepasia)

rows countries years license

TL;DR

This dataset contains 1,104 observations of Area Burned Wildfires By Type data across 48 Asia countries, spanning 2002โ€“2024.

About the source

  • โ€”Source: Our World in Data
  • โ€”Publisher: Our World in Data
  • โ€”License: cc-by-4.0
  • โ€”Topic: Area Burned Wildfires By Type

Geographic coverage

48 Asia countries ยท top rows shown below, sorted by row count:

CountryRowsFirst yearLast year
AFG2320022024
ARE2320022024
ARM2320022024
AZE2320022024
BGD2320022024
BHR2320022024
BRN2320022024
BTN2320022024
CHN2320022024
CYP2320022024
GEO2320022024
IDN2320022024
IND2320022024
IRN2320022024
IRQ2320022024
...33 more countries

Schema

ColumnTypeDescriptionExample
country_namestringโ€”Afghanistan
country_iso3stringโ€”AFG
yearint64โ€”2002
Shrublands and grasslandsfloat64โ€”18052.795
Savannasfloat64โ€”0.0
Forestsfloat64โ€”0.0
Croplandsfloat64โ€”14167.473

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepasia/asia-owid-area-burned-wildfires-by-type")
df = ds["train"].to_pandas()
print(df.head())

Filter to one country

python
indonesia = df[df["country_iso3"] == "IDN"]

Time-series for a single indicator

python
sample = df.sort_values("year")
sample.plot(x="year", y="Shrublands and grasslands")

Citation

bibtex
@misc{asia_owid_area_burned_wildfires_by_type_2024,
  title        = {Area Burned Wildfires By Type | Asia (Our World in Data)},
  author       = {Our World in Data},
  year         = {2024},
  url          = {https://ourworldindata.org/grapher/area-burned-wildfires-by-type},
  publisher    = {HuggingFace Datasets, repackaged by Electric Sheep Asia},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepasia/asia-owid-area-burned-wildfires-by-type}}
}

License

Released under cc-by-4.0.

Original data ยฉ Our World in Data. When using this dataset, please cite both the original source above and the Electric Sheep Asia repackaging.

About Electric Sheep

Electric Sheep Asia is part of the Electric Sheep mission: a unified, ML-ready data layer for Asia 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/electricsheepasia


Provenance: ingested 2026-06-02 via the Electric Sheep pipeline. Source URL: https://ourworldindata.org/grapher/area-burned-wildfires-by-type