electricsheepafrica/africa-wash-zimbabwe

Water Point Data Exchange (ZWE)

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: ZWE.

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)	17,574
Columns	37 (16 numeric, 19 categorical, 2 datetime)
Train split	14,059 rows
Test split	3,514 rows
Geographic scope	ZWE
Publisher	Water Point Data Exchange
HDX last updated	2025-12-14

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Variables

Geographic — lat_deg (range -22.3473–-16.3175), lon_deg (range 26.1909–32.9486), clean_country_id (ZWE, #country+code+v_iso3), clean_country_name (Zimbabwe, #country+name), wpdx_id (#loc+wpdx+id, 5GGG76HH+62X, 5GGG7H2Q+8CW) and 13 others.

Temporal — report_date, created_timestamp.

Demographic — usage_cap (range 50.0–300.0).

Identifier / Metadata — status_id (Yes, No, Unknown), source (The Zimbabwe Rural Water, Sanitation and Hygiene Management Information System, Living Water International, UNHCR), water_source_clean (Borehole/Tubewell, Protected Well, Sand or Sub-surface Dam), dataset_title, esa_source and 1 others.

Other — water_tech_clean (Hand Pump - Bush Type, Rope and Bucket, Hand Pump), clean_adm1 (Masvingo, Midlands, Mashonaland West), clean_adm2 (Hurungwe, Chiredzi, Mberengwa), clean_adm3 (Ward 8, Ward 3, Ward 13), status_clean and 5 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wash-zimbabwe")
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%	-22.3473 – -16.3175 (mean -19.389)
lon_deg	float64	0.0%	26.1909 – 32.9486 (mean 30.0927)
status_id	object	0.0%	Yes, No, Unknown
report_date	datetime64[ns]	0.0%
source	object	0.0%	The Zimbabwe Rural Water, Sanitation and Hygiene Management Information System, Living Water International, UNHCR
water_source_clean	object	0.0%	Borehole/Tubewell, Protected Well, Sand or Sub-surface Dam
water_tech_clean	object	4.8%	Hand Pump - Bush Type, Rope and Bucket, Hand Pump
clean_country_id	object	0.0%	ZWE, #country+code+v_iso3
clean_country_name	object	0.0%	Zimbabwe, #country+name
clean_adm1	object	0.0%	Masvingo, Midlands, Mashonaland West
clean_adm2	object	0.0%	Hurungwe, Chiredzi, Mberengwa
clean_adm3	object	0.0%	Ward 8, Ward 3, Ward 13
wpdx_id	object	0.0%	#loc+wpdx+id, 5GGG76HH+62X, 5GGG7H2Q+8CW
install_year	float64	12.1%	1900.0 – 2022.0 (mean 1991.2092)
status_clean	object	0.0%
notes	object	0.9%
local_population	float64	8.7%	0.0 – 26312.0 (mean 283.8935)
assigned_population	float64	8.7%	0.0 – 13424.0 (mean 186.3656)
facility_type	object	0.0%
water_source_category	object	0.0%
water_tech_category	object	6.2%
created_timestamp	datetime64[ns]	0.0%
dataset_title	object	0.0%
rehab_priority	float64	75.5%	0.0 – 11201.0 (mean 175.9942)
usage_cap	float64	1.7%	50.0 – 300.0 (mean 273.5218)
criticality	float64	9.5%	0.0605 – 1.0 (mean 0.7769)
pressure	float64	11.1%	0.0033 – 44.7467 (mean 0.8961)
distance_to_primary	float64	0.0%	4.0134 – 162277.1725 (mean 37801.4523)
distance_to_secondary	float64	0.0%	2.7044 – 91400.474 (mean 19221.4799)
distance_to_tertiary	float64	0.0%	0.0356 – 48258.0538 (mean 5464.6845)
distance_to_city	float64	0.0%	7199.1138 – 225950.8065 (mean 96433.5246)
distance_to_town	float64	0.0%	80.5471 – 93689.1062 (mean 29702.2935)
is_urban	object	0.0%
days_since_report	float64	0.0%	1177.0 – 4622.0 (mean 3406.7183)
staleness	float64	0.0%	23.2954 – 69.004 (mean 34.3251)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
lat_deg	-22.3473	-16.3175	-19.389	-19.8049
lon_deg	26.1909	32.9486	30.0927	30.1628
install_year	1900.0	2022.0	1991.2092	1993.0
local_population	0.0	26312.0	283.8935	193.0
assigned_population	0.0	13424.0	186.3656	142.0
rehab_priority	0.0	11201.0	175.9942	140.0
usage_cap	50.0	300.0	273.5218	300.0
criticality	0.0605	1.0	0.7769	0.8505
pressure	0.0033	44.7467	0.8961	0.5367
distance_to_primary	4.0134	162277.1725	37801.4523	32843.0969
distance_to_secondary	2.7044	91400.474	19221.4799	16405.775
distance_to_tertiary	0.0356	48258.0538	5464.6845	3495.2808
distance_to_city	7199.1138	225950.8065	96433.5246	88845.4039
distance_to_town	80.5471	93689.1062	29702.2935	27709.7465
days_since_report	1177.0	4622.0	3406.7183	3464.0

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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. 19 column(s) with >80% missing values were removed: clean_adm4, activity_id, scheme_id, installer, rehab_year, rehabilitator.... 18 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: rehab_priority.
• 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_zimbabwe,
  title     = {Water Point Data Exchange (ZWE)},
  author    = {Water Point Data Exchange},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wpdx_zwe},
  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.