Population-weighted urban heat inequity in Almaty from satellite temperature and greenness data
- 1Faculty of General Medicine, Asfendiyarov Kazakh National Medical University, Almaty, Kazakhstan
Abstract
Background: Extreme heat is a growing urban public-health risk, yet within-city heat inequity remains poorly quantified in Central Asian settings. This study mapped where high land surface temperature, low greenness and higher residential population overlap in Almaty, Kazakhstan, to identify priority zones for climate-resilient heat-health planning.
Methods: A cross-sectional spatial analysis was conducted in Google Earth Engine using cloud-masked Landsat 8/9 Collection 2 Level-2 imagery for June-August 2025. Land surface temperature (LST) was derived from the ST_B10 thermal band and converted to degrees Celsius; greenness was measured using NDVI. Residential population was represented using WorldPop gridded data. A population-weighted Heat Inequity Index combined normalized LST, inverse normalized NDVI and population normalized to the 95th percentile. Priority hotspots were defined as the top decile of the index.
Results: The exported 10,205-point sample showed mean LST of 36.91 °C and mean NDVI of 0.506. LST and NDVI were strongly inversely associated (r=-0.854, p < 0.001); each 0.1 increase in NDVI corresponded to 2.84 °C lower LST. Priority hotspots had higher mean LST than non-hotspot areas (42.31 °C vs 36.31 °C), lower NDVI (0.243 vs 0.535), and higher population values (40.13 vs 10.53). Thus, top-decile priority zones were approximately 6.0 °C hotter, 54.5% less green and 3.8 times more populated than other areas.
Conclusions: Satellite-derived temperature, greenness and population data can identify actionable urban heat inequity hotspots in Almaty. Priority zones should be considered for targeted tree-canopy expansion, shade infrastructure, cooling corridors and heat-health warning interventions. Future work should validate findings with official administrative boundaries, air-temperature observations and health outcome data.
Key messages: • High-priority heat zones in Almaty combined hotter surfaces, lower greenness and substantially higher residential population.
• Population-weighted satellite mapping can guide targeted tree canopy, shade infrastructure and heat-health planning.
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BibTeX
@article{kabylbek2026populationweighted,
title = {{Population-weighted urban heat inequity in Almaty from satellite temperature and greenness data}},
author = {M Kabylbek},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.905},
url = {https://doi.org/10.1093/eurpub/ckag169.905},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Population-weighted urban heat inequity in Almaty from satellite temperature and greenness data
AU - M Kabylbek
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.905
UR - https://doi.org/10.1093/eurpub/ckag169.905
AB - Background: Extreme heat is a growing urban public-health risk, yet within-city heat inequity remains poorly quantified in Central Asian settings. This study mapped where high land surface temperature, low greenness and higher residential population overlap in Almaty, Kazakhstan, to identify priority zones for climate-resilient heat-health planning. Methods: A cross-sectional spatial analysis was conducted in Google Earth Engine using cloud-masked Landsat 8/9 Collection 2 Level-2 imagery for June-August 2025. Land surface temperature (LST) was derived from the ST_B10 thermal band and converted to degrees Celsius; greenness was measured using NDVI. Residential population was represented using WorldPop gridded data. A population-weighted Heat Inequity Index combined normalized LST, inverse normalized NDVI and population normalized to the 95th percentile. Priority hotspots were defined as the top decile of the index. Results: The exported 10,205-point sample showed mean LST of 36.91 °C and mean NDVI of 0.506. LST and NDVI were strongly inversely associated (r=-0.854, p < 0.001); each 0.1 increase in NDVI corresponded to 2.84 °C lower LST. Priority hotspots had higher mean LST than non-hotspot areas (42.31 °C vs 36.31 °C), lower NDVI (0.243 vs 0.535), and higher population values (40.13 vs 10.53). Thus, top-decile priority zones were approximately 6.0 °C hotter, 54.5% less green and 3.8 times more populated than other areas. Conclusions: Satellite-derived temperature, greenness and population data can identify actionable urban heat inequity hotspots in Almaty. Priority zones should be considered for targeted tree-canopy expansion, shade infrastructure, cooling corridors and heat-health warning interventions. Future work should validate findings with official administrative boundaries, air-temperature observations and health outcome data. Key messages: • High-priority heat zones in Almaty combined hotter surfaces, lower greenness and substantially higher residential population. • Population-weighted satellite mapping can guide targeted tree canopy, shade infrastructure and heat-health planning.
SN - 1101-1262
SN - 1464-360X
ER -