electricsheepafrica/africa-synth-climate-urban-heat-island-mapping-all

⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

African Urban Heat Island Mapping

A comprehensive synthetic dataset of urban heat island (UHI) characteristics across 20 major African cities with spatially-distributed sampling across urban-rural gradients and 3 climate scenarios. Designed for urban climate planning, heat risk assessment, and adaptation research.

Overview

This dataset contains 27,000+ spatially-distributed records covering urban heat island metrics for 20 major African cities across 3 climate scenarios (~450 spatial sampling points per city per scenario). Parameters are calibrated from peer-reviewed literature including IPCC AR6 reports, remote sensing LST studies, and World Bank urban heat risk assessments.

Cities (20)

City	Country	Population (M)	Koppen	Baseline UHI (C)
Lagos	Nigeria	15.38	Aw	3.8
Cairo	Egypt	20.90	BWh	4.5
Johannesburg	South Africa	5.63	Cwb	3.2
Nairobi	Kenya	4.74	Cwb	3.5
Addis Ababa	Ethiopia	5.23	Cwb	2.8
Accra	Ghana	2.56	Aw	3.6
Dakar	Senegal	3.14	BSh	3.4
Kinshasa	DR Congo	14.97	Aw	3.0
Dar es Salaam	Tanzania	7.41	Aw	3.3
Kampala	Uganda	3.52	Aw	2.9
Casablanca	Morocco	3.72	Csa	3.1
Tunis	Tunisia	2.85	Csa	3.3
Luanda	Angola	8.33	BSh	3.5
Maputo	Mozambique	1.19	Aw	2.7
Kigali	Rwanda	1.13	Cwb	2.5
Bamako	Mali	2.53	BSh	4.2
Abidjan	Cote d'Ivoire	5.07	Aw	3.2
Douala	Cameroon	3.54	Am	2.8
Algiers	Algeria	2.85	Csa	3.4
Khartoum	Sudan	5.27	BWh	4.8

Scenarios

baseline

Current conditions reflecting 2020-2024 averages. Parameters calibrated from remote sensing LST observations and urban morphology studies.

extreme_heat

SSP5-8.5 high-emissions scenario projecting +2.7C warming by 2050 (IPCC AR6). Includes amplified UHI intensity (+1.5C), vegetation stress (NDVI -0.08), increased impervious surfaces, reduced nighttime cooling, and 45% increase in cooling demand.

green_infrastructure

Intervention scenario modeling comprehensive green infrastructure deployment: cool roofs/pavements (albedo +0.06), expanded tree canopy (+10%), permeable surfaces, resulting in -0.8C temperature reduction and -1.2C UHI intensity reduction.

Variables

Variable	Description	Range
city	City name	20 African cities
country	Country name	-
latitude	Sampling point latitude	-37 to 37
longitude	Sampling point longitude	-18 to 40
distance_from_center_km	Distance from city center	0-30 km
zone	Urban zone classification	urban_core, suburban, rural
scenario	Climate scenario	baseline, extreme_heat, green_infrastructure
scenario_description	Scenario description text	-
koppen_climate	Koppen climate classification	Aw, BWh, BSh, Cwb, Csa, Am
elevation_m	City elevation above sea level	4-2355 m
population_millions	Metropolitan population	1.13-20.9 M
surface_temperature_c	Land surface temperature	10-45 C
daytime_max_temperature_c	Daytime maximum temperature	15-52 C
nighttime_min_temperature_c	Nighttime minimum temperature	5-35 C
uhi_intensity_c	Urban heat island intensity (urban-rural differential)	0-10 C
ndvi	Normalized Difference Vegetation Index	0.02-0.85
building_density	Building footprint density	0.05-0.95
impervious_surface_area_frac	Fraction of impervious surface area	0.02-0.98
albedo	Surface albedo (reflectivity)	0.08-0.35
green_space_accessibility_pct	Green space within 300m	0-100%
tree_canopy_cover_pct	Tree canopy cover percentage	0-60%
water_body_pct	Water body coverage	0-50%
heat_vulnerability_index	Composite heat vulnerability (0-1)	0.15-0.95
cooling_demand_kwh_per_capita	Annual cooling energy demand	10-800 kWh
population_density_per_km2	Population density	50-50000 /km2
population_exposed_uhi_2c	Population exposed to UHI > 2C	0-max density
population_exposed_nighttime_25c	Population exposed to nighttime > 25C	0-max density
heatwave_frequency_monthly	Monthly heatwave event frequency	0-10
sky_view_factor	Sky visibility factor (urban canyon)	0.1-1.0
urban_canyon_height_width_ratio	Building height to street width ratio	0.1-3.0
floor_area_ratio	Total floor area to lot area ratio	0.1-15.0
impervious_vegetation_ratio	Impervious to vegetated surface ratio	0.01-50.0
surface_roughness_length_m	Aerodynamic roughness length	0.01-5.0 m
nighttime_cooling_capacity_c	Nighttime temperature drop capacity	0-6 C

Data Files

File	Records	Description
baseline.csv	~9,000	Baseline scenario (2020-2024 average)
extreme_heat.csv	~9,000	SSP5-8.5 extreme heat scenario
green_infrastructure.csv	~9,000	Green infrastructure intervention scenario
combined.csv	~27,000	All scenarios combined
metadata.json	-	Dataset metadata and references

Usage

from datasets import load_dataset

# Load all scenarios
dataset = load_dataset("electricsheepafrica/african-urban-heat-island-mapping")

# Load specific scenario
baseline = load_dataset("electricsheepafrica/african-urban-heat-island-mapping", "baseline")
extreme = load_dataset("electricsheepafrica/african-urban-heat-island-mapping", "extreme_heat")
green = load_dataset("electricsheepafrica/african-urban-heat-island-mapping", "green_infrastructure")

# Access as pandas
df = baseline["train"].to_pandas()

# Filter by city
lagos_uhi = df[df["city"] == "Lagos"]

# Compare scenarios
cities = df["city"].unique()
for city in cities:
    city_data = df[df["city"] == city]
    print(f"{city}: mean UHI = {city_data['uhi_intensity_c'].mean():.2f}C")

Methodology

Spatial Sampling

Each city is sampled with ~450 points distributed across an urban-rural gradient:

• Urban core (0-5 km): 35% of points, highest UHI intensity
• Suburban (5-15 km): 35% of points, transitional zone
• Rural (15-30 km): 30% of points, baseline reference

Parameter Calibration

City-level parameters are calibrated from:

• LST observations: MODIS/Landsat-derived land surface temperature studies
• Urban morphology: Satellite-derived building density and impervious surface maps
• NDVI: Sentinel-2 vegetation indices for African cities
• Climate data: WorldClim and CHIRPS baseline climatologies
• Vulnerability indices: World Bank and UN-Habitat urban risk assessments

Scenario Construction

• Extreme heat: Based on IPCC AR6 SSP5-8.5 projections for Africa, with UHI amplification factors from Oke (2017) and Zhou et al. (2019)
• Green infrastructure: Based on documented cooling effects from green roofs, urban forests, and cool surfaces (Santamouris 2014, Norton et al. 2015)

References

1. IPCC, 2022: Climate Change 2022: Impacts, Adaptation and Vulnerability. WGII Contribution to AR6. Chapter 6 (Cities) and Chapter 9 (Africa).
2. IPCC, 2021: Climate Change 2021: The Physical Science Basis. WGI Contribution to AR6. Chapter 12.
3. Molla, A. (2022). Evaluating contributions of urbanization and global climate change to urban land surface temperature change: a case study in Lagos, Nigeria. Scientific Reports, 12, 14078.
4. Dallimer, M. et al. (2022). The Impacts of Urbanisation and Climate Change on the Urban Thermal Environment in Africa. Climate, 10(11), 164.
5. World Bank (2022-2024). Urban Heat Risk Assessments for African Cities.
6. Oke, T.R. et al. (2017). Urban Climates. Cambridge University Press.
7. Santamouris, M. (2014). Cooling the cities - A review of reflective and green roof mitigation technologies. Energy and Buildings, 82, 681-695.

Citation

@dataset{african_urban_heat_island_2025,
  title={African Urban Heat Island Mapping Dataset},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/african-urban-heat-island-mapping},
  license={CC-BY-4.0}
}

License

CC BY 4.0 - This is synthetic data generated for research and educational purposes, calibrated from peer-reviewed literature.