1,721,021 research outputs found
Understanding the expansion of Italian metropolitan areas: A study based on entropy measures
Get PDFThis work presents a study on the urban configuration of a number of Italian metropolitan areas and their development over time, with the aim of evaluating the size and shape of urban areas expansion. Raster data are used, produced by the European Environmental Agency within the COoRdination of INformation on the Environment land cover project. The study is based on a version of spatial entropy measures proposed and validated by a recent series of papers, aimed at the evaluation of spatial data heterogeneity; the methods assess the efficiency of the spatial configuration of urban areas. An innovative combination of two entropy measures is the tool for evaluating the urban development in Italy. Results allow both conclusive comments about each metropolitan area and comparisons across areas over space and time
Spatial Entropy Measures
No full textThe article reviews, under a unified framework, the main approaches to spatial entropy measures. It also illustrates the recent proposal of a set of entropy measures for spatial data, which allows to split the data heterogeneity, usually assessed via Shannon's entropy, into two components: spatial mutual information, identifying the role of space, and spatial residual entropy, measuring heterogeneity due to other sources. Some practical aspects are also covered by presenting the available software for the computation of the considered measures
Model-based entropy estimation for data with covariates and dependence structures
No full textEntropy is widely used in ecological and environmental studies, where data often present complex interactions. Difficulties arise in linking entropy to available covariates or data dependence structures, thus all existing entropy estimators assume independence. To overcome this limit, we take a Bayesian model-based approach which focuses on estimating the probabilities that compose the index, accounting for any data dependence and correlation. An estimate of entropy can be constructed from the model fitted values, returning an observation-specific measure of entropy, rather than an overall index. This way, the latent heterogeneity of the system can be represented by a curve in time or a surface in space, according to the characteristics of the survey study at hand. An empirical study illustrates the flexibility and interpretability of our results over temporally and spatially correlated data. An application is presented about the biodiversity of spatially structured rainforest tree data
Continuous time-interaction processes for population size estimation, with an application to drug dealing in Italy
Get PDFWe introduce a time-interaction point process where the occurrence of an event can increase (self-excitement) or reduce (self-correction) the probability of future events. Self-excitement and self-correction are allowed to be triggered by the same event, at different timescales; other effects such as those of covariates, unobserved heterogeneity, and temporal dependence are also allowed in the model. We focus on capture-recapture data, as our work is motivated by an original example about the estimation of the total number of drug dealers in Italy. To do so, we derive a conditional likelihood formulation where only subjects with at least one capture are involved in the inference process. The result is a novel and flexible continuous-time population size estimator. A simulation study and the analysis of our motivating example illustrate the validity of our approach in several scenarios
Estimation of entropy measures for categorical variables with spatial correlation
No full textEntropy is a measure of heterogeneity widely used in applied sciences, often when data are collected over space. Recently, a number of approaches has been proposed to include spatial information in entropy. The aim of entropy is to synthesize the observed data in a single, interpretable number. In other studies the objective is, instead, to use data for entropy estimation; several proposals can be found in the literature, which basically are corrections of the estimator
based on substituting the involved probabilities with proportions. In this case, independence is assumed and spatial correlation is not considered. We propose a path for spatial entropy estimation: instead of intervening on the global entropy estimator, we focus on improving the estimation of its components, i.e. the probabilities, in order to account for spatial effects. Once probabilities are suitably evaluated, estimating entropy is straightforward since it is a deterministic
function of the distribution. Following a Bayesian approach, we derive the posterior probabilities of a binomial distribution for categorical variables, accounting for spatial correlation. A posterior distribution for entropy can be obtained, which may be synthesized as wished and displayed as an entropy surface for the area under study
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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