electricsheepafrica/africa-global-operational-presence

Global Operational Presence

Publisher: HDX · Source: HDX · License: cc-by-igo · Updated: 2025-03-10

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Abstract

This dataset contains standardised Operational Presence data

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the start_date, end_date column(s). Geographic scope: AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 17 others.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	Country-level aggregates
Rows (total)	38,617
Columns	15 (1 numeric, 12 categorical, 2 datetime)
Train split	30,893 rows
Test split	7,723 rows
Geographic scope	AFG, BFA, BDI, CMR, CAF, TCD, COL, COD, and 17 others
Publisher	HDX
HDX last updated	2025-03-10

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Variables

Geographic — country_iso3 (AFG, YEM, SOM), admin_1_pcode (AF06, YE15, MZ01), admin_1_name (Nangarhar, Ta'iz, Cabo Delgado), admin_2_pcode (SO2201, SO2401, CM007005), admin_2_name (Banadir, Baydhaba, Mezam) and 2 others.

Temporal — start_date, end_date.

Identifier / Metadata — org_name (United Nations Children's Fund, UN Refugee Agency, World Food Programme), dataset_id (d3575877-d3b6-4eec-a116-d9b8a633a399, 3a4d0513-9aeb-4177-9f6d-448c7e740463, be09b1a5-e457-448f-8d2d-63b0debc1742), resource_id (6b43d67e-ef25-46a4-b6ba-b0605324043e, a001ae78-3f6d-4d09-9e3e-05090e1c8e45, 1176ba40-f695-4edd-ab07-90e08226e52e), esa_source, esa_processed.

Other — sector (PRO, HEA, FSC).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-global-operational-presence")
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
country_iso3	object	0.0%	AFG, YEM, SOM
admin_1_pcode	object	1.4%	AF06, YE15, MZ01
admin_1_name	object	1.8%	Nangarhar, Ta'iz, Cabo Delgado
admin_2_pcode	object	5.0%	SO2201, SO2401, CM007005
admin_2_name	object	4.4%	Banadir, Baydhaba, Mezam
org_name	object	0.0%	United Nations Children's Fund, UN Refugee Agency, World Food Programme
org_acronym	object	0.0%	UNICEF, UNHCR, WFP
org_type	float64	3.3%	433.0 – 504.0 (mean 440.9388)
sector	object	0.0%	PRO, HEA, FSC
start_date	datetime64[ns]	0.0%
end_date	datetime64[ns]	0.0%
dataset_id	object	0.0%	d3575877-d3b6-4eec-a116-d9b8a633a399, 3a4d0513-9aeb-4177-9f6d-448c7e740463, be09b1a5-e457-448f-8d2d-63b0debc1742
resource_id	object	0.0%	6b43d67e-ef25-46a4-b6ba-b0605324043e, a001ae78-3f6d-4d09-9e3e-05090e1c8e45, 1176ba40-f695-4edd-ab07-90e08226e52e
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
org_type	433.0	504.0	440.9388	441.0

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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. 2 column(s) with >80% missing values were removed: admin_3_pcode, admin_3_name. 1,832 exact duplicate rows were removed. 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 HDX 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 25 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_africa_global_operational_presence,
  title     = {Global Operational Presence},
  author    = {HDX},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/global-operational-presence},
  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.