electricsheepafrica/africa-srf-2014

Somalia beneficiary figures (targeted and reached) by month, Jan - Dec 2014

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

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Abstract

2014 response data for Somalia. (Single Reporting Format) The data shows targets and response by cluster per month. The data is sourced from the humanitarian clusters in Somalia.

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

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

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Dataset Characteristics

Domain	Public health
Unit of observation	First-level administrative unit observations
Rows (total)	2,350
Columns	14 (3 numeric, 10 categorical, 1 datetime)
Train split	1,880 rows
Test split	470 rows
Geographic scope	SOM
Publisher	OCHA Somalia
HDX last updated	2023-11-15

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Variables

Geographic — region (Awdal, Lower Juba, Nugaal), category (FSC, Wash, Education), end_year_target (550, 6,000, 0), cumulative_reached_to_date (0, 600, 3,265), cumulative_reached_num_to_date (range 0.0–1427330.0).

Temporal — date, month_num (range 1.0–12.0), month (December, April, May), current_target_to_date (0, 3,000, 1,500), percent_current_target_reached_to_date (0%, 6%, 4%) and 1 others.

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-08).

Other — metric (Number of people receiving primary and/or basic secondary health care services, Number of people targeted through interventions geared towards improving access to food and safety nets, Number of people targeted through livelihood investment and assets activities).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-srf-2014")
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
region	object	0.0%	Awdal, Lower Juba, Nugaal
date	datetime64[ns]	0.0%
month_num	int64	0.0%	1.0 – 12.0 (mean 6.9677)
month	object	0.0%	December, April, May
category	object	0.0%	FSC, Wash, Education
metric	object	0.0%	Number of people receiving primary and/or basic secondary health care services, Number of people targeted through interventions geared towards improving access to food and safety nets, Number of people targeted through livelihood investment and assets activities
end_year_target	object	0.0%	550, 6,000, 0
current_target_to_date	object	0.0%	0, 3,000, 1,500
cumulative_reached_to_date	object	25.5%	0, 600, 3,265
cumulative_reached_num_to_date	float64	0.0%	0.0 – 1427330.0 (mean 45403.4732)
percent_current_target_reached_to_date	object	0.0%	0%, 6%, 4%
ratio_of_current_target_reached_to_date	float64	0.0%	0.0 – 13.89 (mean 0.6117)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-08

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Numeric Summary

Column	Min	Max	Mean	Median
month_num	1.0	12.0	6.9677	7.0
cumulative_reached_num_to_date	0.0	1427330.0	45403.4732	1948.0
ratio_of_current_target_reached_to_date	0.0	13.89	0.6117	0.25

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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) 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: cumulative_reached_to_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_srf_2014,
  title     = {Somalia beneficiary figures (targeted and reached) by month, Jan - Dec 2014},
  author    = {OCHA Somalia},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/srf-2014},
  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.