electricsheepafrica/africa-burundi-kirundo-province-operational-presence

Burundi - Kirundo Province: Operational Presence

Publisher: Fields Data · Source: HDX · License: cc-by · Updated: 2025-05-28

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Abstract

This dataset contains Who, What, Where and When (4W) data for the province of Kirundo in Burundi. The operational presence of the various organisations (who) by sector (what), location (where) at the province level and when the information was collected (when). This 4W for Burundi combines OCHA's 4W dataset together with newly discovered information by fieldata.org experts from the field.

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the timestamp column(s). Geographic scope: BDI.

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

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Dataset Characteristics

Domain	Humanitarian and development data
Unit of observation	First-level administrative unit observations
Rows (total)	69
Columns	12 (0 numeric, 11 categorical, 1 datetime)
Train split	55 rows
Test split	13 rows
Geographic scope	BDI
Publisher	Fields Data
HDX last updated	2025-05-28

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Variables

Geographic — type_organization (National NGO, International NGO, Multilateral), region (East Africa, #region+name), country (Burundi, #adm1+name), province (Kirundo, #adm2+name), type_of_activity (Humanitarian, Development, Transitional).

Temporal — timestamp.

Identifier / Metadata — source (Fields Data Team, OCHA, #meta+source), organization_name (World Food Program (WFP), Association National de Soutien aux Séropositifs et malades du SIDA (ANSS), CARE International (CARE)), esa_source (HDX), esa_processed.

Other — web_organization (https://www.wfp.org/countries/burundi, https://www.care-international.org/where-we-work/burundi, http://oideb.org/), sector (Protection, Food Security, Education).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-burundi-kirundo-province-operational-presence")
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
timestamp	datetime64[ns]	1.4%
source	object	0.0%	Fields Data Team, OCHA, #meta+source
organization_name	object	0.0%	World Food Program (WFP), Association National de Soutien aux Séropositifs et malades du SIDA (ANSS), CARE International (CARE)
type_organization	object	0.0%	National NGO, International NGO, Multilateral
web_organization	object	56.5%	https://www.wfp.org/countries/burundi, https://www.care-international.org/where-we-work/burundi, http://oideb.org/
region	object	0.0%	East Africa, #region+name
country	object	0.0%	Burundi, #adm1+name
province	object	0.0%	Kirundo, #adm2+name
sector	object	0.0%	Protection, Food Security, Education
type_of_activity	object	1.4%	Humanitarian, Development, Transitional
esa_source	object	0.0%	HDX
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
No numeric columns.

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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. 1 column(s) with >80% missing values were removed: unnamed_10. 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.

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Limitations

• Data originates from Fields Data 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: web_organization.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_burundi_kirundo_province_operational_presence,
  title     = {Burundi - Kirundo Province: Operational Presence},
  author    = {Fields Data},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/burundi-kirundo-province-operational-presence},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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