An Investigation to Identify the Effectiveness of Socioeconomic, Demographic, and Buildings’ Characteristics on Surface Urban Heat Island Patterns

Paras Sidiqui, Muhammad Atiq Ur Rehman Tariq and Anne W. M. Ng
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Paras Sidiqui: Live + Smart Research Laboratory, School of Architecture & Built Environment, Deakin University, Geelong 3220, Australia
Muhammad Atiq Ur Rehman Tariq: College of Engineering, IT & Environment, Charles Darwin University, Darwin 0810, Australia
Anne W. M. Ng: College of Engineering, IT & Environment, Charles Darwin University, Darwin 0810, Australia

Sustainability, 2022, vol. 14, issue 5, 1-21

Abstract: Despite implementing adaptation strategies and measures to make cities sustainable and resilient, the urban heat island (UHI) has been increasing risks to human health and the urban environment by causing hot spots in city areas. This study investigates the spatial patterns in the surface urban heat island (SUHI) over the study site and develops its relationships to socioeconomic, demographic, and buildings’ characteristics. This paper examines the role of building roof types, building roof material, building height, building age, and socioeconomic and demographic factors in driving the SUHI in a city. Numerous studies have focused primarily on the influence of biophysical and meteorological factors on variations in land surface temperatures (LSTs); however, very little attention has been paid to examining the influence of socioeconomic, demographic, and building factors on SUHIs within a city. The analysis has been carried out by processing Landsat based LST data to UHI in the Google Earth Engine (GEE) cloud-based platform. The satellite-based research is further integrated with GIS data acquired from the state government and local city council. Linear regression and multiple regression correlations are further run to examine selected factors’ variance on SUHI. Results indicate socioeconomic, demographic, and building factors contribute significantly to SUHI generation; these factors collectively can explain 28% of the variance in SUHI patterns with significant p -values.

Keywords: surface urban heat island (SUHI); remote sensing; GIS; satellite data; LST; EVI; socioeconomic factors; demographic factors; SUHI drivers; urban greenness; Landsat (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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