electricsheepasia/asia-climate-afghanistan-districts-affected-by-winter

Afghanistan - Districts Affected by Winter

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-08-17

Abstract

The Winter affected districts mapping showcases the 2021 winter affected areas to aid planning and resource mobilization is critical to get ahead of winter 2022-2023.

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-08-17. Geographic scope: AFG.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

Variables

Geographic — region (Central Highland, North-eastern, South Eastern), physical_environment_road_status_during_the_winter (In winter, access to remote villages is a concern due to bad road conditions/road block because of snow. , Snow and poor road network cause road block and movement restriction during winter., Physical access challenge in winter).

Identifier / Metadata — prov_code (AF24, AF17, AF23), dist_code (#adm2+code, AF0507, AF0504), esa_source (HDX), esa_processed (2026-05-05).

Other — prov_na_eng (Daykundi, Badakhshan, Ghor), dist_na_eng (#adm2+name, Azra, Khoshi), clouser_status_high_medium_low (Medium, High, Low).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-climate-afghanistan-districts-affected-by-winter")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

No numeric columns.

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

• —Data originates from OCHA Afghanistan and has not been independently validated by ESA.
• —Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_climate_afghanistan_districts_affected_by_winter,
  title     = {Afghanistan - Districts Affected by Winter},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-districts-affected-by-winter},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.