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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.

first_page settings Order Article Reprints Open AccessArticle Quantify the Contribution of Nature-Based Solutions in Reducing the Impacts of Hydro-Meteorological Hazards in the Urban Environment: A Case Study in Naples, Italy by Maria Fabrizia Clemente [ORCID] , Valeria D’Ambrosio * [ORCID] , Ferdinando Di Martino [ORCID] and Vittorio Miraglia Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Naples, Italy * Author to whom correspondence should be addressed. Land 2023, 12(3), 569; https://doi.org/10.3390/land12030569 Received: 31 January 2023 / Revised: 20 February 2023 / Accepted: 24 February 2023 / Published: 26 February 2023 (This article belongs to the Special Issue Economic Valuation of Urban Green Spaces) Download Browse Figures Versions Notes Abstract 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.

Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years.

The Multilevel Fuzzy Transform technique (MF-tr) is a hierarchical image compression method based on Fuzzy Transform, which is successfully used to compress images and manage the information loss of the reconstructed image. Unlike other lossy image compression methods, it ensures that the quality of the reconstructed image is not lower than a prefixed threshold. However, this method is not suitable for compressing massive images due to the high processing times and memory usage. In this paper, we propose a variation of MF-tr for the compression of massive images. The image is divided into tiles, each of which is individually compressed using MF-tr; thereafter, the image is reconstructed by merging the decompressed tiles. Comparative tests performed on remote sensing images show that the proposed method provides better performance than MF-tr in terms of compression rate and CPU time. Moreover, comparison tests show that our method reconstructs the image with CPU times that are at least two times less than those obtained using the MF-tr algorithm.

This research implements a GIS framework model aimed at evaluating the ecological efficiency of urban green areas. The model classifies urban green areas by identifying those that can provide ecosystem services to sustain green infrastructure at an urban district level. This model can also guide decision makers in the proper placement of the green infrastructure. The model works on the interrelation of four indicators of size, shape, vegetation structure and vegetation diversity, and it was tested in the case study of the Western Urban Districts of Naples (Italy). The selection of this study area is because it presents four urban districts that are different for physiography, urban patterns, land use, land cover and for the existing building stock. The proposed GIS-based framework can be a useful tool for planning actions and measures to protect, implement and restore existing green areas through integration into urban green infrastructure.

In this research, we propose a GIS-based framework implementing a fuzzy-based document classification method aimed at classifying urban areas by the type of criticality inherent or specific problems highlighted by citizens. The urban study area is divided into subzones; for each subzone, the reports of citizens relating to specific criticalities are analyzed and documents are created, and collected by topic and by temporal extension. The framework implements a model applied to the multiclassification of the documents in which the topic to be analyzed is divided into categories and a dictionary of terms connected to each category is built to measure the relevance of the category in the document. The framework produces, for each time frame, thematic maps of the relevance of a category in a time frame in which a subzone of the study area is classified based on the classification of the corresponding document. The framework was experimented on to analyze and monitor over time the relevance of disruptions detected by users in entities that make up urban areas, such as: roads, private buildings, public buildings and transport infrastructures, lighting networks, and public green areas. The study area is the city of Naples (Italy), partitioned in ten municipalities. The results of the tests show that the proposed framework can be a support for decision makers in analyzing the relevance of categories into which a topic is partitioned and their evolution over time.

We propose a new method based on the greatest (resp., smallest) eigen fuzzy set (GEFS, resp., SEFS) of a fuzzy relation R with respect to the max–min (resp., min–max) composition in order to implement the actions of a decisor. Using information derived from judgments of the evaluators on how much a characteristic is improved with respect to others, we construct the fuzzy relations, RMAX (resp., RMIN), where any entry RMAXijj (resp., RMINij) expresses how much the efficacy produced on the ith characteristic is equal to or greater (resp., lesser) than that one produced by the jth characteristic. The GEFS of RMAX (resp., SEFS of RMIN) are calculated in order to improve the performances of each characteristic. In the wake of previous applications based on GEFS and SEFS, we propose a method to evaluate the tourism enhancement policies in the historical center of an important Italian city. This method is new and different from those known in the literature so far. It is applied to evaluate benefits brought about by locals in order to enhance tourism in a historical center Comparison tests show that the results obtained are consistent with those expressed by the tourists interviewed

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series&rsquo, trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned into microzones, given the smallest areas in which a census of the urban system is taken in terms of resident population, type and number of buildings and properties, and industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert, which characterize the type of urban green area and the relationship with the residents and buildings, are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called urban green contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the urban green context. We test our framework on the municipality of Pozzuoli, Italy, comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the urban study area based on his knowledge of it.

A novel bit reduced fuzzy clustering method applied to segment high resolution massive images is proposed. The image is decomposed in blocks and compressed by using the fuzzy transform method, then adjoint pixels with same gray level are binned and the fuzzy c-means algorithm is applied on the bins to segment the image. This method has the advantage to be applied to massive images as the compressed image can be stored in memory and the runtime to segment the image are reduced. Comparison tests are performed with respect to the fuzzy c-means algorithm to segment high resolution images, the results shown that for not very high compression the results are comparable with the ones obtained applying to the fuzzy c-means algorithm on the source image and the runtimes are reduced by about an eighth with respect to the runtimes of fuzzy c-means.

Here, we present a new unsupervised method aimed at obtaining a partition of a complex urban system in homogenous urban areas, called urban contexts. Our model integrates spatial analysis processes and a fuzzy rule-based system applied to manage the knowledge of domain experts, it is implemented using a GIS platform. The area of study is initially partitioned in microzones, homogeneous portions of the urban system, which are the atomic reference elements for the census data. With the contribution of domain experts, we identify the physical, morphological, environmental, and socio-economic indicators needed to identify synthetic characteristics of urban contexts and create the fuzzy rule set necessary for determining the type of urban context. We implement the set of spatial analysis processes required to calculate the indicators for the microzones and apply a Mamdani fuzzy rule system to classify the microzones. Finally, the partition of the area of study in urban contexts is obtained by dissolving continuous microzones belonging to the same type of urban context. Tests are performed on the Municipality of Pozzuoli (Naples, Italy), the reliability of the out model is measured by comparing the results with the ones obtained through a detailed analysis.

We present a new method for assessing the strength of fuzzy rules with respect to a dataset, based on the measures of the greatest energy and smallest entropy of a fuzzy relation. Considering a fuzzy automaton (relation) in which A is the input fuzzy set and B the output fuzzy set, the fuzzy relation R1 with greatest energy provides information about the greatest strength of the input-output and the fuzzy relation R2 with the smallest entropy provides information about uncertainty of the relationship input-output. We consider a new index of the fuzziness of the input-output based over R1 and R2. In our method this index is calculated for each pair of input and output fuzzy sets in a fuzzy rule. A threshold value is set for choosing the most relevant fuzzy rules with respect to the data.