electricsheepafrica/africa-somalia-current-situation-fewsnet-ipc-classification

Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-01

---

Abstract

Somalia 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: SOM.

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

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	11,297
Columns	40 (10 numeric, 23 categorical, 7 datetime)
Train split	9,037 rows
Test split	2,259 rows
Geographic scope	SOM
Publisher	FEWS NET
HDX last updated	2026-04-01

---

Variables

Geographic — country (Somalia), country_code (SO), fewsnet_region (East Africa), unit_type (fsc_admin_lhz, idp_camp), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158634.0–378217.0), reporting_date.

Outcome / Measurement — value (range 0.0–5.0).

Identifier / Metadata — source_organization (FEWS NET, Somalia), source_document (Food Security Outlook, Somalia), geographic_unit_full_name (Urban, Afgooye, Lower Shabelle, Somalia, Urban, Mogadishu, Banaadir, Somalia, Addun Pastoral, Cadaado, Galgaduud, Somalia), geographic_unit_name (Southern Inland Pastoral – Camels, Goats, Sheep, and Cattle, Southern Inland Pastoral: Camel, Sheep & Goats, Hawd Pastoral), fnid (SO2016C3190309, SO2016C3230515, SO2016C3200212) and 8 others.

Other — geographic_group (Eastern Africa), classification_scale, is_allowing_for_assistance (range 0.0–1.0), projection_start, projection_end and 12 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-somalia-current-situation-fewsnet-ipc-classification")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
source_organization	object	0.0%	FEWS NET, Somalia
source_document	object	0.0%	Food Security Outlook, Somalia
country	object	0.0%	Somalia
country_code	object	0.0%	SO
geographic_group	object	0.0%	Eastern Africa
fewsnet_region	object	0.0%	East Africa
geographic_unit_full_name	object	0.0%	Urban, Afgooye, Lower Shabelle, Somalia, Urban, Mogadishu, Banaadir, Somalia, Addun Pastoral, Cadaado, Galgaduud, Somalia
geographic_unit_name	object	0.0%	Southern Inland Pastoral – Camels, Goats, Sheep, and Cattle, Southern Inland Pastoral: Camel, Sheep & Goats, Hawd Pastoral
unit_type	object	0.0%	fsc_admin_lhz, idp_camp
fnid	object	0.0%	SO2016C3190309, SO2016C3230515, SO2016C3200212
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	float64	11.4%	0.0 – 1.0 (mean 0.1449)
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
value	float64	0.0%	0.0 – 5.0 (mean 2.3281)
description	object	0.0%
id	int64	0.0%	24372083.0 – 41457433.0 (mean 26827672.1944)
datacollectionperiod	int64	0.0%	158634.0 – 378217.0 (mean 197785.085)
datacollection	int64	0.0%	168351.0 – 389003.0 (mean 208014.5078)
scenario	object	0.0%
geographic_unit	int64	0.0%	26247.0 – 210518.0 (mean 29009.8532)
datasourceorganization	int64	0.0%	2035.0 – 2035.0 (mean 2035.0)
datasourcedocument	int64	0.0%	6581.0 – 6581.0 (mean 6581.0)
dataseries	int64	0.0%	6482878.0 – 7052563.0 (mean 6573602.5688)
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%

---

Numeric Summary

Column	Min	Max	Mean	Median
preference_rating	90.0	90.0	90.0	90.0
is_allowing_for_assistance	0.0	1.0	0.1449	0.0
value	0.0	5.0	2.3281	2.0
id	24372083.0	41457433.0	26827672.1944	24630054.0
datacollectionperiod	158634.0	378217.0	197785.085	160154.0
datacollection	168351.0	389003.0	208014.5078	168914.0
geographic_unit	26247.0	210518.0	29009.8532	26759.0
datasourceorganization	2035.0	2035.0	2035.0	2035.0
datasourcedocument	6581.0	6581.0	6581.0	6581.0
dataseries	6482878.0	7052563.0	6573602.5688	6513734.0

---

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. 8 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.

---

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.

---

Citation

@dataset{hdx_africa_somalia_current_situation_fewsnet_ipc_classification,
  title     = {Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/somalia_current_situation_fewsnet_ipc_classification},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.