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This research presents a GIS-based framework used to detect urban heat islands and determine which urban settlement elements are most critical when heatwave risks exist. The proposed method uses the Iterative Self-Organizing Data Analysis (ISODATA) clustering algorithm applied to the satellite land surface temperature distribution recorded during heatwaves for the detection of urban heat islands. A pixel classification confidence level maximization approach, obtained by running a maximum likelihood classification algorithm, is performed to determine the optimal number of clusters. The areas labeled as hotspots constitute the detected urban heat islands (UHIs). This method was tested on an urban settlement set up by the municipality of Naples (Italy). Comparison tests were performed with other urban heat island detection methods such as standard deviation thresholding and Getis-Ord Gi* hotspot detection; indices measuring the density of buildings, the percentage of permeable open spaces, and vegetation cover are taken into consideration to evaluate the accuracy of the urban heat islands detected. These tests highlight that the proposed method provides the most accurate results. It could be an effective tool to support the decision maker in evaluating which urban areas are the most critical during heatwave scenarios. [ABSTRACT FROM AUTHOR]

This research presents a new method for detecting urban areas critical for the presence of air pollutants during periods of heatwaves. The proposed method uses a geospatial model based on the construction of Thiessen polygons and a fuzzy model based on assessing, starting from air quality control unit measurement data, how concentrations of air pollutants are distributed in the urban study area during periods of heatwaves and determine the most critical areas as hotspots. The proposed method represents an optimal trade-off between the accuracy of the detection of critical areas and the computational speed; the use of fuzzy techniques for assessing the intensity of concentrations of air pollutants allows evaluators to model the assessments of critical areas more naturally. The method is implemented in a GIS-based platform and has been tested in the city of Bologna, Italy. The resulting criticality maps of PM10, NO2, and PM2.5 pollutants during a heatwave period that occurred from 10 to 14 July 2023 revealed highly critical hotspots with high pollutant concentrations in densely populated areas. This framework provides a portable and easily interpretable decision support tool which allows you to evaluate which urban areas are most affected by air pollution during heatwaves, potentially posing health risks to the exposed population. [ABSTRACT FROM AUTHOR]

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold. [ABSTRACT FROM AUTHOR]

In this work, we propose a GIS-based platform aimed at the analysis of heatwave scenarios risks produced in urbanised environments, applied to assess vulnerability and impact heatwave scenarios. Our framework implements a hierarchical model that represents a good trade-off between forecast accuracy and portability in different urban fabrics, apart from the spatial scale of the data, using topographic and remote sensing spatial data provided by institutional agencies. The framework has been applied to two study areas: the dense city of Naples (Italy) and the intermediately populated city of Avellino (Italy) in order to evaluate its accuracy performances and portability in different urban fabrics. Our framework can be used by urban planners and decision makers as a tool to locate potential risk zones where it is necessary to implement climate-resilient solutions. [ABSTRACT FROM AUTHOR]

Urban areas are vulnerable to multiple risks associated with hydro-meteorological hazards (HMHs). The assessment of the climate benefits of implementing nature-based solutions (NBSs) in urban areas, especially in open spaces, is widely recognised and discussed within the scientific literature; however, the quantification of these benefits, in terms of the HMHs reduction, human safety and human well-being, is still a subject of debate. In this context, this contribution proposes a methodological approach that, starting from the analysis of the impacts of coastal flooding and in terms of the potential direct and tangible economic damages, heatwave events and vulnerability of open spaces, proposes the application and assessment of NBSs in terms of the reduction in these impacts. The process was developed in the GIS environment based on the processing of open-source data. The test was conducted in the case study of Naples' waterfront to identify the potentialities and limitations of the approach. The results showed the contribution of NBSs in reducing the economic damages due to coastal flooding and the improved vulnerability conditions to heatwave events. [ABSTRACT FROM AUTHOR]

Hot and cold spot identification is a spatial analysis technique used in various issues to identify regions where a specific phenomenon is either strongly or poorly concentrated or sensed. Many hot/cold spot detection techniques are proposed in literature; clustering methods are generally applied in order to extract hot and cold spots as polygons on the maps; the more precise the determination of the area of the hot (cold) spots, the greater the computational complexity of the clustering algorithm. Furthermore, these methods do not take into account the hidden information provided by users through social networks, which is significant for detecting the presence of hot/cold spots based on the emotional reactions of citizens. To overcome these critical points, we propose a GIS-based hot and cold spot detection framework encapsulating a classification model of emotion categories of documents extracted from social streams connected to the investigated phenomenon is implemented. The study area is split into subzones; residents' postings during a predetermined time period are retrieved and analyzed for each subzone. The proposed model measures for each subzone the prevalence of pleasant and unpleasant emotional categories in different time frames; with the aid of a fuzzy-based emotion classification approach, subzones in which unpleasant/pleasant emotions prevail over the analyzed time period are labeled as hot/cold spots. A strength of the proposed framework is to significantly reduce the CPU time of cluster-based hot and cold spot detection methods as it does not require detecting the exact geometric shape of the spot. Our framework was tested to detect hot and cold spots related to citizens' discomfort due to heatwaves in the study area made up of the municipalities of the northeastern area of the province of Naples (Italy). The results show that the hot spots, where the greatest discomfort is felt, correspond to areas with a high population/building density. On the contrary, cold spots cover urban areas having a lower population density. [ABSTRACT FROM AUTHOR]