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Mapping Urban Heat Vulnerability in London: Multi-Scale Geospatial Analysis Using Machine Learning, SHAP Explainability, and Temporal Mortality Data

Mapping Urban Heat Vulnerability in London: Multi-Scale Geospatial Analysis Using Machine Learning, SHAP Explainability, and Temporal Mortality Data

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Authors

Adesuyi Adeoti, William Alston

Abstract

Urban heat is a significant cause of mortality. In London, during the summer of 2022, the United Kingdom experienced its first recorded 40°C day, resulting in excess mortality levels not observed since pre-antibiotic influenza seasons. On 19 July 2022, London reached a temperature of 40.3°C, the highest recorded in the United Kingdom, which corresponded to 18,435 excess deaths in England and Wales. This study develops the first Lower Layer Super Output Area (LSOA)-scale heat vulnerability index (VI) for all 33 London Boroughs (n = 4,994) by integrating eight distinct datasets using a reproducible six-script Python pipeline. A critical methodological step involved identifying and excluding seven variables that leaked data, thereby reducing the maximum feature-VI correlation from 1.000 to 0.772. Five machine learning models were trained and validated spatially using borough-based data splitting. The four best-performing models achieved Test R² scores between 0.864 and 0.873, with overlapping 95% bootstrap confidence intervals, indicating that model learnability is not dependent on specific architectural choices. XGBoost is the main model (Test R² = 0.869, MAE = 0.161); the SHAP attribution shows HVI_mean (|SHAP| mean = 0.272) and imd_employment_score (0.216) as co-dominant drivers in all five architectures. Temporal trend analysis from 2015 to 2024 identified a pronounced asymmetry: the number of deaths increased substantially during the summer period (τ = 0.644, p = 0.012, with 1,544 additional deaths annually), whereas temperature trends were not statistically significant (p = 0.371). However, seasonal-trend decomposition using LOESS (STL) indicated an underlying warming trend (p = 0.032). The environmental justice analysis demonstrated a vulnerability ratio of 1.30 between the most and least deprived decile groups, with 60.3% of the vulnerability index attributable to physical thermal exposure. UKCP18 projections indicate that Barnet, currently ranked 32nd among 33 boroughs, is projected to become the primary location for emerging hot spots.

DOI

https://doi.org/10.31223/X5M212

Subjects

Environmental Sciences, Physical Sciences and Mathematics

Keywords

heat vulnerability, urban heat, climate–health equity, remote sensing, machine learning, SHAP, spatial cross-validation, London, LSOA, UKCP18.

Dates

Published: 2026-08-06 10:45

Last Updated: 2026-08-06 10:45

License

CC-BY Attribution-NonCommercial 4.0 International

Additional Metadata

Conflict of interest statement:
None

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