electricsheepafrica/africa-demographics-congo-rep

Congo Brazzaville - 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 Congo Brazzaville, 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 (COG_ADM2_access)
• Facilities (COG_ADM2_facilities)
• Coping Capacity (COG_ADM2_coping)
• Demographics (COG_ADM2_demographics)
• Rural Population (COG_ADM2_rural_population)
• Vulnerability (COG_ADM2_vulnerability)
• Flood Exposure (COG_ADM2_flood_exposure)

 

 

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Indicator Descriptions

Access to Services (COG_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 (COG_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 (COG_ADM2_coping)

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

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Demographics (COG_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 (COG_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 (COG_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

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Flood Exposure (COG_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 communications@heigit.org!

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

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)	89
Columns	17 (13 numeric, 4 categorical, 0 datetime)
Train split	71 rows
Test split	17 rows
Geographic scope	COG
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-04-13

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Variables

Geographic — elderly (range 14.0–38348.0), elderly_rural (range 10.0–4263.0).

Demographic — female_pop (range 117.0–617995.0), female_u5 (range 19.0–85331.0), pop_u15 (range 97.0–479890.0), female_u15 (range 48.0–242248.0), female_pop_rural (range 117.0–77617.0) and 4 others.

Identifier / Metadata — adm2_pcode (CG0901, CG1109, CG0909), adm_pcode (CG0901, CG1109, CG0909), esa_source (HDX), esa_processed (2026-04-27).

Other — children_u5 (range 38.0–170335.0), children_u5_rural (range 38.0–30025.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-congo-rep")
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%	CG0901, CG1109, CG0909
female_pop	int64	0.0%	117.0 – 617995.0 (mean 22929.1685)
children_u5	int64	0.0%	38.0 – 170335.0 (mean 7458.8989)
female_u5	int64	0.0%	19.0 – 85331.0 (mean 3722.7528)
elderly	int64	0.0%	14.0 – 38348.0 (mean 1699.4607)
pop_u15	int64	0.0%	97.0 – 479890.0 (mean 19901.2472)
female_u15	int64	0.0%	48.0 – 242248.0 (mean 9872.7416)
female_pop_rural	int64	0.0%	117.0 – 77617.0 (mean 9472.4719)
children_u5_rural	int64	0.0%	38.0 – 30025.0 (mean 3271.809)
female_u5_rural	int64	0.0%	19.0 – 14843.0 (mean 1633.7191)
elderly_rural	int64	0.0%	10.0 – 4263.0 (mean 787.6629)
pop_u15_rural	int64	0.0%	97.0 – 76758.0 (mean 8584.2809)
female_u15_rural	int64	0.0%	48.0 – 37937.0 (mean 4230.4607)
rural_pop_perc	float64	0.0%	0.2 – 100.0 (mean 85.0308)
adm_pcode	object	0.0%	CG0901, CG1109, CG0909
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
female_pop	117.0	617995.0	22929.1685	8368.0
children_u5	38.0	170335.0	7458.8989	2626.0
female_u5	19.0	85331.0	3722.7528	1312.0
elderly	14.0	38348.0	1699.4607	702.0
pop_u15	97.0	479890.0	19901.2472	7279.0
female_u15	48.0	242248.0	9872.7416	3613.0
female_pop_rural	117.0	77617.0	9472.4719	7226.0
children_u5_rural	38.0	30025.0	3271.809	2380.0
female_u5_rural	19.0	14843.0	1633.7191	1212.0
elderly_rural	10.0	4263.0	787.6629	660.0
pop_u15_rural	97.0	76758.0	8584.2809	6346.0
female_u15_rural	48.0	37937.0	4230.4607	3127.0
rural_pop_perc	0.2	100.0	85.0308	100.0

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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_congo_rep,
  title     = {Congo Brazzaville - Risk Assessment Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/congo-brazzaville---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.