electricsheepafrica/africa-demographics-nigeria

Nigeria - 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 Nigeria, 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 (NGA_ADM2_access)
• Facilities (NGA_ADM2_facilities)
• Coping Capacity (NGA_ADM2_coping)
• Demographics (NGA_ADM2_demographics)
• Rural Population (NGA_ADM2_rural_population)
• Vulnerability (NGA_ADM2_vulnerability)
• Flood Exposure (NGA_ADM2_flood_exposure)

 

 

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Indicator Descriptions

Access to Services (NGA_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 (NGA_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 (NGA_ADM2_coping)

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

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Demographics (NGA_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 (NGA_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 (NGA_ADM2_vulnerability)

Combines Demographics and Rural Population indicators.

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Flood Exposure (NGA_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: NGA.

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)	774
Columns	52 (48 numeric, 4 categorical, 0 datetime)
Train split	619 rows
Test split	154 rows
Geographic scope	NGA
Publisher	HeiGIT (Heidelberg Institute for Geoinformation Technology)
HDX last updated	2026-04-13

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Variables

Geographic — rp10_elderly_30cm (range 0.0–14470.0), rp10_primary_healthcare_30cm_pct (range 0.0–100.0), rp10_primary_healthcare_30cm_count (range 0.0–6.0), rp50_elderly_30cm (range 0.0–16595.0), rp50_primary_healthcare_30cm_pct and 7 others.

Demographic — rp10_female_pop_30cm (range 0.0–221376.0), rp10_female_u5_30cm (range 0.0–26580.0), rp10_pop_u15_30cm (range 0.0–169289.0), rp10_female_u15_30cm (range 0.0–82068.0), rp50_female_pop_30cm (range 0.0–254249.0) and 11 others.

Outcome / Measurement — rp10_education_30cm_pct (range 0.0–100.0), rp10_education_30cm_count (range 0.0–51.0), rp10_hospitals_30cm_pct (range 0.0–100.0), rp10_hospitals_30cm_count (range 0.0–13.0), rp50_education_30cm_pct (range 0.0–100.0) and 11 others.

Identifier / Metadata — adm2_pcode (NG001001, NG018021, NG024012), adm_pcode (NG001001, NG018021, NG024012), esa_source (HDX), esa_processed (2026-04-27).

Other — rp10_children_u5_30cm (range 0.0–54610.0), rp50_children_u5_30cm (range 0.0–66262.0), rp100_children_u5_30cm, rp500_children_u5_30cm.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-demographics-nigeria")
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%	NG001001, NG018021, NG024012
adm_pcode	object	0.0%	NG001001, NG018021, NG024012
rp10_female_pop_30cm	int64	0.0%	0.0 – 221376.0 (mean 6148.1744)
rp10_children_u5_30cm	int64	0.0%	0.0 – 54610.0 (mean 1701.0504)
rp10_female_u5_30cm	int64	0.0%	0.0 – 26580.0 (mean 828.1059)
rp10_elderly_30cm	int64	0.0%	0.0 – 14470.0 (mean 405.7817)
rp10_pop_u15_30cm	int64	0.0%	0.0 – 169289.0 (mean 4895.0401)
rp10_female_u15_30cm	int64	0.0%	0.0 – 82068.0 (mean 2384.2726)
rp10_education_30cm_pct	int64	0.0%	0.0 – 100.0 (mean 1.7429)
rp10_education_30cm_count	int64	0.0%	0.0 – 51.0 (mean 0.2274)
rp10_hospitals_30cm_pct	int64	0.0%	0.0 – 100.0 (mean 1.0388)
rp10_hospitals_30cm_count	int64	0.0%	0.0 – 13.0 (mean 0.0995)
rp10_primary_healthcare_30cm_pct	int64	0.0%	0.0 – 100.0 (mean 0.876)
rp10_primary_healthcare_30cm_count	int64	0.0%	0.0 – 6.0 (mean 0.0452)
rp50_female_pop_30cm	int64	0.0%	0.0 – 254249.0 (mean 8581.491)
rp50_children_u5_30cm	int64	0.0%	0.0 – 66262.0 (mean 2376.208)
rp50_female_u5_30cm	int64	0.0%	0.0 – 33136.0 (mean 1157.1589)
rp50_elderly_30cm	int64	0.0%	0.0 – 16595.0 (mean 566.0207)
rp50_pop_u15_30cm	int64	0.0%	0.0 – 194395.0 (mean 6811.7351)
rp50_female_u15_30cm	int64	0.0%	0.0 – 94237.0 (mean 3321.7235)
rp50_education_30cm_pct	int64	0.0%	0.0 – 100.0 (mean 2.5181)
rp50_education_30cm_count	int64	0.0%	0.0 – 51.0 (mean 0.3165)
rp50_hospitals_30cm_pct	int64	0.0%
rp50_hospitals_30cm_count	int64	0.0%
rp50_primary_healthcare_30cm_pct	int64	0.0%
rp50_primary_healthcare_30cm_count	int64	0.0%
rp100_female_pop_30cm	int64	0.0%
rp100_children_u5_30cm	int64	0.0%
rp100_female_u5_30cm	int64	0.0%
rp100_elderly_30cm	int64	0.0%
rp100_pop_u15_30cm	int64	0.0%
rp100_female_u15_30cm	int64	0.0%
rp100_education_30cm_pct	int64	0.0%
rp100_education_30cm_count	int64	0.0%
rp100_hospitals_30cm_pct	int64	0.0%
rp100_hospitals_30cm_count	int64	0.0%
rp100_primary_healthcare_30cm_pct	int64	0.0%
rp100_primary_healthcare_30cm_count	int64	0.0%
rp500_female_pop_30cm	int64	0.0%
rp500_children_u5_30cm	int64	0.0%
rp500_female_u5_30cm	int64	0.0%
rp500_elderly_30cm	int64	0.0%
rp500_pop_u15_30cm	int64	0.0%
rp500_female_u15_30cm	int64	0.0%
rp500_education_30cm_pct	int64	0.0%
rp500_education_30cm_count	int64	0.0%
rp500_hospitals_30cm_pct	int64	0.0%
rp500_hospitals_30cm_count	int64	0.0%
rp500_primary_healthcare_30cm_pct	int64	0.0%
rp500_primary_healthcare_30cm_count	int64	0.0%
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
rp10_female_pop_30cm	0.0	221376.0	6148.1744	362.5
rp10_children_u5_30cm	0.0	54610.0	1701.0504	128.5
rp10_female_u5_30cm	0.0	26580.0	828.1059	62.5
rp10_elderly_30cm	0.0	14470.0	405.7817	23.0
rp10_pop_u15_30cm	0.0	169289.0	4895.0401	330.0
rp10_female_u15_30cm	0.0	82068.0	2384.2726	162.0
rp10_education_30cm_pct	0.0	100.0	1.7429	0.0
rp10_education_30cm_count	0.0	51.0	0.2274	0.0
rp10_hospitals_30cm_pct	0.0	100.0	1.0388	0.0
rp10_hospitals_30cm_count	0.0	13.0	0.0995	0.0
rp10_primary_healthcare_30cm_pct	0.0	100.0	0.876	0.0
rp10_primary_healthcare_30cm_count	0.0	6.0	0.0452	0.0
rp50_female_pop_30cm	0.0	254249.0	8581.491	477.0
rp50_children_u5_30cm	0.0	66262.0	2376.208	168.0
rp50_female_u5_30cm	0.0	33136.0	1157.1589	81.5

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