electricsheepafrica/africa-health-centers-data-base

Guinea: Health Centers Data Base

Publisher: WASH Cluster Guinea (inactive) · Source: HDX · License: other-pd-nr · Updated: 2023-03-03

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Abstract

This "Health Center Database" is a compilation about a work of the WASH cluster Guinea in collaboration with all his partners. This data base has been built from the initial data base from the Health Ministry of Guinea. If you get any suggestions or updates to this "Health center database", please send an email to: washclusterguinea@gmail.com The compilation of the update will be send to this HDX regularly.

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2023-03-03. Geographic scope: GIN.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Subnational administrative unit observations
Rows (total)	1,747
Columns	22 (2 numeric, 20 categorical, 0 datetime)
Train split	1,397 rows
Test split	349 rows
Geographic scope	GIN
Publisher	WASH Cluster Guinea (inactive)
HDX last updated	2023-03-03

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Variables

Geographic — code_provisoire (1, GIN00700109 CSMitt, GIN00700110PSKene), x (range -15.0219–12.5832), y (range 7.2563–14.4817), sourcecoord (geonode, Ministere Plan, CRS), type_cxxxgroupes (PS, CS, HP) and 3 others.

Identifier / Metadata — name_structure_sanitaire (Balandougou, Hamdallaye, Hafia), georef (OK, Coord absent), pref, spref, prefecture_prioritaire and 2 others.

Other — nom_localite (Labé, Hafia, Kankan), nom_hop (Sanoyah, Hérico, Balandougou), liste_officielle (ok, Non), reg (Nzerekore, Kankan, Kindia), acteur and 2 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-health-centers-data-base")
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
code_provisoire	object	0.0%	1, GIN00700109 CSMitt, GIN00700110PSKene
x	float64	33.0%	-15.0219 – 12.5832 (mean -10.7227)
y	float64	33.0%	7.2563 – 14.4817 (mean 10.0786)
sourcecoord	object	29.4%	geonode, Ministere Plan, CRS
nom_localite	object	38.6%	Labé, Hafia, Kankan
name_structure_sanitaire	object	0.0%	Balandougou, Hamdallaye, Hafia
nom_hop	object	33.7%	Sanoyah, Hérico, Balandougou
georef	object	0.1%	OK, Coord absent
type_cxxxgroupes	object	0.0%	PS, CS, HP
typo_ministere	object	1.4%	PS, CS, CS
liste_officielle	object	0.1%	ok, Non
reg	object	0.0%	Nzerekore, Kankan, Kindia
pref	object	0.0%
admin2_code	object	1.7%
spref	object	0.0%
admin3_code	object	0.0%
prefecture_prioritaire	object	45.6%
acteur	object	71.7%
kit	object	76.8%
remarque	object	80.0%
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
x	-15.0219	12.5832	-10.7227	-10.7212
y	7.2563	14.4817	10.0786	10.2138

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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. 7 column(s) with >80% missing values were removed: activite, tri_isolement, infra, nan, unnamed_24, unnamed_25.... 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 WASH Cluster Guinea (inactive) 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: x, y, sourcecoord, nom_localite, nom_hop, prefecture_prioritaire, acteur, kit....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_health_centers_data_base,
  title     = {Guinea: Health Centers Data Base},
  author    = {WASH Cluster Guinea (inactive)},
  year      = {2023},
  url       = {https://data.humdata.org/dataset/health-centers-data-base},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.