electricsheepasia/asia-myanmar-and-bangladesh-combined-administrative-level-2-boundaries-shapefile

Myanmar and Bangladesh combined administrative level 2 boundaries

Publisher: OCHA Field Information Services Section (FISS) · Source: HDX · License: cc-by-igo · Updated: 2025-04-15

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Abstract

Emergency GIS union of the Bangladesh - Subnational Administrative Boundaries administrative level 2 (district or "zila") features and Myanmar District Boundaries MIMU v9.3 administrative level 2 (district) features - shapefile and gazetteer.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-15. Geographic scope: BGD, MMR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	144
Columns	8 (0 numeric, 8 categorical, 0 datetime)
Train split	115 rows
Test split	28 rows
Geographic scope	BGD, MMR
Publisher	OCHA Field Information Services Section (FISS)
HDX last updated	2025-04-15

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Variables

Identifier / Metadata — adm2_pcode (BD1004, BD1006, BD1009), adm1_pcode (BD30, MMR005, BD20), adm0_pcode (MMR, BD), esa_source (HDX), esa_processed (2026-05-04).

Other — adm2_en (Barguna, Barisal, Bhola), adm1_en (Dhaka, Sagaing, Chittagong), adm0_en (Myanmar, Bangladesh).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-myanmar-and-bangladesh-combined-administrative-level-2-boundaries-shapefile")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
adm2_en	object	0.0%	Barguna, Barisal, Bhola
adm2_pcode	object	0.0%	BD1004, BD1006, BD1009
adm1_en	object	0.0%	Dhaka, Sagaing, Chittagong
adm1_pcode	object	0.0%	BD30, MMR005, BD20
adm0_en	object	0.0%	Myanmar, Bangladesh
adm0_pcode	object	0.0%	MMR, BD
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-05-04

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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.

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Limitations

• Data originates from OCHA Field Information Services Section (FISS) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• This dataset spans 2 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_myanmar_and_bangladesh_combined_administrative_level_2_boundaries_shapefile,
  title     = {Myanmar and Bangladesh combined administrative level 2 boundaries},
  author    = {OCHA Field Information Services Section (FISS)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/myanmar-and-bangladesh-combined-administrative-level-2-boundaries-shapefile},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.