Estimating and modeling spatio-temporal complex-valued covariance functions

<p><span><span>Oceanographic data belong to the wide class of vectorial data, for which the decomposition in modulus and direction is meaningful, and the vectorial components are characterized by homogeneous quantities, with the same unit of measurement. Another feature of oceanographic data is that they exhibit spatio-temporal dependence.<br>In Geostatistics, such data can be properly modelled by recalling the theory of complex-valued random fields. However, in the literature, only techniques for modeling and predicting the spatial evolution of these phenomena were proposed; while the temporal dependence was analyzed separately from the spatial one, or just time-varying complex covariance models were used. Thus, the novelty of this paper regards some advances of the complex formalism for analyzing complex data in space-time and new classes of spatio-temporal complex covariance models.<br>A case study on spatio-temporal complex estimating and modeling with oceanographic data is provided and a comparison between two classes of complex covariance models is also proposed.</span></span></p>

Fuzzy Clustering Method for Spherical Data Based on q-Divergence

This study presents a fuzzy clustering algorithm for classifying spherical data based on q-divergence. First, it is shown that a conventional method for vectorial data is equivalent to the regularization of another conventional method using q-divergence. Next, based on the knowledge that q-divergence is a generalization of Kullback-Leibler (KL)-divergence and that there is a conventional fuzzy clustering method for classifying spherical data based on KL-divergence, a fuzzy clustering algorithm for spherical data is derived based on q-divergence. This algorithm uses an optimization problem built by extending KL-divergence in the conventional method to q-divergence. Finally, some numerical experiments are conducted to verify the proposed methods.

Fuzzy Clustering Methods for Categorical Multivariate Data Based on q-Divergence

This paper presents two fuzzy clustering algorithms for categorical multivariate data based on q-divergence. First, this study shows that a conventional method for vectorial data can be explained as regularizing another conventional method using q-divergence. Second, based on the known results that Kullback-Leibler (KL)-divergence is generalized into the q-divergence, and two conventional fuzzy clustering methods for categorical multivariate data adopt KL-divergence, two fuzzy clustering algorithms for categorical multivariate data that are based on q-divergence are derived from two optimization problems built by extending the KL-divergence in these conventional methods to the q-divergence. Through numerical experiments using real datasets, the proposed methods outperform the conventional methods in term of clustering accuracy.

Visualization on the Web of 20 Years of Crop Rotation and Wildlife Co-Evolutions

The accumulation of evidences of the effects of intensive agricultural practices against wildlife fauna and flora, and biodiversity in general, has been largely published in scientific papers (Tildman, 1999). However, data serving as sup-port to their conclusions are often kept hidden behind research institutions. This paper presents a data visualization sys-tem opened on the Web allowing citizens to get a comprehensive access to data issued from such kind of research institution, collected for more than 20 years. The Web Information System has been thought in order to ease the comparison of data issues from various databases describing the same object, the agricultural landscape, at different scales and through different observation devices. An interactive visualization is proposed in order to check co-evolution of fauna and flora together with agricultural practices. It mixes aerial orthoimagery produced since 1950 with vectorial data showing the evolutions of agricultural parcels with those of a few sentinel species such as the Montagu's harrier. This is made through a composition of maps, charts and time lines, and specific tools for comparison. A particular concern is given to the observation effort bias in order to show meaningful statistical aggregates.

q-Divergence-Based Relational Fuzzy c-Means Clustering

In this paper, a clustering algorithm for relational data based on q-divergence between memberships and variables that control cluster sizes is proposed. A conventional method for vectorial data is first presented for interpretation as the regularization of another conventional method with q-divergence. With this interpretation, a clustering algorithm for relational data, based on q-divergence, is then derived from an optimization problem built by regularizing the conventional method with q-divergence. A theoretical discussion reveals the property of the proposed method. Numerical results are presented that substantiate this property and show that the proposed method outperforms two conventional methods in terms of accuracy.

Modelización geoespacial del escurrimiento superficial en cuencas de las sierras del sudoeste, Tucumán, Argentina

La aplicación del modelo hidrológico L-THIA ©, con apoyo en la metodología del número de la curva (CN) del Servicio de Conservación de Suelos de Estados Unidos, es empleado para transformar la precipitación total en precipitación efectiva, constituyéndose en una herramienta de gran valor para realizar estudios hidrológicos en cuencas hidrográficas, en las que no se cuenta con registros lo suficientemente extensos y confiables. Esta metodología requiere del conocimiento del tipo y uso de suelo de la cuenca en estudio, así como de registros de precipitación, en estaciones cercanas a ella. El presente estudio se aplica en la cuenca hidrográfica de los ríos Singuil y Chavarria, Tucumán, Argentina. Se cuantificó el uso y cobertura del suelo a partir del procesamiento de imágenes Landsat TM, identificando los cambios de uso y cobertura del suelo para el período 1986-2010. El procesamiento digital de la base de datos vectorial consistió en la rasterización automática con herramientas de sistema de información geográfica. Se obtuvo el valor de CN y se cuantificó la lámina de escurrimiento. La disminución de la cobertura de pastizal y su reemplazo por bosque nativo, incrementa la tasa de infiltración reduciendo el escurrimiento superficial. AbstractThe application of the L-THIA ©, hydrologic model, supported on the curve number methodology (CN), SCS-USA, it is used to transform total precipitation into effective precipitation. This becomes a useful tool for hydrologic studies in basins lacking extended and truthful registers. This methodology requires: type of soil data, land use land cover map, and precipitation data. The study area was Singuil and Chavarria basins, Tucumán, Argentina. We analyzed land use and cover change during 1986-2010 using Landsat TM images. Vectorial data base was rasterized using Geographic Information System. CN value and run off level were obtained.The decrease of grassland cover and its replacement by native forest increases the rate of infiltration reducing the surface runoff in the analyzed basins.

DATA STRUCTURE FOR PROGRESSIVE VISUALISATION AND EDITION OF VECTORIAL GEOSPATIAL DATA

3D mock-ups of cities are becoming an increasingly common tool for urban planning. Sharing the mock-up is still a challenge since the volume of data is so high. Furthermore, the recent surge in low-end, mobile devices requires developers to carefully control the amount of data they process. In this paper, we present a hierarchical data structure that allows the streaming of vectorial data. Loosely based on a quadtree, the structure stores the data in tiles and is organised following a weight function which allows the most relevant data to be displayed first. The relevance of a feature can be measured by its geometry and semantic attributes, and can vary depending on the application or client type. Tiles can be limited in size (number of features or triangles) for the client to be able to control resource consumption. The article also presents algorithms for the addition or removal of features in the data structure, opening the path for the interactive edition of city data stored in a database.

Bezdek-Type Fuzzified Co-Clustering Algorithm

In this study, two co-clustering algorithms based on Bezdek-type fuzzification of fuzzy clustering are proposed for categorical multivariate data. The two proposed algorithms are motivated by the fact that there are only two fuzzy co-clustering methods currently available – entropy regularization and quadratic regularization – whereas there are three fuzzy clustering methods for vectorial data: entropy regularization, quadratic regularization, and Bezdek-type fuzzification. The first proposed algorithm forms the basis of the second algorithm. The first algorithm is a variant of a spherical clustering method, with the kernelization of a maximizing model of Bezdek-type fuzzy clustering with multi-medoids. By interpreting the first algorithm in this way, the second algorithm, a spectral clustering approach, is obtained. Numerical examples demonstrate that the proposed algorithms can produce satisfactory results when suitable parameter values are selected.