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

Central African Republic: JIAF Humanitarian Needs People in Need and Severity

Publisher: OCHA Central African Republic · Source: HDX · License: cc-by · Updated: 2026-02-02

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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 2026-02-02. Geographic scope: CAF.

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)	391
Columns	19 (11 numeric, 8 categorical, 0 datetime)
Train split	312 rows
Test split	78 rows
Geographic scope	CAF
Publisher	OCHA Central African Republic
HDX last updated	2026-02-02

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Variables

Geographic — populaton (Autre_Hôte, Hôte, IDP_FA), preliminary_pin (range 2.0–4.0).

Outcome / Measurement — sectoral_pin_number (range 0.0–46864.0).

Identifier / Metadata — unnamed_1 (CF63, CF12, CF62), unnamed_2 (Mbrès, Boda, Ndim), unnamed_3 (CF422, CF123, CF343), unnamed_6 (range 0.0–38340.0), unnamed_7 (range 0.0–22111.0) and 9 others.

Other — lieu ( Haut-Mbomou,  Lobaye,  Mbomou), final_pin (range 0.0–103715.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-caf-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%	Haut-Mbomou,  Lobaye,  Mbomou
unnamed_1	object	0.0%	CF63, CF12, CF62
unnamed_2	object	0.0%	Mbrès, Boda, Ndim
unnamed_3	object	0.0%	CF422, CF123, CF343
populaton	object	0.0%	Autre_Hôte, Hôte, IDP_FA
sectoral_pin_number	float64	0.3%	0.0 – 46864.0 (mean 1055.4308)
unnamed_6	float64	0.3%	0.0 – 38340.0 (mean 1501.2051)
unnamed_7	float64	0.3%	0.0 – 22111.0 (mean 859.7564)
unnamed_8	float64	0.3%	0.0 – 103715.0 (mean 4524.5077)
unnamed_9	float64	0.3%	0.0 – 46864.0 (mean 2448.7385)
unnamed_10	float64	0.3%	0.0 – 35990.0 (mean 2674.9846)
unnamed_11	float64	0.3%	0.0 – 30743.0 (mean 771.1538)
unnamed_12	float64	0.3%	0.0 – 34173.0 (mean 2810.3641)
preliminary_pin	float64	0.3%	2.0 – 4.0 (mean 3.1077)
unnamed_14	float64	0.3%	0.0 – 103715.0 (mean 5676.8179)
final_pin	float64	0.3%	0.0 – 103715.0 (mean 5676.8179)
unnamed_17	object	0.0%	Oui, Scope
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
sectoral_pin_number	0.0	46864.0	1055.4308	0.0
unnamed_6	0.0	38340.0	1501.2051	89.5
unnamed_7	0.0	22111.0	859.7564	0.0
unnamed_8	0.0	103715.0	4524.5077	309.0
unnamed_9	0.0	46864.0	2448.7385	0.0
unnamed_10	0.0	35990.0	2674.9846	348.0
unnamed_11	0.0	30743.0	771.1538	0.0
unnamed_12	0.0	34173.0	2810.3641	217.5
preliminary_pin	2.0	4.0	3.1077	3.0
unnamed_14	0.0	103715.0	5676.8179	572.0
final_pin	0.0	103715.0	5676.8179	572.0

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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. 1 column(s) with >80% missing values were removed: unnamed_16. 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 Central African Republic 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_caf_jiaf_humanitarian_needs_pin_and_severity,
  title     = {Central African Republic: JIAF Humanitarian Needs People in Need and Severity},
  author    = {OCHA Central African Republic},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/caf-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.