electricsheepafrica/africa-demographics-tanzania

Tanzania - Risk Assessment Indicators

Publisher: HeiGIT (Heidelberg Institute for Geoinformation Technology) · Source: HDX · License: cc-by-sa · Updated: 2026-04-13

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Abstract

This dataset provides comprehensive Risk Assessment Indicators for Tanzania, aggregated at admin level 2 and can in particular be used to perform a structured risk assessment for flood hazards. It includes demographic, environmental, infrastructure, accessibility, and hazard-related data to support disaster risk and resilience analysis.

All layers are derived from HeiGIT’s GAIA Pipeline, integrating open data sources such as WorldPop, OpenStreetMap, and Google Earth Engine based on HDX COD-AB boundaries.

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Data Overview

• Access to Services (TZA_ADM2_access)
• Facilities (TZA_ADM2_facilities)
• Coping Capacity (TZA_ADM2_coping)
• Demographics (TZA_ADM2_demographics)
• Rural Population (TZA_ADM2_rural_population)
• Vulnerability (TZA_ADM2_vulnerability)
• Flood Exposure (TZA_ADM2_flood_exposure)

 

 

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Indicator Descriptions

Access to Services (TZA_ADM2_access)

Represents the share of the population with access to key facilities within defined distances or travel times.

• ADM2_PCODE – Administrative division code (ADM2)
• access_pop_education_5km / 10km / 20km – Population within 5, 10, and 20 km of educational facilities
• access_pop_hospitals_30min / 1h / 2h – Population within 30 minutes, 1 hour, and 2 hours of a hospital
• access_pop_primary_healthcare_30min / 1h / 2h – Population within 30 minutes, 1 hour, and 2 hours of a primary health care facility

Data Source: openrouteservice (ORS)

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Facilities (TZA_ADM2_facilities)

Counts of essential service facilities within each district.

• ADM2_PCODE – Administrative division code (ADM2)
• education_count – Number of educational facilities
• hospitals_count – Number of hospitals
• primary_healthcare_count – Number of primary health care facilities

Data Source: OpenStreetMap (OSM)

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Coping Capacity (TZA_ADM2_coping)

Combines Access to Services and Facilities data to represent a district’s coping capacity.

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Demographics (TZA_ADM2_demographics)

Shows the population composition by age and gender.

• ADM2_PCODE – Administrative division code (ADM2)
• female_pop – Total female population
• children_u5 – Population under 5 years old
• female_u5 – Female population under 5 years old
• elderly – Population aged 65 and older
• pop_u15 – Population under 15 years old
• female_u15 – Female population under 15 years old

Data Source: Worldpop

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Rural Population (TZA_ADM2_rural_population)

Same demographic breakdown as above, but limited to rural populations. Rural areas are those outside urban extents, typically characterized by lower population density, agricultural or natural land use, and limited infrastructure compared to urban centers.

• ADM2_PCODE – Administrative division code (ADM2)
• female_pop_rural, children_u5_rural, female_u5_rural, elderly_rural, pop_u15_rural, female_u15_rural – Rural demographic counts
• rural_pop_perc – Percentage of total population living in rural areas

Data Source: Global Human Settlement Layer (GHSL)

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Vulnerability (TZA_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

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Flood Exposure (TZA_ADM2_flood_exposure)

Shows population and facility exposure to flooding at 30 cm depth for multiple return periods.

• ADM2_PCODE – Administrative division code (ADM2)
• female_pop_30cm, children_u5_30cm, female_u5_30cm, elderly_30cm, pop_u15_30cm, female_u15_30cm – Exposed population by group
• education_30cm_pct / count, hospitals_30cm_pct / count, primary_healthcare_30cm_pct / count – Facility exposure (percentage and count)

Data Source: The Joint Research Centre (JRC)

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QGIS Plugin Risk Assessment Inputs

• Coping Capacity = Access + Facilities
• Vulnerability = Demographics + Rural Population
• Exposure = Vulnerable Population + Facilities exposed to Floods

This dataset is part of HeiGIT’s Risk Assessment Indicator Collection on HDX. See more at HeiGIT on HDX and learn about HeiGIT’s research at HeiGIT.

We are happy to hear about your use-cases — contact us at [email protected]!

Each row in this dataset represents tabular records. Data was last updated on HDX on 2026-04-13. Geographic scope: TZA.

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

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Dataset Characteristics

Domain	Public health
Unit of observation	Tabular records
Rows (total)	170
Columns	16 (12 numeric, 4 categorical, 0 datetime)
Train split	136 rows
Test split	34 rows
Geographic scope	TZA
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-04-13

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Variables

Geographic — access_pop_primary_healthcare_30min (range 0.0–2847754.0), access_pop_primary_healthcare_1h (range 0.0–2847924.0), access_pop_primary_healthcare_2h (range 0.0–2847958.0), primary_healthcare_count (range 0.0–135.0).

Demographic — access_pop_education_5km (range 30613.0–2819777.0), access_pop_education_10km (range 34697.0–2847766.0), access_pop_education_20km (range 37976.0–2847828.0), access_pop_hospitals_30min (range 0.0–2847810.0), access_pop_hospitals_1h (range 0.0–2847918.0) and 1 others.

Outcome / Measurement — education_count (range 28.0–529.0), hospitals_count (range 0.0–82.0).

Identifier / Metadata — adm2_pcode (TZ0206, TZ0602, TZ1901), adm_pcode (TZ0206, TZ0602, TZ1901), esa_source (HDX), esa_processed (2026-04-27).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-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
adm2_pcode	object	0.0%	TZ0206, TZ0602, TZ1901
access_pop_education_5km	int64	0.0%	30613.0 – 2819777.0 (mean 313798.9176)
access_pop_education_10km	int64	0.0%	34697.0 – 2847766.0 (mean 342312.8647)
access_pop_education_20km	int64	0.0%	37976.0 – 2847828.0 (mean 372402.7941)
access_pop_hospitals_30min	int64	0.0%	0.0 – 2847810.0 (mean 233920.0353)
access_pop_hospitals_1h	int64	0.0%	0.0 – 2847918.0 (mean 325121.2824)
access_pop_hospitals_2h	int64	0.0%	40736.0 – 2847933.0 (mean 398097.9059)
access_pop_primary_healthcare_30min	int64	0.0%	0.0 – 2847754.0 (mean 296753.9765)
access_pop_primary_healthcare_1h	int64	0.0%	0.0 – 2847924.0 (mean 365431.3412)
access_pop_primary_healthcare_2h	int64	0.0%	0.0 – 2847958.0 (mean 413085.7059)
education_count	int64	0.0%	28.0 – 529.0 (mean 142.9882)
hospitals_count	int64	0.0%	0.0 – 82.0 (mean 6.8647)
primary_healthcare_count	int64	0.0%	0.0 – 135.0 (mean 19.3353)
adm_pcode	object	0.0%	TZ0206, TZ0602, TZ1901
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-27

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Numeric Summary

Column	Min	Max	Mean	Median
access_pop_education_5km	30613.0	2819777.0	313798.9176	220308.0
access_pop_education_10km	34697.0	2847766.0	342312.8647	239009.0
access_pop_education_20km	37976.0	2847828.0	372402.7941	278157.5
access_pop_hospitals_30min	0.0	2847810.0	233920.0353	136538.5
access_pop_hospitals_1h	0.0	2847918.0	325121.2824	233300.0
access_pop_hospitals_2h	40736.0	2847933.0	398097.9059	333264.5
access_pop_primary_healthcare_30min	0.0	2847754.0	296753.9765	189819.5
access_pop_primary_healthcare_1h	0.0	2847924.0	365431.3412	285853.5
access_pop_primary_healthcare_2h	0.0	2847958.0	413085.7059	354674.5
education_count	28.0	529.0	142.9882	133.0
hospitals_count	0.0	82.0	6.8647	4.0
primary_healthcare_count	0.0	135.0	19.3353	15.0

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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. 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 HeiGIT (Heidelberg Institute for Geoinformation Technology) and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_demographics_tanzania,
  title     = {Tanzania - Risk Assessment Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/tanzania---risk-assessment-indicators},
  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.