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

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...

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Bibliographic Details
Published inSustainability Vol. 14; no. 5; p. 2777
Main Authors Sidiqui, Paras, Tariq, Muhammad Atiq Ur Rehman, Ng, Anne W. M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2022
Subjects
Buildings
Cities
Climate change
Data acquisition
Demographics
Design
Health risks
Heat
Land surface temperature
Land use
Landsat
Literature reviews
Population density
Roofing materials
Satellites
Socioeconomic factors
Socioeconomics
Sustainability
Temperature
Urban environments
Urban heat islands
Variables
Vegetation
Hong Kong China
China
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Summary: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.
ISSN:2071-1050
2071-1050
DOI:10.3390/su14052777