Spatial structure effects in spatial interaction model: a Geographically Weighted Regression (GWR) approach

EnThe development of local forms of spatial analysis has been the subject of intense research over last decade. In this paper we propose a local calibration procedure for handling varying parameter estimates of an origin-constrained spatial interaction model. In this context, the estimates of local parameters depends both on origins and destinations and a four dimensional space is involved. A suitable estimation of local parameters can be obtained by the maximisation of a weighted maximum likelihood function, exploiting the same principle of geographical weighted regression (GWR) approach

Chapter Assessment of visitors’ perceptions in protected areas through a model-based clustering

Protected areas are well-defined geographical spaces that, in view of their recognized, natural, ecological or cultural values, receive protection. They have the twofold mandate of protection of natural resources and providing a space for nature-based tourism activities. In the last years, the nature-based tourism is experiencing positive and sustainable growth worldwide. Understanding the value attached by visitors to their destination and know their assessment on various activities in which they are engaged during their stay is a key element in shaping tourist’s satisfaction. Objective of this research was to identify the profiles of visitors to tourist destinations within Natural Park of Majella (Abruzzo region, Italy) and to assess the link with their satisfaction. The data for this study were collected by means of a structured questionnaire administrated to tourists who visited the sites of the protected area during the last three summer months. A total of 150 valid questionnaires were obtained and form the base of the data analysis. Through a Bayesian model-based clustering, better known as Bayesian Profile Regression, we partition visitors into clusters, characterized by similar profiles in terms of their demographic characteristics (age, gender, education attainment), as well as, in terms of the features of their travel behaviour (accommodation, length of stay, past visitation experience). A further benefit of the followed approach lies in the ability of that Bayesian technique of simultaneously estimating the contribute of all covariates to the outcome of interest. In our context, we explore the association of detected groups with the tourists’ satisfaction. In the survey, the global quality of tourism service is segmented into single features and respondents were asked to give their level of appreciation on a five-point Likert satisfaction scale. To estimate the latent trait measured by the items and related to the overall satisfaction we followed an IRT modelling

Thirty years of research into hate speech: topics of interest and their evolution

AbstractThe 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

Spatial structure effects in spatial interaction model: a Geographically Weighted Regression (GWR) approach

EnThe development of local forms of spatial analysis has been the subject of intense research over last decade. In this paper we propose a local calibration procedure for handling varying parameter estimates of an origin-constrained spatial interaction model. In this context, the estimates of local parameters depends both on origins and destinations and a four dimensional space is involved. A suitable estimation of local parameters can be obtained by the maximisation of a weighted maximum likelihood function, exploiting the same principle of geographical weighted regression (GWR) approach

Position estimation using the Digital Audio Broadcast (DAB) signal

Over the past decades, there have been a number of trends that have driven the desire to improve the ability to navigate in all environments. While the Global Positioning System has been the driving factor behind most of these trends, there are limitations to this system that have become more evident over time as the world has increasingly come to rely on navigation. These limitations are mostly due to the low transmission power of the satellites, where navigation signals broadcast from space are comparatively weak, especially by the time they have travelled to receivers on the ground. This makes the signals particularly vulnerable to fading in difficult environments such as "urban jungles" and other built up areas. The low signal-to-noise ratio (SNR) also means, that the signals are susceptible to jamming, both hostile and accidental. This motivates the need for alternatives technologies to satellite navigation and consider terrestrial based alternatives such as LORAN-C and eLORAN, but there is also significant interest in the exploitation of other non-navigation signals for positioning and navigation purposes. These so-called 'Signals of Opportunity' do not generally require any alterations to existing communications transmission infrastructure and utilise alternative multi-carrier modulation techniques to those used by navigation systems. This project examines the use of such a signal, the Digital Audio Broadcast (DAB) signal, as a positioning source. This thesis contains complete research from initial coverage simulations in the UK, through to extensive static testing, and the use of the signal in a dynamic environment and it has been shown that the Digital Audio Broadcast signal has potential as a terrestrial based positioning signal.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Morocco, members of Royal Guard at royal review of troops in Fez

ColorVolume 108, Page

Chapter Assessment of visitors’ perceptions in protected areas through a model-based clustering

Protected areas are well-defined geographical spaces that, in view of their recognized, natural, ecological or cultural values, receive protection. They have the twofold mandate of protection of natural resources and providing a space for nature-based tourism activities. In the last years, the nature-based tourism is experiencing positive and sustainable growth worldwide. Understanding the value attached by visitors to their destination and know their assessment on various activities in which they are engaged during their stay is a key element in shaping tourist’s satisfaction. Objective of this research was to identify the profiles of visitors to tourist destinations within Natural Park of Majella (Abruzzo region, Italy) and to assess the link with their satisfaction. The data for this study were collected by means of a structured questionnaire administrated to tourists who visited the sites of the protected area during the last three summer months. A total of 150 valid questionnaires were obtained and form the base of the data analysis. Through a Bayesian model-based clustering, better known as Bayesian Profile Regression, we partition visitors into clusters, characterized by similar profiles in terms of their demographic characteristics (age, gender, education attainment), as well as, in terms of the features of their travel behaviour (accommodation, length of stay, past visitation experience). A further benefit of the followed approach lies in the ability of that Bayesian technique of simultaneously estimating the contribute of all covariates to the outcome of interest. In our context, we explore the association of detected groups with the tourists’ satisfaction. In the survey, the global quality of tourism service is segmented into single features and respondents were asked to give their level of appreciation on a five-point Likert satisfaction scale. To estimate the latent trait measured by the items and related to the overall satisfaction we followed an IRT modelling

Regression Analysis in a Data Rich Environment

This paper discusses a number of conceptual issues pertaining to the study of the relationships existing between two groups of dependent and regressor variables which are supposed to be spatially and temporally correlated. Since it is assumed that this relationships can be studied in a reduced latent space, we propose a factor analytic approach and provide an overview of the motivations for including spatial effects in dynamic factor models. Considerable attention is paid to the inferential framework necessary to carry out estimation and to the different assumptions, constraints and implications embedded in the various model specifications. The discussion combines insights from the traditional (spatial) econometrics literature as well as from geostatistics and image analysis