electricsheepafrica/africa-boderlands-analysis-dataset

East Africa Borderlands Indicators

Publisher: OCHA Regional Office for Southern and Eastern Africa (ROSEA) · Source: HDX · License: other-pd-nr · Updated: 2023-03-03

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Abstract

This is a dataset containing information on various selected indicators from the borderland districts in Kenya, Uganda, Somalia, Ethiopia and South Sudan.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2023-03-03. Geographic scope: ETH, KEN, SOM, SSD, UGA.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Tabular records
Rows (total)	105
Columns	17 (10 numeric, 7 categorical, 0 datetime)
Train split	84 rows
Test split	21 rows
Geographic scope	ETH, KEN, SOM, SSD, UGA
Publisher	OCHA Regional Office for Southern and Eastern Africa (ROSEA)
HDX last updated	2023-03-03

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Variables

Geographic — poverty_incidence (range 28.7–94.3), births_attended_by_skilled_birth_attendants_women_assisted_by_health_worker (range 0.0–0.82).

Demographic — vaccination_coverage (range 0.0–1.58), hhs_cooking_fuel_firewood (range 68.0–98.6).

Outcome / Measurement — pri_ger_total (range 0.06–2.562).

Identifier / Metadata — unnamed_0 (ETH, KEN, SOM), unnamed_1 (Ethiopia, Kenya, Somalia), unnamed_2 (Southern Nations, Nationalities and Peoples, Oromia, Somali), unnamed_3 (2nd Admin, Kembata Alaba Tembaro, Lafey), unnamed_4 (District, Sub County, Type) and 3 others.

Other — gini_coefficient (range 0.169–0.56), access_to_improved_sanitation (range 0.0–0.98), prevalence_of_wasting_weight_for_height_mam (range 0.015–0.275), gam (range 5.9–39.2).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-boderlands-analysis-dataset")
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
unnamed_0	object	1.0%	ETH, KEN, SOM
unnamed_1	object	1.0%	Ethiopia, Kenya, Somalia
unnamed_2	object	1.0%	Southern Nations, Nationalities and Peoples, Oromia, Somali
unnamed_3	object	12.4%	2nd Admin, Kembata Alaba Tembaro, Lafey
unnamed_4	object	11.4%	District, Sub County, Type
poverty_incidence	float64	60.0%	28.7 – 94.3 (mean 72.4881)
gini_coefficient	float64	74.3%	0.169 – 0.56 (mean 0.3109)
pri_ger_total	float64	59.0%	0.06 – 2.562 (mean 0.3957)
access_to_improved_water_source	float64	62.9%	0.255 – 0.87 (mean 0.4684)
access_to_improved_sanitation	float64	37.1%	0.0 – 0.98 (mean 0.4379)
vaccination_coverage	float64	60.0%	0.0 – 1.58 (mean 0.7016)
births_attended_by_skilled_birth_attendants_women_assisted_by_health_worker	float64	62.9%	0.0 – 0.82 (mean 0.2945)
hhs_cooking_fuel_firewood	float64	66.7%	68.0 – 98.6 (mean 89.9986)
prevalence_of_wasting_weight_for_height_mam	float64	76.2%	0.015 – 0.275 (mean 0.0751)
gam	float64	75.2%	5.9 – 39.2 (mean 18.1615)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-18

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Numeric Summary

Column	Min	Max	Mean	Median
poverty_incidence	28.7	94.3	72.4881	82.55
gini_coefficient	0.169	0.56	0.3109	0.289
pri_ger_total	0.06	2.562	0.3957	0.262
access_to_improved_water_source	0.255	0.87	0.4684	0.44
access_to_improved_sanitation	0.0	0.98	0.4379	0.3692
vaccination_coverage	0.0	1.58	0.7016	0.73
births_attended_by_skilled_birth_attendants_women_assisted_by_health_worker	0.0	0.82	0.2945	0.297
hhs_cooking_fuel_firewood	68.0	98.6	89.9986	94.0
prevalence_of_wasting_weight_for_height_mam	0.015	0.275	0.0751	0.055
gam	5.9	39.2	18.1615	17.95

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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. 28 column(s) with >80% missing values were removed: mpi, primary_school_gross_enrolment_ratio, unnamed_9, secondary_school_gross_enrolment_ratio, unnamed_12, sec_ger_total.... 3 exact duplicate rows were removed. 2 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: poverty_incidence, gini_coefficient, pri_ger_total, access_to_improved_water_source, access_to_improved_sanitation, vaccination_coverage, births_attended_by_skilled_birth_attendants_women_assisted_by_health_worker, hhs_cooking_fuel_firewood....
• This dataset spans 5 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_boderlands_analysis_dataset,
  title     = {East Africa Borderlands Indicators},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/boderlands-analysis-dataset},
  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.