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

South Sudan: JIAF Humanitarian Needs People in Need and Severity

Publisher: OCHA South Sudan · 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: SSD.

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)	239
Columns	19 (12 numeric, 7 categorical, 0 datetime)
Train split	191 rows
Test split	47 rows
Geographic scope	SSD
Publisher	OCHA South Sudan
HDX last updated	2026-02-02

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Variables

Geographic — location (Upper Nile, Jonglei, Western Equatoria), populaton (range 0.0–310546.0911).

Outcome / Measurement — sectoral_pin_number (range 0.0–1359005.6655).

Identifier / Metadata — unnamed_1 (SS07, SS03, SS10), unnamed_2 (Abiemnhom, Abyei Administrative Area, Panyikang), unnamed_3 (SS0601, SS0001, SS0710), unnamed_8 (IDPs, Residents, Returnees), unnamed_10 (range 0.0–2435900.2641) and 11 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ssd-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
location	object	0.4%	Upper Nile, Jonglei, Western Equatoria
unnamed_1	object	0.4%	SS07, SS03, SS10
unnamed_2	object	0.4%	Abiemnhom, Abyei Administrative Area, Panyikang
unnamed_3	object	0.4%	SS0601, SS0001, SS0710
populaton	float64	0.8%	0.0 – 310546.0911 (mean 50521.7222)
unnamed_8	object	0.4%	IDPs, Residents, Returnees
sectoral_pin_number	float64	33.5%	0.0 – 1359005.6655 (mean 17094.4109)
unnamed_10	float64	0.4%	0.0 – 2435900.2641 (mean 20469.7501)
unnamed_11	float64	0.4%	0.0 – 5250932.6035 (mean 44125.4841)
unnamed_12	float64	0.4%	0.0 – 7650961.5531 (mean 64293.7946)
unnamed_13	float64	0.4%	0.0 – 6269692.2333 (mean 52686.4894)
unnamed_14	float64	0.4%	0.0 – 4867750.0 (mean 40905.4622)
unnamed_15	float64	0.4%	0.0 – 6669355.3915 (mean 56045.0033)
unnamed_16	float64	0.4%	0.0 – 6806494.7796 (mean 57197.4351)
unnamed_17	float64	0.8%	4.0 – 4.0 (mean 4.0)
unnamed_18	float64	0.8%	0.0 – 233351.6448 (mean 39099.4135)
unnamed_19	float64	0.8%	0.0 – 233351.6448 (mean 39099.4135)
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	310546.0911	50521.7222	29744.7143
sectoral_pin_number	0.0	1359005.6655	17094.4109	847.65
unnamed_10	0.0	2435900.2641	20469.7501	5108.3088
unnamed_11	0.0	5250932.6035	44125.4841	11413.4697
unnamed_12	0.0	7650961.5531	64293.7946	16467.9497
unnamed_13	0.0	6269692.2333	52686.4894	14996.2012
unnamed_14	0.0	4867750.0	40905.4622	12846.5
unnamed_15	0.0	6669355.3915	56045.0033	15357.678
unnamed_16	0.0	6806494.7796	57197.4351	14223.8992
unnamed_17	4.0	4.0	4.0	4.0
unnamed_18	0.0	233351.6448	39099.4135	24236.4339
unnamed_19	0.0	233351.6448	39099.4135	24236.4339

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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. 4 column(s) with >80% missing values were removed: unnamed_4, unnamed_5, unnamed_6, unnamed_20. 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 South Sudan 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: sectoral_pin_number.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_ssd_jiaf_humanitarian_needs_pin_and_severity,
  title     = {South Sudan: JIAF Humanitarian Needs People in Need and Severity},
  author    = {OCHA South Sudan},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ssd-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.