Datasets:

electricsheepafrica
/

africa-eastern-africa-region-people-in-need-per-sector-2011-2014

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 · 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.

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

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 for the publisher's own methodology notes and caveats.

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.