electricsheepafrica/africa-aid-flows-niger

Niger: Integrated Context Analysis (ICA), 2018

Publisher: WFP - World Food Programme · Source: HDX · License: hdx-odc-odbl · Updated: 2025-08-26

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Abstract

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk.

The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-08-26. Geographic scope: NER.

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

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Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Geolocated point observations
Rows (total)	68
Columns	19 (16 numeric, 3 categorical, 0 datetime)
Train split	54 rows
Test split	13 rows
Geographic scope	NER
Publisher	WFP - World Food Programme
HDX last updated	2025-08-26

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Variables

Geographic — delete_these_extra_columns_as_necessary_add_remove_rows_according_to_the_number_of_administrative_units_considered_both_here_and_in_the_join_and_population_figures_sheets (range 0.0104–1.0), max (range 0.0583–1.0).

Identifier / Metadata — unnamed_2 (Name, InGall, Keita), unnamed_3 (range 2016.0–1128162.0), unnamed_4 (range 0.0062–0.594), unnamed_5 (range 0.0045–0.4675), unnamed_6 (range 0.004–0.447) and 8 others.

Other — threshold (range 0.2–971.0), min (range 0.0–0.6147), 1st_t (range -0.2187–0.333), 2ndt (range 0.6667–64.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-aid-flows-niger")
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
threshold	float64	4.4%	0.2 – 971.0 (mean 905.6646)
unnamed_2	object	4.4%	Name, InGall, Keita
unnamed_3	float64	4.4%	2016.0 – 1128162.0 (mean 294996.5385)
unnamed_4	float64	5.9%	0.0062 – 0.594 (mean 0.2008)
unnamed_5	float64	7.4%	0.0045 – 0.4675 (mean 0.1553)
unnamed_6	float64	5.9%	0.004 – 0.447 (mean 0.1258)
delete_these_extra_columns_as_necessary_add_remove_rows_according_to_the_number_of_administrative_units_considered_both_here_and_in_the_join_and_population_figures_sheets	float64	7.4%	0.0104 – 1.0 (mean 0.4834)
unnamed_8	float64	7.4%	0.0336 – 0.9907 (mean 0.5046)
unnamed_9	float64	5.9%	4.0 – 5.0 (mean 4.9531)
unnamed_10	float64	5.9%	0.0 – 5.0 (mean 2.5)
unnamed_11	float64	5.9%	0.0 – 1.0 (mean 0.5016)
unnamed_12	float64	5.9%	1.0 – 3.0 (mean 2.0312)
min	float64	2.9%	0.0 – 0.6147 (mean 0.2878)
1st_t	float64	4.4%	-0.2187 – 0.333 (mean 0.0042)
2ndt	float64	4.4%	0.6667 – 64.0 (mean 32.0103)
max	float64	4.4%	0.0583 – 1.0 (mean 0.2344)
unnamed_19	float64	5.9%	1.0 – 64.0 (mean 32.5)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-22

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Numeric Summary

Column	Min	Max	Mean	Median
threshold	0.2	971.0	905.6646	921.0
unnamed_3	2016.0	1128162.0	294996.5385	276557.0
unnamed_4	0.0062	0.594	0.2008	0.1502
unnamed_5	0.0045	0.4675	0.1553	0.1431
unnamed_6	0.004	0.447	0.1258	0.103
delete_these_extra_columns_as_necessary_add_remove_rows_according_to_the_number_of_administrative_units_considered_both_here_and_in_the_join_and_population_figures_sheets	0.0104	1.0	0.4834	0.5385
unnamed_8	0.0336	0.9907	0.5046	0.478
unnamed_9	4.0	5.0	4.9531	5.0
unnamed_10	0.0	5.0	2.5	2.0
unnamed_11	0.0	1.0	0.5016	0.4
unnamed_12	1.0	3.0	2.0312	2.0
min	0.0	0.6147	0.2878	0.2846
1st_t	-0.2187	0.333	0.0042	-0.0079
2ndt	0.6667	64.0	32.0103	32.0
max	0.0583	1.0	0.2344	0.2251

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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: unnamed_0, unnamed_13, unnamed_14. 16 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 WFP - World Food Programme 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_aid_flows_niger,
  title     = {Niger: Integrated Context Analysis (ICA), 2018},
  author    = {WFP - World Food Programme},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wfp_ica_ner_2018},
  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.