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africa-demographics-angola

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metadata 
annotations_creators:
  - no-annotation
language_creators:
  - found
language:
  - en
license: cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - tabular-classification
  - other
task_ids: []
tags:
  - africa
  - humanitarian
  - hdx
  - electric-sheep-africa
  - affected-population
  - demographics
  - flooding
  - hazards-and-risk
  - health-facilities
  - indicators
  - ago
pretty_name: Angola - Risk Assessment Indicators
dataset_info:
  splits:
    - name: train
      num_examples: 128
    - name: test
      num_examples: 32

Angola - Risk Assessment Indicators

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

Abstract

This dataset provides comprehensive Risk Assessment Indicators for Angola, 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.

Data Overview

  • Access to Services (AGO_ADM2_access)
  • Facilities (AGO_ADM2_facilities)
  • Coping Capacity (AGO_ADM2_coping)
  • Demographics (AGO_ADM2_demographics)
  • Rural Population (AGO_ADM2_rural_population)
  • Vulnerability (AGO_ADM2_vulnerability)
  • Flood Exposure (AGO_ADM2_flood_exposure)

 

 

Indicator Descriptions

Access to Services (AGO_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)

Facilities (AGO_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)

Coping Capacity (AGO_ADM2_coping)

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

Demographics (AGO_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

Rural Population (AGO_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)

Vulnerability (AGO_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

Flood Exposure (AGO_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)

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 communications@heigit.org!

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

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

Dataset Characteristics

Domain Public health
Unit of observation Tabular records
Rows (total) 161
Columns 17 (13 numeric, 4 categorical, 0 datetime)
Train split 128 rows
Test split 32 rows
Geographic scope AGO
Publisher HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated 2026-04-13

Variables

Geographic — elderly (range 95.0–70242.0), elderly_rural (range 0.0–17136.0).

Demographic — female_pop (range 1587.0–2570143.0), female_u5 (range 291.0–351082.0), pop_u15 (range 1343.0–2036965.0), female_u15 (range 710.0–1037880.0), female_pop_rural (range 0.0–204318.0) and 4 others.

Identifier / Metadata — adm2_pcode (AO15128, AO05032, AO12108), adm_pcode (AO15128, AO05032, AO12108), esa_source (HDX), esa_processed (2026-04-27).

Other — children_u5 (range 574.0–697927.0), children_u5_rural (range 0.0–77355.0).

Quick Start 

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-angola")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column Type Null % Range / Sample Values
adm2_pcode object 0.0% AO15128, AO05032, AO12108
female_pop int64 0.0% 1587.0 – 2570143.0 (mean 119379.2857)
children_u5 int64 0.0% 574.0 – 697927.0 (mean 38434.3851)
female_u5 int64 0.0% 291.0 – 351082.0 (mean 19403.6273)
elderly int64 0.0% 95.0 – 70242.0 (mean 5292.1429)
pop_u15 int64 0.0% 1343.0 – 2036965.0 (mean 102229.7081)
female_u15 int64 0.0% 710.0 – 1037880.0 (mean 51790.5901)
female_pop_rural int64 0.0% 0.0 – 204318.0 (mean 33518.0807)
children_u5_rural int64 0.0% 0.0 – 77355.0 (mean 12301.7516)
female_u5_rural int64 0.0% 0.0 – 37278.0 (mean 6219.3665)
elderly_rural int64 0.0% 0.0 – 17136.0 (mean 2000.3043)
pop_u15_rural int64 0.0% 0.0 – 183004.0 (mean 30663.1677)
female_u15_rural int64 0.0% 0.0 – 91088.0 (mean 15444.3043)
rural_pop_perc float64 0.0% 0.0 – 100.0 (mean 66.1942)
adm_pcode object 0.0% AO15128, AO05032, AO12108
esa_source object 0.0% HDX
esa_processed object 0.0% 2026-04-27

Numeric Summary

Column Min Max Mean Median
female_pop 1587.0 2570143.0 119379.2857 42037.0
children_u5 574.0 697927.0 38434.3851 14529.0
female_u5 291.0 351082.0 19403.6273 7502.0
elderly 95.0 70242.0 5292.1429 2455.0
pop_u15 1343.0 2036965.0 102229.7081 37280.0
female_u15 710.0 1037880.0 51790.5901 18775.0
female_pop_rural 0.0 204318.0 33518.0807 22439.0
children_u5_rural 0.0 77355.0 12301.7516 8226.0
female_u5_rural 0.0 37278.0 6219.3665 4145.0
elderly_rural 0.0 17136.0 2000.3043 1315.0
pop_u15_rural 0.0 183004.0 30663.1677 20802.0
female_u15_rural 0.0 91088.0 15444.3043 10474.0
rural_pop_perc 0.0 100.0 66.1942 74.84

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.

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.

Citation 

@dataset{hdx_africa_demographics_angola,
  title     = {Angola - Risk Assessment Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/angola---risk-assessment-indicators},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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