Assessing the Impacts of Urban Heat Islands on Residential Energy Demand and Vulnerable Populations in a Megacity Using Remote Sensing and GIS Analysis

Project Abstract

Cities worldwide are experiencing intensified urban heat islands (UHIs) that exacerbate energy consumption, particularly in residential sectors, while disproportionately impacting vulnerable populations with heightened exposure to heat stress and cooling costs. This study investigates the spatial and temporal dynamics of UHIs in a megacity and evaluates how UHI intensity relates to residential energy demand and the distribution of heat vulnerability among residents, employing an integrated remote sensing and geographic information systems (GIS) framework. Landsat, Sentinel-2, and thermal infrared imagery are used to derive land surface temperature (LST) and albedo-based indices across a ten-year period (2014–2023), enabling the construction of high-resolution UHI maps and urban canopy/green space metrics. Household energy demand data, electricity tariffs, and appliance usage patterns are harmonized with meteorological data (air temperature, humidity, wind) and socio-economic indicators (income, housing type, age, health status) to quantify the sensitivity of cooling and heating energy use to ambient thermal conditions. We implement a mixed-methods approach that combines spatial regression, geographically weighted regression (GWR), and machine learning models (random forest and gradient boosting) to identify key drivers of energy demand, such as building morphology, roof reflectivity, vegetation cover, and surface material. Vulnerability is assessed through a composite index incorporating exposure, sensitivity, and adaptive capacity, derived from census data, health records (where available), and accessibility to cooling resources (public cooling centers, green infrastructure). The analysis reveals spatial heterogeneity in UHI effects, with dense, low-vegetation cores showing the highest LST anomalies and correspondingly elevated residential energy use peaks during heatwaves. The results indicate that green corridors, high-albedo surfaces, and increased shaded area significantly mitigate UHI intensity and reduce energy demand, while housing types with poor insulation and limited access to affordable cooling experience disproportionate energy burdens. Scenario testing evaluates policy interventions, including heat-resilient building retrofits, district cooling networks, and urban greening strategies, estimating potential reductions in peak electricity load and household expenditures under extreme heat conditions. The study also examines equity implications by overlaying vulnerability maps with energy burden, highlighting neighborhoods where high heat exposure coincides with low adaptive capacity, thereby informing targeted adaptation measures. Sensitivity analyses address uncertainties in LST as a proxy for near-surface temperatures, temporal resolution of remote sensing data, and the transferability of models across similar megacities. The integration of remote sensing-derived thermal metrics with granular energy and socio-demographic data provides a robust framework for urban planners to diagnose UHI-related energy inefficiencies and to prioritize equitable adaptation investments. The research contributes to methodological advancements in UHI quantification, enhances understanding of energy insecurity drivers in rapidly urbanizing contexts, and offers actionable insights for building codes, urban design, and climate resilience policies that balance energy efficiency with social equity in megacities.

Project Overview

What This Project Is About

---

A plain-language overview of how urban heat in big cities affects how much energy people use at home and who gets affected most, using simple maps and satellite data to link heat, energy use, and vulnerable groups.

---

---

The Problem It Addresses

---

Cities often get hotter than surrounding areas, raising cooling needs and costs for residents, especially the elderly, low-income households, and those in poorly built neighborhoods. There is a gap in how heat patterns are tied to real energy use and who is most at risk, which makes it hard to plan affordable, fair cooling and energy policies.

---

---

Objectives of the Project

---

1. Identify areas in a megacity with the strongest urban heat island effects.
2. Link heat patterns to residential energy demand indicators (e.g., electricity use for cooling).
3. Assess which population groups are most vulnerable to heat and energy burdens.
4. Provide practical recommendations for equitable cooling strategies.

---

---

What You Will Do Step by Step

---

1. Review basic concepts like urban heat islands, energy demand, and vulnerability.
2. Gather open data: land surface temperature (from satellites), basic energy data, and population characteristics.
3. Create simple maps showing heat intensity and energy use hotspots.
4. Analyze relationships between heat levels and energy demand; identify vulnerable groups.
5. Interpret results and discuss policy or planning implications.

---

---

Expected Outcome

A clear, easy-to-use set of maps and findings that show where cooling costs are highest and who needs the most support, plus practical steps for urban planners to reduce heat exposure and energy hardship.