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April 1, 2026City and Environment Interactions2 citationsOpen Access

The impact of hazard indicator selection on urban heat risk assessment: evidence from two Sub-Saharan African cities

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TMTobi Eniolu MorakinyoOOOluwafemi Benjamin ObeGMGerald Mills

Key Points

  • This research aims to evaluate how different hazard indicators impact urban heat risk assessments in two African cities.
  • Compared heat risk zones using two indicators: Land Surface Temperature (LST) and Humidex (HI).
  • Analyzed spatial agreement through statistical measures like Cohen’s Kappa and Bivariate Moran’s I.
  • Utilized Monte Carlo simulations to assess uncertainty from Local Climate Zone misclassification.
  • Demonstrated moderate-to-strong spatial agreement between LST- and HI-based critical heat risk zones.
  • Highlighted heat risk concentrations in Local Climate Zones 3, 6, and 7 across both cities.
  • Showed that indicator merging enhances urban planning and adaptation strategies.

Abstract

• Hazard indicator choice (LST vs. Humidex) shapes urban heat risk assessment in African cities. • LCZ-disaggregated CHRZ maps reveal hotspots concentrated in LCZ 3, 6, and 7. • Statistical analysis confirms moderate-to-strong spatial agreement between LST- and Humidex-based CHRZs. • Monte Carlo simulations quantify uncertainty from LCZ misclassification on CHRZ distribution. • Framework provides a replicable approach for planning and adaptation in data-scarce tropical cities. Urban heat exposure poses significant health and socio-economic risks in rapidly growing Sub-Saharan African cities. Accurate mapping of heat risk is critical for targeted adaptation, yet the choice of hazard indicator can influence risk assessment outcomes. This study evaluates the impact of hazard indicator selection on urban heat risk by comparing Critical Heat Risk Zones (CHRZs) derived from Land Surface Temperature (LST) and Humidex (HI) in Dar es Salaam, Tanzania, and Lagos, Nigeria. Using high-resolution geospatial data, WRF derived climate data, and Local Climate Zone (LCZ) disaggregation, we quantified the spatial agreement between LST- and HI-based CHRZs through Cohen’s Kappa and Bivariate Moran’s I. Results show moderate-to-strong spatial correspondence in both cities (Dar es Salaam: Kappa = 0.584, Moran’s I = 0.706; Lagos: Kappa = 0.336, Moran’s I = 0.521), with heat risk concentrated in LCZ 3, 6, and 7. Monte Carlo simulations highlight the robustness of LCZ-based CHRZ disaggregation under realistic classification uncertainty. Findings emphasize that merging multiple hazard indicators can guide urban planning, early-warning systems, and neighbourhood-level interventions. The framework is transferable to other data-scarce African cities, supporting context-sensitive heat mitigation strategies and climate-resilient urban development.

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Cite This Study

Morakinyo et al. (2026) studied this question.

synapsesocial.com/papers/69cd7aa45652765b073a7e43https://doi.org/10.1016/j.cacint.2026.100338
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