---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- eastern-africa
- humanitarian-needs-overview-hno
- dji
- eth
- ken
- ssd
- sdn
pretty_name: "Eastern Africa Region People in Need Per Sector 2011-2015"
dataset_info:
  splits:
    - name: train
      num_examples: 20
    - name: test
      num_examples: 5
---

# Eastern Africa Region People in Need Per Sector 2011-2015

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014) · **License:** `cc-by-igo` · **Updated:** 2023-09-28

---

## Abstract

Data on people in need per sector in Kenya, Somalia, Sudan, South Sudan and Ethiopia from 2011 to 2015

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2023-09-28. Geographic scope: **DJI, ETH, KEN, SSD, SDN**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 25 |
| **Columns** | 24 (1 numeric, 23 categorical, 0 datetime) |
| **Train split** | 20 rows |
| **Test split** | 5 rows |
| **Geographic scope** | DJI, ETH, KEN, SSD, SDN |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2023-09-28 |

---

## Variables

**Geographic** — `year` (range 2011.0–2014.0), `country` (SOM, SUD, SSD).

**Identifier / Metadata** — `source`, `esa_source`, `esa_processed`.

**Other** — `pin` (2,100,000, 1,700,000, 3,800,000), `fs_pin` (3,750,000, 3,200,000, 257,000), `fs_tar` (2,200,000, 3,750,000, 1,981,000), `nut_pin` (3,900,000, 172,500, 2,999,937), `nut_tar` (107,000, 475,000, 591,000) and 14 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-eastern-africa-region-people-in-need-per-sector-2011-2014")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `year` | float64 | 4.0% | 2011.0 – 2014.0 (mean 2012.5) |
| `country` | object | 4.0% | SOM, SUD, SSD |
| `pin` | object | 68.0% | 2,100,000, 1,700,000, 3,800,000 |
| `fs_pin` | object | 20.0% | 3,750,000, 3,200,000, 257,000 |
| `fs_tar` | object | 24.0% | 2,200,000, 3,750,000, 1,981,000 |
| `nut_pin` | object | 8.0% | 3,900,000, 172,500, 2,999,937 |
| `nut_tar` | object | 28.0% | 107,000, 475,000, 591,000 |
| `health_pin` | object | 20.0% | 222,500, 7,500,000, 7,770,000 |
| `healthtar` | object | 24.0% | 164,800, 3,700,000, 3,549,955 |
| `wash_pin` | object | 20.0% | 3,751,000, 2,000,000, 300,000 |
| `wash_tar` | object | 28.0% | 2,600,000, 2,549,000, 2,500,000 |
| `edu_pin` | object | 36.0% |  |
| `edu_tar` | object | 40.0% |  |
| `shelter_nfi_pin` | object | 52.0% |  |
| `shelter_nfi_tar` | object | 52.0% |  |
| `protection_pin` | object | 60.0% |  |
| `protection_tar` | object | 56.0% |  |
| `multi_sector_pin` | object | 56.0% |  |
| `multi_sector_tar` | object | 56.0% |  |
| `mine_action_pin` | object | 72.0% |  |
| `mine_action_tar` | object | 72.0% |  |
| `source` | object | 16.0% |  |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 2011.0 | 2014.0 | 2012.5 | 2012.5 |

---

## 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`. 20 column(s) with >80% missing values were removed: `tar`, `rch`, `fs_rch`, `nut_rch`, `healthrch`, `wash_rch`.... 2 exact duplicate rows were removed. 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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: `pin`, `fs_tar`, `nut_tar`, `healthtar`, `wash_tar`, `edu_pin`, `edu_tar`, `shelter_nfi_pin`....
- This dataset spans 5 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_eastern_africa_region_people_in_need_per_sector_2011_2014,
  title     = {Eastern Africa Region People in Need Per Sector 2011-2015},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014},
  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.*