electricsheepafrica/africa-wash-tanzania

Water Point Data Exchange (TZA)

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: TZA.

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)	17,519
Columns	43 (17 numeric, 24 categorical, 2 datetime)
Train split	14,015 rows
Test split	3,503 rows
Geographic scope	TZA
Publisher	Water Point Data Exchange
HDX last updated	2025-12-14

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Variables

Geographic — lat_deg (range -11.5668–-1.0003), lon_deg (range 29.6023–39.8813), clean_country_id (TZA, #country+code+v_iso3), clean_country_name (Tanzania, #country+name), wpdx_id and 15 others.

Temporal — report_date, created_timestamp.

Demographic — management_clean, usage_cap (range 50.0–300.0).

Identifier / Metadata — status_id (Yes, No, #indicator+water_avail+bool), source (WaterAid, SNV, Concern), water_source_clean (Borehole/Tubewell, Protected Well, Protected Spring), scheme_id (Shallow well, DHV, Mitema), dataset_title and 2 others.

Other — water_tech_clean (Public Tapstand, Hand Pump - Nira, Hand Pump - Mono), clean_adm1 (Dodoma, Kagera, Singida), clean_adm2 (Ngara, Namtumbo, Kondoa), clean_adm3 (Makoga, Kassambya, Itololo), installer and 7 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-wash-tanzania")
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%	-11.5668 – -1.0003 (mean -5.2533)
lon_deg	float64	0.0%	29.6023 – 39.8813 (mean 34.3954)
status_id	object	0.0%	Yes, No, #indicator+water_avail+bool
report_date	datetime64[ns]	0.0%
source	object	0.0%	WaterAid, SNV, Concern
water_source_clean	object	0.0%	Borehole/Tubewell, Protected Well, Protected Spring
water_tech_clean	object	5.7%	Public Tapstand, Hand Pump - Nira, Hand Pump - Mono
clean_country_id	object	0.0%	TZA, #country+code+v_iso3
clean_country_name	object	0.0%	Tanzania, #country+name
clean_adm1	object	0.0%	Dodoma, Kagera, Singida
clean_adm2	object	0.0%	Ngara, Namtumbo, Kondoa
clean_adm3	object	0.0%	Makoga, Kassambya, Itololo
scheme_id	object	62.5%	Shallow well, DHV, Mitema
wpdx_id	object	0.0%
install_year	float64	5.5%	1921.0 – 2020.0 (mean 1993.0013)
installer	object	1.8%
management_clean	object	2.3%
pay_clean	object	0.6%
status_clean	object	0.0%
subjective_quality	object	1.1%
notes	object	0.8%
local_population	float64	2.5%	0.0 – 41063.0 (mean 1643.8205)
assigned_population	float64	2.5%	0.0 – 41063.0 (mean 763.4608)
facility_type	object	0.0%
water_source_category	object	0.0%
water_tech_category	object	5.9%
created_timestamp	datetime64[ns]	0.0%
dataset_title	object	0.0%
rehab_priority	float64	58.4%	0.0 – 29744.0 (mean 1041.9479)
would_gain_access	float64	58.2%	0.0 – 10202.2266 (mean 639.8173)
usage_cap	float64	0.0%	50.0 – 300.0 (mean 281.533)
criticality	float64	2.9%	0.0234 – 1.0 (mean 0.5652)
pressure	float64	2.9%	0.0033 – 272.0 (mean 2.9508)
distance_to_primary	float64	0.0%	0.6585 – 249271.0721 (mean 45903.3896)
distance_to_secondary	float64	0.0%	0.0325 – 130047.9869 (mean 13117.5085)
distance_to_tertiary	float64	0.0%	0.1239 – 86854.5553 (mean 4041.127)
distance_to_city	float64	0.0%	27.6481 – 271798.8804 (mean 83929.0914)
distance_to_town	float64	0.0%	22.0077 – 131920.5039 (mean 25929.6087)
is_urban	object	0.0%
days_since_report	float64	0.0%	576.0 – 17208.0 (mean 6784.0368)
staleness	float64	0.0%	0.4409 – 83.3966 (mean 12.0647)
esa_source	object	0.0%
esa_processed	object	0.0%

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Numeric Summary

Column	Min	Max	Mean	Median
lat_deg	-11.5668	-1.0003	-5.2533	-4.9022
lon_deg	29.6023	39.8813	34.3954	34.6067
install_year	1921.0	2020.0	1993.0013	1996.0
local_population	0.0	41063.0	1643.8205	1028.0
assigned_population	0.0	41063.0	763.4608	449.0
rehab_priority	0.0	29744.0	1041.9479	637.0
would_gain_access	0.0	10202.2266	639.8173	280.9339
usage_cap	50.0	300.0	281.533	300.0
criticality	0.0234	1.0	0.5652	0.5235
pressure	0.0033	272.0	2.9508	1.5967
distance_to_primary	0.6585	249271.0721	45903.3896	30588.6366
distance_to_secondary	0.0325	130047.9869	13117.5085	7566.6545
distance_to_tertiary	0.1239	86854.5553	4041.127	1604.0788
distance_to_city	27.6481	271798.8804	83929.0914	74413.4945
distance_to_town	22.0077	131920.5039	25929.6087	20970.4418

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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. 13 column(s) with >80% missing values were removed: clean_adm4, activity_id, rehab_year, rehabilitator, fecal_coliform_presence, fecal_coliform_value.... 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: scheme_id, rehab_priority, would_gain_access.
• 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_tanzania,
  title     = {Water Point Data Exchange (TZA)},
  author    = {Water Point Data Exchange},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/wpdx_tza},
  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.