electricsheepafrica/africa-wash-ghana

Water Point Data Exchange (GHA)

Publisher: Water Point Data Exchange · Source: HDX · License: Creative Commons Attribution Share-Alike · Updated: 2025-12-14

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Abstract

WPdx is a platform to compile crowdsourced data focused on rural water points (wells, springs, tapstands) with contributions from governments, NGOs, and researchers. Shared data is cleaned and harmonized using the WPdx Data Standard to create a robust analysis-ready dataset. There are two primary datasets available from WPdx. The first is WPdx-Basic, which includes all records shared with the platform. The second, WPdx-Plus is a subset of the Basic dataset which focuses on countries where district and/or national data is available and undergoes additional cleaning. WPdx-Plus is used as an input to the WPdx Decision Support tools and is the basis for the datasets posted here. The decision support tools provide insights on rural basic water service access and recommendations regarding prioritized water point repair, highlight areas with apparent service gaps, and identify water points which are at high risk of failure. For each country where WPdx data is available, there are two datasets available for download are described below.

• The wpdx_adm_region_analysis file provides an overview of the population served, unserved and uncharted for each available administrative level. The file also includes a data quality analysis based on water point record age.
• The wpdx_water_points file provides water point level details for each point shared with WPdx including all the WPdx data standard parameters plus results from the WPdx Rehab Priority, Service Gap/New Construction and Status Prediction analyses.
• A data dictionary describing each parameter included in each file is available in the Data and Resources section.
  To share data with the WPdx platform, please visit (www.waterpointdata.org/share-data) and/or reach out to info@waterpointdata.org

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the report_date, created_timestamp column(s). Geographic scope: GHA.

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

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Dataset Characteristics

Domain	Demographics and population
Unit of observation	Country-level aggregates
Rows (total)	26,368
Columns	38 (17 numeric, 19 categorical, 2 datetime)
Train split	21,094 rows
Test split	5,273 rows
Geographic scope	GHA
Publisher	Water Point Data Exchange
HDX last updated	2025-12-14

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Variables

Geographic — lat_deg (range 4.7476–11.1642), lon_deg (range -3.2351–0.5583), clean_country_id (GHA, #country+code+v_iso3), clean_country_name (Ghana, #country+name), activity_id (range 1871.0–954358668.0) and 15 others.

Temporal — report_date, created_timestamp.

Demographic — management_clean (Community Management, Private Operator/Delegated Management, No Management), usage_cap (range 50.0–300.0).

Identifier / Metadata — status_id (Yes, No, Unknown), source (CWSA, Water4, World Vision), water_source_clean (Borehole/Tubewell, Piped Water, Protected Well), dataset_title, esa_source and 1 others.

Other — water_tech_clean (Hand Pump - Afridev, Hand Pump - India Mark, Public Tapstand), clean_adm1 (Upper West, Upper East, Northern), clean_adm2 (Wassa East, Nadowli-Kaleo, Sawla-Tuna-Kalba), status_clean, pressure (range 0.0033–110.1467) and 3 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wash-ghana")
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
lat_deg	float64	0.0%	4.7476 – 11.1642 (mean 8.5309)
lon_deg	float64	0.0%	-3.2351 – 0.5583 (mean -1.5615)
status_id	object	0.0%	Yes, No, Unknown
report_date	datetime64[ns]	0.0%
source	object	0.0%	CWSA, Water4, World Vision
water_source_clean	object	1.3%	Borehole/Tubewell, Piped Water, Protected Well
water_tech_clean	object	3.4%	Hand Pump - Afridev, Hand Pump - India Mark, Public Tapstand
clean_country_id	object	0.0%	GHA, #country+code+v_iso3
clean_country_name	object	0.0%	Ghana, #country+name
clean_adm1	object	0.0%	Upper West, Upper East, Northern
clean_adm2	object	0.0%	Wassa East, Nadowli-Kaleo, Sawla-Tuna-Kalba
activity_id	float64	78.8%	1871.0 – 954358668.0 (mean 552709764.7482)
wpdx_id	object	0.0%	#loc+wpdx+id, 6CQWM5W4+QXW, 6CRVCP7X+7X3
install_year	float64	2.0%	1947.0 – 2041.0 (mean 2008.1461)
management_clean	object	6.3%	Community Management, Private Operator/Delegated Management, No Management
pay_clean	object	25.5%
status_clean	object	0.0%
local_population	float64	20.7%	0.0 – 35879.0 (mean 1633.7971)
assigned_population	float64	20.7%	0.0 – 33044.0 (mean 567.6386)
facility_type	object	0.0%
water_source_category	object	1.3%
water_tech_category	object	3.9%
created_timestamp	datetime64[ns]	0.0%
dataset_title	object	0.0%
rehab_priority	float64	79.4%	0.0 – 24125.0 (mean 753.0088)
usage_cap	float64	0.3%	50.0 – 300.0 (mean 289.6828)
criticality	float64	22.5%	0.0352 – 1.0 (mean 0.4563)
pressure	float64	22.5%	0.0033 – 110.1467 (mean 1.9428)
distance_to_primary	float64	0.0%	0.0065 – 93327.8974 (mean 22706.5087)
distance_to_secondary	float64	0.0%	0.2347 – 98940.7239 (mean 14571.231)
distance_to_tertiary	float64	0.0%	0.0752 – 50760.6251 (mean 5934.551)
distance_to_city	float64	0.0%	373.147 – 163868.5402 (mean 64400.2893)
distance_to_town	float64	0.0%	0.8367 – 52281.2105 (mean 10872.0169)
is_urban	object	0.0%
days_since_report	float64	0.0%	1.0 – 9472.0 (mean 3340.7131)
staleness	float64	0.0%	5.0504 – 99.9685 (mean 40.2689)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
lat_deg	4.7476	11.1642	8.5309	9.4705
lon_deg	-3.2351	0.5583	-1.5615	-1.6371
activity_id	1871.0	954358668.0	552709764.7482	591690251.0
install_year	1947.0	2041.0	2008.1461	2010.0
local_population	0.0	35879.0	1633.7971	687.0
assigned_population	0.0	33044.0	567.6386	242.0
rehab_priority	0.0	24125.0	753.0088	316.0
usage_cap	50.0	300.0	289.6828	300.0
criticality	0.0352	1.0	0.4563	0.3597
pressure	0.0033	110.1467	1.9428	0.8367
distance_to_primary	0.0065	93327.8974	22706.5087	20658.1785
distance_to_secondary	0.2347	98940.7239	14571.231	8812.484
distance_to_tertiary	0.0752	50760.6251	5934.551	3737.8604
distance_to_city	373.147	163868.5402	64400.2893	59483.4091
distance_to_town	0.8367	52281.2105	10872.0169	9276.7801

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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. 18 column(s) with >80% missing values were removed: clean_adm3, clean_adm4, scheme_id, installer, rehab_year, rehabilitator.... 19 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 Water Point Data Exchange 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: activity_id, pay_clean, local_population, assigned_population, rehab_priority, criticality, pressure.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_wash_ghana,
  title     = {Water Point Data Exchange (GHA)},
  author    = {Water Point Data Exchange},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wpdx_gha},
  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.