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.

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.

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.

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.

Many sentiment analysis methods have been proposed recently to evaluate, through the Web, the perceptions of users and their satisfaction with the use of products and services; these approaches have been applied in various fields in which it is necessary to evaluate, for example, the degree of appreciation of a product or a service or political orientations or emotional states following an event or the occurrence of a phenomenon. On the other hand, these methods are based on natural language processing models needed to capture information hidden in comments, which generally require a high computational cost which can affect their performance; for this reason, review-collecting providers prefer to synthetically evaluate user satisfaction by considering a score on a numerical scale entered by users. To overcome this criticality, we propose an emotion detection method based on a light fuzzy-based document classification model to capture the relevance of pleasant and unpleasant emotions expressed by users in their reviews of service facilities. This method is implemented in a geo-computational framework and tested to evaluate the satisfaction of customers of theater venues located in the municipality of Naples (Italy). A fuzzy-based approach is used to classify user satisfaction according to the relevance of the emotional categories of pleasant and unpleasant. We show that our emotion detection method refines service feature pleasure assessments expressed on scales by users in their reviews.

Image segmentation is a well-known image processing task that consists of partitioning an image into homogeneous areas. It is applied to remotely sensed imagery for many problems such as land use classification and landscape changes. Recently, several hybrid remote sensing image segmentation techniques have been proposed that include metaheuristic approaches in order to increase the segmentation accuracy; however, the critical point of these approaches is the high computational complexity, which affects time and memory consumption. In order to overcome this criticality, we propose a fuzzy-based image segmentation framework implemented in a GIS-based platform for remotely sensed images; furthermore, the proposed model allows us to evaluate the reliability of the segmentation. The Fast Generalized Fuzzy c-means algorithm is implemented to segment images in order to detect local spatial relations between pixels and the Triple Center Relation validity index is used to find the optimal number of clusters. The framework elaborates the composite index to be analyzed starting by multiband remotely sensed images. For each cluster, a segmented image is obtained in which the pixel value represents, transformed into gray levels, the graph belonging to the cluster. A final thematic map is built in which the pixels are classified based on the assignment to the cluster to which they belong with the highest membership degree. In addition, the reliability of the classification is estimated by associating each class with the average of the membership degrees of the pixels assigned to it. The method was tested in the study area consisting of the south-western districts of the city of Naples (Italy) for the segmentation of composite indices maps determined by multiband remote sensing images. The segmentation results are consistent with the segmentations of the study area by morphological and urban characteristics, carried out by domain experts. The high computational speed of the proposed image segmentation method allows it to be applied to massive high-resolution remote sensing images.