electricsheepafrica/africa-somalia-3w-q1-2015

Somalia - Who is doing what and where (3W) - 2015

Publisher: OCHA Somalia · Source: HDX · License: cc-by-igo · Updated: 2023-11-15

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Abstract

This is 3W data for Somalia from clusters (WASH, Health, Nutrition, Shelter, Food Security, Education and Protection)

Each row in this dataset represents subnational administrative unit observations. Temporal coverage is indicated by the start_date, end_date column(s). Geographic scope: SOM.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Subnational administrative unit observations
Rows (total)	3,922
Columns	14 (1 numeric, 11 categorical, 2 datetime)
Train split	3,137 rows
Test split	784 rows
Geographic scope	SOM
Publisher	OCHA Somalia
HDX last updated	2023-11-15

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Variables

Geographic — activity_description (Response activities to GBV (case management, PEP kits, medical assistance, legal assistance for the survivors, safe house) , Teacher Incentives, Learning Materials), region (Banadir, Gedo, Bay), district (Baidoa, Jowhar, Hargeysa).

Temporal — start_date, end_date.

Identifier / Metadata — esa_source (HDX), esa_processed.

Other — sector (Nutrition, Protection , WASH), organisation (WFP, UNICEF, INGO), implementing_partner (Undisclosed, WVI, MOH SL), sub_sector (TSFP, OTP, Gender-Based Violence), status (Open, Ongoing, Completed) and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-somalia-3w-q1-2015")
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
sector	object	0.0%	Nutrition, Protection , WASH
organisation	object	32.7%	WFP, UNICEF, INGO
implementing_partner	object	0.0%	Undisclosed, WVI, MOH SL
sub_sector	object	9.0%	TSFP, OTP, Gender-Based Violence
activity_description	object	9.0%	Response activities to GBV (case management, PEP kits, medical assistance, legal assistance for the survivors, safe house) , Teacher Incentives, Learning Materials
region	object	0.6%	Banadir, Gedo, Bay
district	object	2.1%	Baidoa, Jowhar, Hargeysa
status	object	23.3%	Open, Ongoing, Completed
no_of_beneficiaries	float64	68.2%	0.0 – 500000.0 (mean 5133.8796)
unit	object	64.4%	IND, Ind, PLANS
start_date	datetime64[ns]	34.4%
end_date	datetime64[ns]	51.5%
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
no_of_beneficiaries	0.0	500000.0	5133.8796	180.0

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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. 1,737 exact duplicate rows were removed. 3 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 Somalia 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: organisation, status, no_of_beneficiaries, unit, start_date, end_date.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_somalia_3w_q1_2015,
  title     = {Somalia - Who is doing what and where (3W) - 2015},
  author    = {OCHA Somalia},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/somalia-3w-q1-2015},
  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.