Urban Heat Island (UHI) is a phenomenon that can cause hotspots in city areas due to dense, impervious infrastructure and minimal vegetation cover. UHI hotspots may become worse in extreme heat events that are already aAecting many regions across the globe due to increased frequent hot extremes, humaninduced warming in cities, and rapidly growing urbanization, as documented by the latest IPCC report 2021. In seeking to support designers, planners, and decision-makers in developing and implementing adaptation strategies and measures to make our cities sustainable and resilient, reliable projections and modelling are required. In this study, we modelled UHI vulnerability using high-resolution spatial data, advanced geospatial tools, and socio-demographic data. This modiBed vulnerability approach drew upon UHI index maps and 20 select customized indicators of heat exposure, population sensitivity, and mobility/adaptive capacity. The indicators were Delphi evaluated and weighted, and the methodology was applied against the City of Greater Geelong municipality in Australia. The resulting UHI index maps indicated significant hotspots in areas of high building density, commercial/industrial zones, newly constructed sites, and zones with low urban green infrastructure. These UHI maps, in combination with selected indicators, highlighted the areal concentration of heat risk areas and vulnerable locations for the sensitive human population. The highlighted areas were primarily concentrated in high building density and high population density areas, which was seen through correlation curves. However, the building
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.
As growth regions evolve to accommodate the increasing population, they need to develop a wider variety of residential properties to accommodate the varying needs of the residents. As a result, the new accommodation is denser which involves higher embodied water carbon and energy. This research compares the construction differences in metropolitan and growth regions of Melbourne to identify embodied carbon, water, and energy. Representative areas of 25 km2 are selected from both regions. The growth region has 80% of the built area comprised of 2nd generation low-rise residential buildings whereas the prolific construction type in the Metropolitan region is mixed purpose industrial with 30% of the built area comprising of this type. The methodology implies open-source satellite imagery to build a spatial dataset in QGIS. The visual identification of the constructions in the study areas enables to identity the materials used in their construction. The total embodied carbon, water, and energy for the Metropolitan region are 32,895 tonnes, 4192 mL, and 3,694,412 GJ, respectively, whereas in the growth region, the totals are 179,376 tonnes carbon, 2533 mL water, and 2,243,571 GJ. Whilst Metropolitan has a significantly higher overall footprint when this is compared to the population of each region, it is shown that the growth region with its current construction type has a higher embodied carbon, water, and energy per head. The total per head for Metropolitan is 226.7 GJ energy, 257 kL water, and 20 tonnes carbon, whereas in the growth region, the embodied energy, water, and carbon, respectively, per head is 287.4 GJ, 324.6 kL, and 22 tonnes. The current performance per head of the growth region is considerably lower than that of Metropolitan. Using diverse residential construction types and efficient materials can serve the demanding needs of denser populated areas.
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