A New Geospatial Model Integrating a Fuzzy Rule-Based System in a GIS Platform to Partition a Complex Urban System in Homogeneous Urban Contexts

Here, we present a new unsupervised method aimed at obtaining a partition of a complex urbansysteminhomogenousurbanareas,calledurbancontexts.Ourmodelintegratesspatialanalysis 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

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

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

A GIS-Based Fuzzy Multiclassification Framework Applied for Spatiotemporal Analysis of Phenomena in Urban Contexts

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

A Fuzzy Entropy-Based Thematic Classification Method Aimed at Improving the Reliability of Thematic Maps in GIS Environments

Thematic maps of spatial data are constructed by using standard thematic classification methods that do not allow management of the uncertainty of classification and, consequently, eval uation of the reliability of the resulting thematic map. We propose a novel fuzzy-based thematic classification method applied to construct thematic maps in Geographical Information Systems. An initial fuzzy partition of the domain of the features of the spatial dataset is constructed using triangular fuzzy numbers; our method finds an optimal fuzzy partition evaluating the fuzziness of the fuzzy sets by using a fuzzy entropy measure. An assessment of the reliability of the final thematic map is performed according to the fuzziness of the fuzzy sets. We implement our method on a GIS framework, testing it on various vector and image spatial datasets. The results of these tests confirm that our thematic classification method provide thematic maps with a higher reliability with respect to that obtained through fuzzy partitions constructed by expert users

A Fuzzy-Based Emotion Detection Method to Classify the Relevance of Pleasant/Unpleasant Emotions Posted by Users in Reviews of Service Facilities

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

italian High‑Speed Railway Stations and the Attractivity Index: the Downscaling Potential to Implement Coworking as Service in Station

This article introduces a methodology to evidence the current attractiveness level of Italian high-speed railway stations in a GIS environment, involving station services and fow parameters. The model has been elevant to detect stations with lower attractive capacity, and afterward, to implement the station attractivity, the work proposed employing a coworking spaces strategy as a service in station. Coworking spaces produce enefts both for the traveler and the transport company. These places became part of the services ofered within railway stations since they are fow providers able to change appearance and idea of experience at station. In France, a coworking strategy has been created from the collaboration of Regus, leader company in coworking spaces supply, and the French railway group (SNCF). The Italian railway company (Ferrovie dello Stato) does not consider the attractiveness potential of coworking in the management of station resources; coworking spaces in Italy are placed outside stations. Accordingly, Torino Porta Susa station has been identifed as one of the stations with low attractivity capacity from the methodology implemented, and it has been chosen as the case study to implement the coworking strategy. The choice of Torino Porta Susa is accurate also for showing the value of associating coworking as urban policies support. The coworking strategy can implement attractiveness levels and, in a long-term future perspective, encourage sustainable mobility target

Improving biomarker list stability by integration of biological knowledge in the learning process

BACKGROUND: The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes. RESULTS: Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy. CONCLUSIONS: The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html

Surveillance Study of Hepatitis E Virus (HEV) in Domestic and Wild Ruminants in Northwestern Italy

In industrialized countries, increasing autochthonous infections of hepatitis E virus (HEV) are caused by zoonotic transmission of genotypes (Gts) 3 and 4, mainly through consumption of contaminated raw or undercooked pork meat. Although swine and wild boar are recognized as the main reservoir for Gt3 and Gt4, accumulating evidence indicates that other animal species, including domestic and wild ruminants, may harbor HEV. Herein, we screened molecularly and serologically serum and fecal samples from two domestic and four wild ruminant species collected in Valle d'Aosta and Piemonte regions (northwestern Italy. HEV antibodies were found in sheep (21.6%), goats (11.4%), red deer (2.6%), roe deer (3.1%), and in Alpine ibex (6.3%). Molecular screening was performed using different primer sets targeting highly conserved regions of hepeviruses and HEV RNA, although at low viral loads, was detected in four fecal specimens (3.0%, 4/134) collected from two HEV seropositive sheep herds. Taken together, the data obtained document the circulation of HEV in the geographical area assessed both in wild and domestic ruminants, but with the highest seroprevalence in sheep and goats. Consistently with results from other studies conducted in southern Italy, circulation of HEV among small domestic ruminants seems to occur more frequently than expected

Seroprevalence for norovirus genogroups GII and GIV in captive non-human primates

Noroviruses (NoVs) are a major cause of epidemic gastroenteritis in children and adults. Several pieces of evidence suggest that viruses genetically and antigenically closely related to human NoVs might infect animals, raising public health concerns about potential cross-species transmission. The natural susceptibility of non-human primates (NPHs) to human NoV infections has already been reported, but a limited amount of data is currently available. In order to start filling this gap, we screened a total of 86 serum samples of seven different species of NPHs housed at the Zoological Garden (Bioparco) of Rome (Italy), collected between 2001 and 2017, using an enzyme-linked immunosorbent assay (ELISA) based on virus-like particles (VLPs) of human GII.4 and GIV.1 NoVs. Antibodies specific for both genotypes were detected with an overall prevalence of 32.6%. In detail, IgG antibodies against GII.4 NoVs were found in 18 Japanese macaques (29.0%, 18/62), a mandrill (10.0%, 1/10), a white-crowned mangabey (16.6%, 1/6) and in an orangutan (33.3%, 1/3). Twelve macaques (19.3%, 12/62), five mandrills (50.0%, 5/10), two chimpanzees (100%, 2/2) and a white-crowned mangabey (16.6%, 1/6) showed antibodies for GIV.1 NoVs. The findings of this study confirm the natural susceptibility of captive NHPs to GII NoV infections. In addition, IgG antibodies against GIV.1 were detected, suggesting that NHPs are exposed to GIV NoVs or to antigenically related NoV strains