electricsheepafrica/africa-wfp-hungermap-data-for-civ

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- indicators
- civ
pretty_name: "Côte d'Ivoire - HungerMap data"
dataset_info:
splits:
- name: train
num_examples: 0
- name: test
num_examples: 0
---

# Côte d'Ivoire - HungerMap data

**Publisher:** WFP - World Food Programme · **Source:** [HDX](https://data.humdata.org/dataset/wfp-hungermap-data-for-civ) · **License:** `cc-by-sa` · **Updated:** 2026-03-04

---

## Abstract

HungerMapLIVE is the World Food Programme (WFP)’s global hunger monitoring system. It combines key metrics from various data sources – such as food security information, weather, population size, conflict, hazards, nutrition information and macro-economic data – to help assess, monitor and predict the magnitude and severity of hunger in near real-time. The resulting analysis is displayed on an interactive map that helps WFP staff, key decision makers and the broader humanitarian community to make more informed and timely decisions relating to food security.

The platform covers 94 countries, including countries where WFP has operations as well as most lower and lower-middle income countries (as classified by the World Bank).

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `date` column(s). Geographic scope: **CIV**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 1 |
| **Columns** | 13 (6 numeric, 6 categorical, 1 datetime) |
| **Train split** | 0 rows |
| **Test split** | 0 rows |
| **Geographic scope** | CIV |
| **Publisher** | WFP - World Food Programme |
| **HDX last updated** | 2026-03-04 |

---

## Variables

**Geographic** — `countrycode` (CIV), `countryname` (Côte d'Ivoire), `adminlevel` (national), `datatype` (SURVEY).

**Temporal** — `date`.

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-07).

**Other** — `fcs_people` (range 5136499.0–5136499.0), `fcs_prevalence` (range 0.1748–0.1748), `rcsi_people` (range 7146630.0–7146630.0), `rcsi_prevalence` (range 0.2432–0.2432), `market_access_people` (range 8577335.0–8577335.0) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-wfp-hungermap-data-for-civ")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `countrycode` | object | 0.0% | CIV |
| `countryname` | object | 0.0% | Côte d'Ivoire |
| `adminlevel` | object | 0.0% | national |
| `date` | datetime64[ns] | 0.0% | |
| `datatype` | object | 0.0% | SURVEY |
| `fcs_people` | int64 | 0.0% | 5136499.0 – 5136499.0 (mean 5136499.0) |
| `fcs_prevalence` | float64 | 0.0% | 0.1748 – 0.1748 (mean 0.1748) |
| `rcsi_people` | int64 | 0.0% | 7146630.0 – 7146630.0 (mean 7146630.0) |
| `rcsi_prevalence` | float64 | 0.0% | 0.2432 – 0.2432 (mean 0.2432) |
| `market_access_people` | int64 | 0.0% | 8577335.0 – 8577335.0 (mean 8577335.0) |
| `market_access_prevalence` | float64 | 0.0% | 0.2919 – 0.2919 (mean 0.2919) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `fcs_people` | 5136499.0 | 5136499.0 | 5136499.0 | 5136499.0 |
| `fcs_prevalence` | 0.1748 | 0.1748 | 0.1748 | 0.1748 |
| `rcsi_people` | 7146630.0 | 7146630.0 | 7146630.0 | 7146630.0 |
| `rcsi_prevalence` | 0.2432 | 0.2432 | 0.2432 | 0.2432 |
| `market_access_people` | 8577335.0 | 8577335.0 | 8577335.0 | 8577335.0 |
| `market_access_prevalence` | 0.2919 | 0.2919 | 0.2919 | 0.2919 |

---

## 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`. 4 column(s) with >80% missing values were removed: `adminone`, `population`, `health_access_people`, `health_access_prevalence`. 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.

---

## 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](https://data.humdata.org/dataset/wfp-hungermap-data-for-civ) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_wfp_hungermap_data_for_civ,
title = {Côte d'Ivoire - HungerMap data},
author = {WFP - World Food Programme},
year = {2026},
url = {https://data.humdata.org/dataset/wfp-hungermap-data-for-civ},
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.*