1,720,995 research outputs found
Detecting irregular shaped clusters via scan statistics
No full textThe topic of this paper regards recent extensions of spatial scan statistics, widely used in public
health research to test disease clusters and to identify their approximate locations. Despite its success,
there is an important limitation associated with the traditional scan statistics: it depends on
the use of circle shaped windows. As results, the identified regions are often not well localized. This
limitation has motivated research aimed at developing new approaches which have the capability to
detect clusters of irregular shapes. Two new techniques have been studied and compared: the spatial
scan statistics, based on the graph theory, and the flexible scan statistics which imposes an irregularly
shaped window. A computational study has been carried out to evaluate the effectiveness of
these new approaches. A better understanding of the relative strengths and weakness of these two
methods is essential to appropriate choices of methodology
A Spatial Composite Indicator for Human and Ecosystem Well-Being in the Italian Urban Areas
No full textThe concept of well-being has evolved over the last decades and a multidisciplinary literature has acknowledged the multidimensional nature of this phenomenon that encompasses different key dimensions. To give concise measure of well-being, methodologies based on composite indices assume relevance, for their capability to summarize the multidimensional issues, rank the units and provide interesting analysis tools. This paper intends to make a contribution to the efforts of assessing human and ecosystem well-being in the Italian urban areas, by appreciating the spatial dimension of the elementary indicators involved in the building process of the composite indicator. To this end, we derive a set of local weights reflecting the spatial variability of data through the Geographically Weighted PCA. Then, the analysis proceeds by employing a unitary-input DEA model, also known as Benefit of Doubt approach, as a benchmarking tool for constructing a spatial composite indicator to evaluate the well-being in the Italian urban areas. In such way, we can take local peculiarities into account and identify the best performing cities to follow as examples of good administrative practices for promoting urban well-being. The approach followed in this specific study is applied empirically with data from the Urban ‘Equitable and Sustainable Well-Being” (Ur-Bes) project, proposed by ISTAT
Capitolo 12. I Modelli dell'item Response Theory e il Loro Impiego per Misurare la Qualità dei Servizi Sociali
No full textA quantitative valuation of tourist experience in Lisbon
No full textThe increasing value of tourist satisfaction for tourism promotion has led to a substantial increase in research into the process of measuring the satisfaction of tourists, and various approaches and theories have been developed. This paper proposes an Item Response Theory (IRT) approach to ensure the measurements of perceptions and satisfaction of tourists. Data were collected by means of a questionnaire administered to tourists who had visited Lisbon. The formulation of the IRT models allowed us to determine the influence of some demographic and travel behaviour characteristics on a number of given destination attributes. We also specified georeferenced IRT models to attain geographically differentiated measures of tourist satisfaction. The main findings from the models are compared and discussed
A Functional approach for constructing dynamic Composite Indicators
Full text linkThis paper contributes to the research on the development of comparable composite indicators by introducing a Functional Weighted Malmquist Productive Index that allows for comparative trend analysis. In analogy with entropy-based weighted methods, this novel dynamic indicator is derived by measuring the degree of diversification of the single method through a family of diversity indices. The paper has the merit of proposing a new dynamic composite indicator that supplements the analysis with Functional Data Analysis (FDA) tools that provide us with useful information about the order and dynamics of the composite index trajectories. The simulation study set up in this paper raises doubts about the robustness of the entropy-based weighted methods while the application of the new index to well-being dataset highlights its practical appeal
Quantile regression and Bayesian cluster detection to identify radon prone areas
No full textAlbeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys
Geographies of Twitter debates
Full text linkOver the last years, the prodigious success of online social media sites has marked
a shift in the way people connect and share information. Coincident with this trend
is the proliferation of location-aware devices and the consequent emergence of usergenerated geospatial data. From a social scientifc perspective, these location data
are of incredible value as it can be mined to provide researchers with useful information about activities and opinions across time and space. However, the utilization
of geo-located data is a challenging task, both in terms of data management and in
terms of knowledge production, which requires a holistic approach. In this paper, we
implement an integrated knowledge discovery in cyberspace framework for retrieving, processing and interpreting Twitter geolocated data for the discovery and classifcation of the latent opinion in user-generated debates on the internet. Text mining
techniques, supervised machine learning algorithms and a cluster spatial detection
technique are the building blocks of our research framework. As real-word example, we focus on Twitter conversations about Brexit, posted on Uk during the 13
months before the Brexit day. The experimental results, based on various analysis
of Brexit-related tweets, demonstrate that diferent spatial patterns can be identifed, clearly distinguishing pro- and anti-Brexit enclaves and delineating interesting
Brexit geographie
Emerging topics in Brexit debate on Twitter around the deadlines a probabilistic topic modelling approach
Full text linkThe present study is focused on the online debate relating to the Brexit process, three years and half since the historical referendum that has sanctioned the divide of the United Kingdom from the European Union. In our analysis we consider a corpus of approximately 33 million Brexit related tweets, shared on Twitter for 58 weeks, spanning from 31 December 2019 to 9 February 2020. Due to its great accessibility to data, Twitter constitutes a convenient data source to monitor and evaluate a wide variety of topics. In addition, Twitter’s marked orientation towards news and the dissemination of information makes this microblogging network more connected to politics compared to other platforms. Through static and dynamic topic modelling techniques, we were able to identify the topics that have attracted the most attention from Twitters users and to characterise their temporal evolution. The topics retrieved by the static model highlight the major events of the Brexit process while the dynamic analysis recovered the persistent themes of discussion and debate over the entire period
Thirty years of research into hate speech: topics of interest and their evolution
No full textThe exponential growth of social media has brought with it an increasing propagation of hate speech and hate based propaganda. Hate speech is commonly defined as any communication that disparages a person or a group on the basis of some characteristics such as race, colour, ethnicity, gender, sexual orientation, nationality, religion. Online hate diffusion has now developed into a serious problem and this has led to a number of international initiatives being proposed, aimed at qualifying the problem and developing effective counter-measures. The aim of this paper is to analyse the knowledge structure of hate speech literature and the evolution of related topics. We apply co-word analysis methods to identify different topics treated in the field. The analysed database was downloaded from Scopus, focusing on a number of publications during the last thirty years. Topic and network analyses of literature showed that the main research topics can be divided into three areas: “general debate hate speech versus freedom of expression”,“hate-speech automatic detection and classification by machine-learning strategies”, and “gendered hate speech and cyberbullying”. The understanding of how research fronts interact led to stress the relevance of machine learning approaches to correctly assess hatred forms of online speech
Functional data analysis approach for identifying redundancy in air quality monitoring stations amid Covid-19”.
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