electricsheepafrica/africa-bfa-jiaf-humanitarian-needs-pin-and-severity

Burkina Faso: JIAF Humanitarian Needs People in Need and Severity

Publisher: OCHA Burkina Faso · Source: HDX · License: cc-by · Updated: 2026-02-27

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Abstract

This dataset contains the overall people in need and intersectoral severity by disaggregation level which Includes administrative divisions and population groups, depending on each country's decision. The dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners using the Joint Intersectoral Analysis Framework(JIAF).

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2026-02-27. Geographic scope: BFA.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Geolocated point observations
Rows (total)	142
Columns	19 (12 numeric, 7 categorical, 0 datetime)
Train split	113 rows
Test split	28 rows
Geographic scope	BFA
Publisher	OCHA Burkina Faso
HDX last updated	2026-02-27

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Variables

Geographic — population (range 0.0–3703271.0969).

Identifier / Metadata — unnamed_2 (BF57, BF50, BF54), unnamed_3 (Ganzourgou, Kadiogo, Bassitenga), unnamed_4 (BF5501, BF1300, BF5503), unnamed_7 (Non deplaces, PDI, Retournes), unnamed_9 (range 0.0–179099.0) and 10 others.

Other — lieu (Djôrô, Nando, Yaadga), pin_des_clusters_nombre (range 0.0–146625.0), pin_définitif (range 0.0–244375.8341).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-bfa-jiaf-humanitarian-needs-pin-and-severity")
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
lieu	object	0.0%	Djôrô, Nando, Yaadga
unnamed_2	object	0.0%	BF57, BF50, BF54
unnamed_3	object	0.0%	Ganzourgou, Kadiogo, Bassitenga
unnamed_4	object	0.0%	BF5501, BF1300, BF5503
population	float64	0.7%	0.0 – 3703271.0969 (mean 170713.1386)
unnamed_7	object	0.0%	Non deplaces, PDI, Retournes
pin_des_clusters_nombre	float64	0.7%	0.0 – 146625.0 (mean 6533.3759)
unnamed_9	float64	0.7%	0.0 – 179099.0 (mean 13321.5914)
unnamed_10	float64	0.7%	0.0 – 103947.1589 (mean 9804.7082)
unnamed_11	float64	0.7%	0.0 – 164455.9873 (mean 15188.4642)
unnamed_12	float64	0.7%	0.0 – 141521.4 (mean 15416.3305)
unnamed_13	float64	0.7%	0.0 – 218026.0 (mean 23874.383)
unnamed_14	float64	0.7%	0.0 – 242470.0 (mean 11668.4184)
unnamed_15	float64	0.7%	0.0 – 207158.0 (mean 16836.0851)
unnamed_16	float64	0.7%	2.0 – 4.0 (mean 2.8298)
unnamed_17	float64	0.7%	0.0 – 244375.8341 (mean 34093.3014)
pin_définitif	float64	0.7%	0.0 – 244375.8341 (mean 31435.0608)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-17

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Numeric Summary

Column	Min	Max	Mean	Median
population	0.0	3703271.0969	170713.1386	38949.0
pin_des_clusters_nombre	0.0	146625.0	6533.3759	0.0
unnamed_9	0.0	179099.0	13321.5914	3648.0
unnamed_10	0.0	103947.1589	9804.7082	0.0
unnamed_11	0.0	164455.9873	15188.4642	600.5907
unnamed_12	0.0	141521.4	15416.3305	0.0
unnamed_13	0.0	218026.0	23874.383	7740.0
unnamed_14	0.0	242470.0	11668.4184	2758.0
unnamed_15	0.0	207158.0	16836.0851	426.0
unnamed_16	2.0	4.0	2.8298	3.0
unnamed_17	0.0	244375.8341	34093.3014	9377.8792
pin_définitif	0.0	244375.8341	31435.0608	9377.8792

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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: unnamed_0, unnamed_5. 12 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 Burkina Faso 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_bfa_jiaf_humanitarian_needs_pin_and_severity,
  title     = {Burkina Faso: JIAF Humanitarian Needs People in Need and Severity},
  author    = {OCHA Burkina Faso},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/bfa-jiaf-humanitarian-needs-pin-and-severity},
  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.