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

Mali: JIAF Humanitarian Needs People in Need and Severity

Publisher: OCHA Mali · Source: HDX · License: cc-by · Updated: 2026-02-19

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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 2026-02-19. Geographic scope: MLI.

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)	642
Columns	19 (11 numeric, 8 categorical, 0 datetime)
Train split	513 rows
Test split	128 rows
Geographic scope	MLI
Publisher	OCHA Mali
HDX last updated	2026-02-19

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Variables

Geographic — populaton (range 0.0–4600332.6094).

Identifier / Metadata — unnamed_0 (ID, RapatriesML0101, PDI RetourneesML1202), unnamed_2 (ML07, ML06, ML04), unnamed_3 (Timétrine, Bamako, KITA), unnamed_4 (ML0807, ML2001, ML1201), unnamed_9 (Communauté Hôte, PDI, PDI Retournees) and 11 others.

Other — lieu (GAO, TOMBOUCTOU, SEGOU), pin_définitif (range 0.0–268014.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-mli-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
unnamed_0	object	0.0%	ID, RapatriesML0101, PDI RetourneesML1202
lieu	object	0.0%	GAO, TOMBOUCTOU, SEGOU
unnamed_2	object	0.0%	ML07, ML06, ML04
unnamed_3	object	0.0%	Timétrine, Bamako, KITA
unnamed_4	object	0.0%	ML0807, ML2001, ML1201
populaton	float64	0.3%	0.0 – 4600332.6094 (mean 39951.5143)
unnamed_9	object	0.2%	Communauté Hôte, PDI, PDI Retournees
unnamed_11	float64	0.2%	0.0 – 116824.0 (mean 2746.3869)
unnamed_12	float64	0.2%	0.0 – 776939.0 (mean 5044.6193)
unnamed_13	float64	0.2%	0.0 – 323510.0 (mean 3177.7126)
unnamed_14	float64	0.2%	0.0 – 88914.0 (mean 3029.8534)
unnamed_15	float64	0.2%	0.0 – 257892.0 (mean 7842.0078)
unnamed_16	float64	0.2%	0.0 – 350366.0 (mean 6318.4626)
unnamed_17	float64	0.2%	0.0 – 154314.9827 (mean 4653.1454)
unnamed_18	float64	0.2%	1.0 – 4.0 (mean 1.8658)
unnamed_19	float64	0.2%	0.0 – 268014.0 (mean 7921.7482)
pin_définitif	float64	0.2%	0.0 – 268014.0 (mean 8327.0431)
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
populaton	0.0	4600332.6094	39951.5143	8.0
unnamed_11	0.0	116824.0	2746.3869	0.0
unnamed_12	0.0	776939.0	5044.6193	0.0
unnamed_13	0.0	323510.0	3177.7126	0.0
unnamed_14	0.0	88914.0	3029.8534	0.0
unnamed_15	0.0	257892.0	7842.0078	0.0
unnamed_16	0.0	350366.0	6318.4626	0.0
unnamed_17	0.0	154314.9827	4653.1454	0.0
unnamed_18	1.0	4.0	1.8658	1.0
unnamed_19	0.0	268014.0	7921.7482	0.0
pin_définitif	0.0	268014.0	8327.0431	0.0

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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. 7 column(s) with >80% missing values were removed: unnamed_5, unnamed_6, unnamed_7, pin_des_clusters_nombre, unnamed_21, unnamed_22.... 11 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 Mali 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_mli_jiaf_humanitarian_needs_pin_and_severity,
  title     = {Mali: JIAF Humanitarian Needs People in Need and Severity},
  author    = {OCHA Mali},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mli-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.