electricsheepasia/asia-cod-em-qat

Qatar - Subnational Edge-matched Administrative Boundaries

Publisher: OCHA Field Information Services Section (FISS) · Source: HDX · License: cc-by-igo · Updated: 2025-06-24

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Abstract

Qatar administrative level 0-2 boundaries and gazetteer

These administrative level 0-1 layers are suitable for database or GIS linkage to the Qatar - Subnational Population Statistics tables using the ADM0 and ADM1_PCODE fields.

Vetting by Information Technology Outreach Services (ITOS) with funding from USAID.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-06-24. Geographic scope: QAT.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Tabular records
Rows (total)	781
Columns	15 (1 numeric, 14 categorical, 0 datetime)
Train split	624 rows
Test split	156 rows
Geographic scope	QAT
Publisher	OCHA Field Information Services Section (FISS)
HDX last updated	2025-06-24

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Variables

Identifier / Metadata — adm3_pcode (QAT001069111, QAT001069812, QAT001069113), adm2_pcode (QAT008073, QAT008085, QAT008072), adm1_pcode (QAT008, QAT003, QAT002), adm0_pcode, esa_source and 1 others.

Other — adm3_en (Umm Ghuwailina, Umm Garn, Abal Hasak), adm3_ar (ام قرن, ابا الحسك, روضة الفرس), adm2_en (Lijmiliya 73, Al Nasraniya 85, Al Utouriya 72), adm2_ar (لجملية 73, النصرانية 85, العطورية 72), adm1_en (Al Sheehaniya Municipality, Al Rayyan Municipality, Al Khor and Al Thakhira Municipality) and 4 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-cod-em-qat")
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
adm3_en	object	0.0%	Umm Ghuwailina, Umm Garn, Abal Hasak
adm3_ar	object	0.0%	ام قرن, ابا الحسك, روضة الفرس
adm3_pcode	object	0.0%	QAT001069111, QAT001069812, QAT001069113
adm2_en	object	0.0%	Lijmiliya 73, Al Nasraniya 85, Al Utouriya 72
adm2_ar	object	0.0%	لجملية 73, النصرانية 85, العطورية 72
adm2_pcode	object	0.0%	QAT008073, QAT008085, QAT008072
adm1_en	object	0.0%	Al Sheehaniya Municipality, Al Rayyan Municipality, Al Khor and Al Thakhira Municipality
adm1_ar	object	0.0%	بلدية الشيحانية, بلدية الريان, بلدية الخور و الذخيرة
adm1_pcode	object	0.0%	QAT008, QAT003, QAT002
adm0_en	object	0.0%	Qatar
adm0_ar	object	0.0%
adm0_pcode	object	0.0%
area_sqkm	float64	0.0%	0.0466 – 131.2499 (mean 14.8957)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
area_sqkm	0.0466	131.2499	14.8957	10.1495

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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.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_asia_cod_em_qat,
  title     = {Qatar - Subnational Edge-matched Administrative Boundaries},
  author    = {OCHA Field Information Services Section (FISS)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/cod-em-qat},
  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.