1,720,966 research outputs found
Mixed effect quantile and M-quantile regression for spatial data
Full text linkObserved data are frequently characterized by a spatial dependence; that is the observed values can be influenced by the "geographical" position. In such a context it is possible to assume that the values observed in a given area are similar to those recorded in neighboring areas. Such data is frequently referred to as spatial data and they are frequently met in epidemiological, environmental and social studies, for a discussion see Haining, (1990). Spatial data can be multilevel, with samples being composed of lower level units (population, buildings) nested within higher level units (census tracts, municipalities, regions) in a geographical area.
Green and Richardson (2002) proposed a general approach to modelling spatial data based on finite mixtures with spatial constraints, where the prior probabilities are modelled through a Markov Random Field (MRF) via a Potts representation (Kindermann and Snell, 1999, Strauss, 1977). This model was defined in a Bayesian context, assuming that the interaction parameter for the Potts model is fixed over the entire analyzed region. Geman and Geman (1984) have shown that this class process can be modelled by a Markov Random Field (MRF). As proved by the Hammersley-Clifford theorem, modelling the process through a MRF is equivalent to using a Gibbs distribution for the membership vector. In other words, the spatial dependence between component indicators is captured by a Gibbs distribution, using a representation similar to the Potts model discussed by Strauss (1977).
In this work, a Gibbs distribution, with a component specific intercept and a constant interaction parameter, as in Green and Richardson (2002), is proposed to model effect of neighboring areas.
This formulation allows to have a parameter specific to each component and a constant spatial dependence in the whole area, extending to quantile and m-quantile regression the proposed by Alfò et al. (2009) who suggested to have both intercept and interaction parameters depending on the mixture component, allowing for different prior probability and varying strength of spatial dependence.
We propose, in the current dissertation to adopt this prior distribution to define a Finite mixture of quantile regression model (FMQRSP) and a Finite mixture of M-quantile regression model (FMMQSP), for spatial data
Exploring complex relationships using non-parametric principal component analysis: a case study with land-use data
No full textThe present study illustrates a simplified non-parametric approach to Principal Components Analysis (PCA) with the aim to explore non-linear relationships in large data-bases. Three PCA trials were applied to a data matrix illustrating the composition of landscape (i.e. the percent distribution of several land-use classes) in a number of local analysis domains using both the standard Pearson linear correlation matrix and two non-parametric correlation matrices (Spearman and Kendall correlation coefficients). Using standard PCA diagnostics, results indicate that the analysis carried out on non-parametric Spearman correlation matrix shows the highest performance in terms of both variance extracted by each principal component and factor loadings. Non-parametric approaches appear as promising tools in the analysis of large data-sets characterized by complex, non-linear relationships between variables
A multivariate assessment of fringe landscape dynamics in Rome, Italy, and implications for peri-urban forest conservation
No full textThe present study assesses how urban growth impacted landscape composition, structure and diversity in peri-urban Rome, central Italy, during the last 60 years (1949–2008). The spatial distribution and fragmentation of nine land-use classes derived from comparable digital maps covering the whole study area (1500 km2) were assessed by computing a total of 27 metrics using a relational approach based on exploratory data analysis. Landscape transformations were explored through hierarchical clustering applied on the selected landscape metrics. Our results indicate the increased fragmentation of peri-urban landscape over the study interval. Especially vineyards, arable land and pastures underwent patch fragmentation. This process was reflected into smaller ‘core’ areas compared with the remaining non-urban uses of land (woodland, olive groves). A negative relation between class area and patchiness was observed for all classes with the exception of forests and olive groves. Policies aimed at contrasting fragmentation and simplification of the relict landscape on the fringe of large cities are finally discussed
Exploring forest ‘fringescapes’: urban growth, society and swimming pools as a sprawl landmark in coastal Rome
No full textUrban expansion; Earth and Planetary Sciences (all); Agricultural and Biological Sciences (all); 230
The contribution of non-parametric multivariate Statistics in land-use assesment
No full textIn order to overcome the increasing complexity of large data matrices explored through multivariate and data mining approaches, the standard PCA, originally proposed by Karl Pearson in 1901, was generalized using non-linear, multi-linear, higher-order, ro- bust and weighted approaches, among others. The present study contributes to this issue introducing a simpli ed non-parametric approach with the aim to explore non-lin- ear relationships among variables and to improve the performances of standard PCA. Three different PCA were applied to a matrix illustrating the composition of landscape (i.e. the percent distribution of several land-use classes) in a number of local analysis domains using both the standard Pearson linear correlation matrix and two non-para- metric correlation matrices (Spearman and Kendall correlation coef cients)
Land-cover changes and sustainable development in a rural cultural landscape of central Italy: classical trends and counter-intuitive results
No full textDefining and understanding the long-term social and ecological evolution of rural cultural landscapes can provide insights into complex dynamics of landscape and environment changes. Land cover changes (LCCs) in Mediterranean-type ecosystems are mainly due to human-induced landscape transformations. Multi-scale spatial analysis can provide useful information in the interpretation of LCCs data and contribute to identifying underlying drivers of landscape change. In the present study, we analyze eight diachronic land cover maps and perform statistical data assessments of human pressure in the Tolfa–Cerite district (Northern Latium, central Italy) to investigate potential changes in the cultural landscape. The Tolfa– Cerite district is a generally dry area with subhumid–humid sites and an interesting mosaic of Mediterranean-temperate vegetation, agricultural and pastoral land, and a millenarian human presence. LCCs were assessed over a period of 57 years (1949–2006) using maps at both low- resolution (1:100.000) and high-resolution (1:25.000) with different class nomenclature sys- tems. Three primary land cover changes have been observed during the investigated period: (i) urbanization, (ii) land abandonment, and (iii) deforestation. While the former two classes of landscape change are particularly common in the northern Mediterranean region, forest conversion to pastures and shrub lands due to intensive grazing, fires, climate aridity, and increasing human pressure is, nowadays, rarely observed in Italy. Better understanding the influence of population dynamics at the local scale and other drivers of LCCs can help fine- tuning conservation policies looking at landscape quality, diversity, and fragmentation
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
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
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