electricsheepafrica/africa-demographics-mali

Mali - 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 Mali, 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 (MLI_ADM2_access)
• Facilities (MLI_ADM2_facilities)
• Coping Capacity (MLI_ADM2_coping)
• Demographics (MLI_ADM2_demographics)
• Rural Population (MLI_ADM2_rural_population)
• Vulnerability (MLI_ADM2_vulnerability)
• Flood Exposure (MLI_ADM2_flood_exposure)

 

 

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Indicator Descriptions

Access to Services (MLI_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 (MLI_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 (MLI_ADM2_coping)

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

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Demographics (MLI_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 (MLI_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 (MLI_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

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Flood Exposure (MLI_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: MLI.

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)	53
Columns	17 (13 numeric, 4 categorical, 0 datetime)
Train split	42 rows
Test split	10 rows
Geographic scope	MLI
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-04-13

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Variables

Geographic — elderly (range 279.0–57636.0), elderly_rural (range 0.0–14255.0).

Demographic — female_pop (range 5379.0–1665030.0), female_u5 (range 942.0–237272.0), pop_u15 (range 5069.0–1288357.0), female_u15 (range 2444.0–656496.0), female_pop_rural (range 17.0–301767.0) and 4 others.

Identifier / Metadata — adm2_pcode (ML0101, ML0407, ML0502), adm_pcode (ML0101, ML0407, ML0502), esa_source (HDX), esa_processed (2026-04-27).

Other — children_u5 (range 1931.0–480320.0), children_u5_rural (range 6.0–112810.0).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-mali")
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%	ML0101, ML0407, ML0502
female_pop	int64	0.0%	5379.0 – 1665030.0 (mean 221280.5283)
children_u5	int64	0.0%	1931.0 – 480320.0 (mean 77815.7547)
female_u5	int64	0.0%	942.0 – 237272.0 (mean 38432.3774)
elderly	int64	0.0%	279.0 – 57636.0 (mean 10792.3774)
pop_u15	int64	0.0%	5069.0 – 1288357.0 (mean 205528.0566)
female_u15	int64	0.0%	2444.0 – 656496.0 (mean 100799.434)
female_pop_rural	int64	0.0%	17.0 – 301767.0 (mean 117770.5283)
children_u5_rural	int64	0.0%	6.0 – 112810.0 (mean 42666.9245)
female_u5_rural	int64	0.0%	3.0 – 56005.0 (mean 21073.1132)
elderly_rural	int64	0.0%	0.0 – 14255.0 (mean 5750.8868)
pop_u15_rural	int64	0.0%	16.0 – 293602.0 (mean 112420.6792)
female_u15_rural	int64	0.0%	8.0 – 144137.0 (mean 54839.1132)
rural_pop_perc	float64	0.0%	0.0 – 100.0 (mean 70.7498)
adm_pcode	object	0.0%	ML0101, ML0407, ML0502
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	5379.0	1665030.0	221280.5283	149345.0
children_u5	1931.0	480320.0	77815.7547	55314.0
female_u5	942.0	237272.0	38432.3774	27462.0
elderly	279.0	57636.0	10792.3774	7766.0
pop_u15	5069.0	1288357.0	205528.0566	143936.0
female_u15	2444.0	656496.0	100799.434	70665.0
female_pop_rural	17.0	301767.0	117770.5283	114373.0
children_u5_rural	6.0	112810.0	42666.9245	42753.0
female_u5_rural	3.0	56005.0	21073.1132	20988.0
elderly_rural	0.0	14255.0	5750.8868	5773.0
pop_u15_rural	16.0	293602.0	112420.6792	110048.0
female_u15_rural	8.0	144137.0	54839.1132	53423.0
rural_pop_perc	0.0	100.0	70.7498	76.33

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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_mali,
  title     = {Mali - Risk Assessment Indicators},
  author    = {HeiGIT (Heidelberg Institute for Geoinformation Technology)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/mali---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.