1,721,117 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Springer Proceedings in Mathematics and Statistics
No full textRecently, the interest of many environmental agencies is on short-term air pollution predictions referred at high spatial resolution. This permits citizens and public health decision-makers to be informed with visual and easy access to air-quality assessment. We propose a hierarchical spatiotemporal model to enable use of different sources of information to provide short-term air pollution forecasting. In particular, we combine monitoring data and numerical model output in order to obtain short-term ozone forecasts over the Emilia Romagna region where the orography plays an important role on the air pollution; thus, the elevation is also included in the model. We provide high-resolution spatial forecast maps and uncertainty associated with these predictions. The assessment of the predictive performance of the model is based upon a site-one-out cross-validation experiment. © Springer International Publishing Switzerland 2014
Variations on the Author
Full text link“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
Full text linkWe 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
Spatio-temporal regression on compositional covariates: modeling vegetation in a gypsum outcrop
Full text linkInvestigating the relationship between vegetation cover and substrate typologies is important for habitat conservation. To study these relationships, common
practice in modern ecological surveys is to collect information regarding vegetation cover and substrate typology over fine regular lattices, as derived from digital ground photos. Information on substrate typologies is often available as compositional measures, e.g., the area proportion occupied by a certain substrate. Two primary issues are of interest for ecologists: first, how much substrate typologies differ in terms of relative suitability for vegetation cover and, second, whether suitability varies over time. This paper develops a procedure for managing compositional covariates within a Bayesian hierarchical framework to effectively address the aforementioned issues. A spatio-temporal model is adopted to estimate the temporal pattern characterizing substrate relative suitability for vegetation cover and, at the same time, to account for spatio-temporal correlation. Relative suitability is modeled by time-varying regression coefficients, and spatial, temporal and spatio-temporal random effects are modeled using Gaussian Markov Random Field models
Regression on compositional covariates: assessing substrate suitability for vegetation
No full textInvestigating the relationship between vegetation cover and substrate typologies is important in habitat conservation and management. We focus on a modern ecological survey, where information regarding vegetation cover are derived from digital ground photos taken at different times. The aim is to estimate the effect of different substrate typologies on vegetation cover (substrate suitability). As it is often the case in ground cover imaging, information on substrate typologies are available as compositional data, e.g., the area proportion occupied by a certain substrate. We develop a novel procedure for managing compositional covariates within a Bayesian hierarchical framework and illustrate it with data from a gypsum outcrop located in the Emilia Romagna region, Italy
Non-parametric regression on compositional covariates using Bayesian P-splines
Full text linkMethods to perform regression on compositional covariates have recently
been proposed using isometric log-ratios (ilr) representation of compositional parts.
This approach consists of first applying standard regression on ilr coordinates and
second, transforming the estimated ilr coefficients into their contrast log-ratio counterparts.
This gives easy-to-interpret parameters indicating the relative effect of each
compositional part. In this work we present an extension of this framework, where compositional
covariate effects are allowed to be smooth in the ilr domain. This is achieved
by fitting a smooth function over the multidimensional ilr space, using Bayesian Psplines.
Smoothness is achieved by assuming random walk priors on spline coefficients
in a hierarchical Bayesian framework. The proposed methodology is applied to spatial
data from an ecological survey on a gypsum outcrop located in the Emilia Romagna
Region, Italy
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