electricsheepafrica/africa-north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018

North East Nigeria Health Sector Operational Presence by LGA as of June 2018

Publisher: iMMAP Inc. · Source: HDX · License: cc-by · Updated: 2024-09-13

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Abstract

Both the shapefile and CSV feature North East Nigeria Health Sector Humanitarian Partner Operational Presence by Local Government Area in Borno, Yobe and Adamawa, the three crisis-affected states - as of June 2018.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2024-09-13. Geographic scope: NGA.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Subnational administrative unit observations
Rows (total)	65
Columns	6 (1 numeric, 5 categorical, 0 datetime)
Train split	52 rows
Test split	13 rows
Geographic scope	NGA
Publisher	iMMAP Inc.
HDX last updated	2024-09-13

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Variables

Geographic — state (Borno, Adamawa, Yobe), lga (Demsa, Jere, Kala-Balge).

Outcome / Measurement — number (range 0.0–18.0).

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-08).

Other — partner_presence (CRS, FHI 360, UNFPA, UNICEF, WHO, UNICEF, WHO, AAH, UNFPA, UNICEF, WHO).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018")
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
state	object	0.0%	Borno, Adamawa, Yobe
lga	object	0.0%	Demsa, Jere, Kala-Balge
partner_presence	object	3.1%	CRS, FHI 360, UNFPA, UNICEF, WHO, UNICEF, WHO, AAH, UNFPA, UNICEF, WHO
number	int64	0.0%	0.0 – 18.0 (mean 4.9538)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
number	0.0	18.0	4.9538	4.0

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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. 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 iMMAP Inc. 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_africa_north_east_nigeria_health_sector_operational_presence_by_lga_as_of_june_2018,
  title     = {North East Nigeria Health Sector Operational Presence by LGA as of June 2018},
  author    = {iMMAP Inc.},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/north-east-nigeria-health-sector-operational-presence-by-lga-as-of-june-2018},
  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.