URBAN HEAT ISLANDS AND SOCIAL STRATIFICATION: NEIGHBORHOOD-LEVEL VULNERABILITY IN FAISALABAD

Abstract

Urban Heat Islands UHIs are amongst the most urgent of urban environmental problems in fast-growing cities, where climatic stress converges with social inequity. In densely populated South Asian megacities such as Faisalabad, Pakistan’s third-largest city, the unequal distribution of heat exposure amplifies existing social divisions. Empirical research has shown that low-income communities are disproportionately exposed to the impacts of UHIs resulting from poor quality housing, lack of vegetation, and limited access to cooling infrastructure (Harlan et al., 2006; Jenerette et al. This article examines neighborhood-level vulnerability in Faisalabad, combining satellite-based land surface temperature (LST) data and socioeconomic indicators. We took a mixed-methods approach, using machine learning classification models (Random Forest and Gradient Boosting) to map UHI intensity and qualitative interviews to capture residents lived experience of heat stress. General patterns indicate pronounced spatial disparities: high-dense, low-income residential areas show increased UHI magnitude; the demand for adaptive elements is their rate-limiting factor. More affluent areas on the other hand have better planning, vegetation and access to cooling technologies. These results complement international evidence concerning the concentration of environment risk with social deprivation (Chakraborty et al., 2019). The research highlights the importance of equity-driven climate adaptation interventions, such as heat action plans, urban greening programs and infrastructure upgrades directed at the most vulnerable.

Keywords: Urban Heat Island, social stratification, vulnerability, Faisalabad, machine learning, climate adaptation.