electricsheepafrica/africa-south-sudan-current-situation-fewsnet-ipc-classification

South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-07

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Abstract

South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the projection_start, projection_end column(s). Geographic scope: SSD.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	3,891
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	3,112 rows
Test split	778 rows
Geographic scope	SSD
Publisher	FEWS NET
HDX last updated	2026-04-07

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Variables

Geographic — country (South Sudan), country_code (SS), fewsnet_region (East Africa), unit_type (fsc_admin), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158825.0–377939.0), reporting_date.

Outcome / Measurement — value (range 1.0–5.0).

Identifier / Metadata — source_organization (FEWS NET), source_document (Food Security Outlook, South Sudan), geographic_unit_full_name (Abiemnhom, Unity, South Sudan, Abyei, Warrap, South Sudan, Raga, Western Bahr el Ghazal, South Sudan), geographic_unit_name (Abiemnhom, Abyei, Raga), fnid (SS2011C11301, SS2011C13302, SS2011C11105) and 8 others.

Other — geographic_group (Eastern Africa), classification_scale, is_allowing_for_assistance, projection_start, projection_end and 12 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-south-sudan-current-situation-fewsnet-ipc-classification")
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
source_organization	object	0.0%	FEWS NET
source_document	object	0.0%	Food Security Outlook, South Sudan
country	object	0.0%	South Sudan
country_code	object	0.0%	SS
geographic_group	object	0.0%	Eastern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	Abiemnhom, Unity, South Sudan, Abyei, Warrap, South Sudan, Raga, Western Bahr el Ghazal, South Sudan
geographic_unit_name	object	0.0%	Abiemnhom, Abyei, Raga
unit_type	object	0.0%	fsc_admin
fnid	object	0.0%	SS2011C11301, SS2011C13302, SS2011C11105
classification_scale	object	0.0%
scenario_name	object	0.0%
preference_rating	int64	0.0%	90.0 – 90.0 (mean 90.0)
is_allowing_for_assistance	bool	0.0%
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
value	float64	2.1%	1.0 – 5.0 (mean 2.6034)
description	object	2.1%
id	int64	0.0%	24403005.0 – 41425124.0 (mean 27079974.2984)
datacollectionperiod	int64	0.0%	158825.0 – 377939.0 (mean 207229.8949)
datacollection	int64	0.0%	168447.0 – 388765.0 (mean 218009.3279)
scenario	object	0.0%
geographic_unit	int64	0.0%	27032.0 – 29864.0 (mean 27644.0995)
datasourceorganization	int64	0.0%	1.0 – 1.0 (mean 1.0)
datasourcedocument	int64	0.0%	6585.0 – 6585.0 (mean 6585.0)
dataseries	int64	0.0%	6495678.0 – 7420829.0 (mean 6574609.8861)
dataseries_name	object	0.0%
specialization_type	object	0.0%
dataseries_specialization_type	object	0.0%
data_usage_policy	object	0.0%
created	datetime64[ns]	0.0%
modified	datetime64[ns]	0.0%
status_changed	datetime64[ns]	0.0%
collection_status	object	0.0%
collection_status_changed	datetime64[ns]	0.0%
collection_schedule	object	0.0%
reporting_date	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
preference_rating	90.0	90.0	90.0	90.0
value	1.0	5.0	2.6034	3.0
id	24403005.0	41425124.0	27079974.2984	24536313.0
datacollectionperiod	158825.0	377939.0	207229.8949	159803.0
datacollection	168447.0	388765.0	218009.3279	168797.0
geographic_unit	27032.0	29864.0	27644.0995	27081.0
datasourceorganization	1.0	1.0	1.0	1.0
datasourcedocument	6585.0	6585.0	6585.0	6585.0
dataseries	6495678.0	7420829.0	6574609.8861	6511351.0

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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. 3 column(s) with >80% missing values were removed: pct_phase3, pct_phase4, pct_phase5. 7 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 FEWS NET 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_south_sudan_current_situation_fewsnet_ipc_classification,
  title     = {South Sudan Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/south_sudan_current_situation_fewsnet_ipc_classification},
  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.