electricsheepasia/asia-aid-flows-wfp-ica-afghanistan-2019

Afghanistan: Integrated Context Analysis (ICA), 2019

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

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: AFG.

Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).

Dataset Characteristics

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 0.0–2008.0), max (range 0.0294–1.0).

Identifier / Metadata — unnamed_2 (Jani Khel, Dawlat Abad, Muqur), unnamed_3 (range 2232.0–3961487.0), unnamed_5 (range 0.0028–2012.0), unnamed_6 (range 0.0066–2014.0), unnamed_7 (range 0.0408–2017.0) and 6 others.

Other — threshold (range 0.3–3405.0), min (range 0.0–0.7827), 1st_t (range -0.2936–0.3891), 2ndt (range 0.6667–207.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-aid-flows-wfp-ica-afghanistan-2019")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snakecase. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 4 column(s) with >80% missing values were removed: `unnamed0, unnamed12`, `unnamed13, unnamed_18`. 11 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 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.
• —The following columns have >20% missing values and should be treated with caution in modelling: 2ndt.
• —Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_aid_flows_wfp_ica_afghanistan_2019,
  title     = {Afghanistan: Integrated Context Analysis (ICA), 2019},
  author    = {WFP - World Food Programme},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wfp_ica_afg_2019},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.