919 resultados para exploratory spatial data analysis


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La formación en Psicología en Brasil pasa por transformaciones, debido a la expansión del sector de enseñanza superior. Por un lado, se observa un crecimiento de cursos de psicología en las regiones del interior del país, por otro, se observa la incorporación cada vez más frecuente de instituciones de enseñanza por parte de grupos extranjeros. Se pretende con este estudio mostrar las etapas de interiorización e internacionalización de los cursos de psicología, enfocando los desafíos para la formación del psicólogo brasileño. Se trata de un estudio descriptivo exploratorio con base en datos oficiales sobre la enseñanza superior en el Brasil. Creemos que este es un contexto que presenta nuevos escenarios y desafíos para la formación de los psicólogos, pues reposiciona a la profesión en nuestro país, vinculándola no apenas a las capitales y grandes centros urbanos, sino también exigiendo que los cursos, inclusive los internacionalizados, pasen a considerar en sus currículos la heterogeneidad espacial, social y simbólica, a veces conflictiva y ambigua, que atraviesa la realidad brasileña.

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A wireless sensor network (WSN) is a group of sensors linked by wireless medium to perform distributed sensing tasks. WSNs have attracted a wide interest from academia and industry alike due to their diversity of applications, including home automation, smart environment, and emergency services, in various buildings. The primary goal of a WSN is to collect data sensed by sensors. These data are characteristic of being heavily noisy, exhibiting temporal and spatial correlation. In order to extract useful information from such data, as this paper will demonstrate, people need to utilise various techniques to analyse the data. Data mining is a process in which a wide spectrum of data analysis methods is used. It is applied in the paper to analyse data collected from WSNs monitoring an indoor environment in a building. A case study is given to demonstrate how data mining can be used to optimise the use of the office space in a building.

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The organization of non-crystalline polymeric materials at a local level, namely on a spatial scale between a few and 100 a, is still unclear in many respects. The determination of the local structure in terms of the configuration and conformation of the polymer chain and of the packing characteristics of the chain in the bulk material represents a challenging problem. Data from wide-angle diffraction experiments are very difficult to interpret due to the very large amount of information that they carry, that is the large number of correlations present in the diffraction patterns.We describe new approaches that permit a detailed analysis of the complex neutron diffraction patterns characterizing polymer melts and glasses. The coupling of different computer modelling strategies with neutron scattering data over a wide Q range allows the extraction of detailed quantitative information on the structural arrangements of the materials of interest. Proceeding from modelling routes as diverse as force field calculations, single-chain modelling and reverse Monte Carlo, we show the successes and pitfalls of each approach in describing model systems, which illustrate the need to attack the data analysis problem simultaneously from several fronts.

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Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

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Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies, it is possible to obtain data cubes in which one combines both techniques simultaneously, producing images with spectral resolution. To extract information from them can be quite complex, and hence the development of new methods of data analysis is desirable. We present a method of analysis of data cube (data from single field observations, containing two spatial and one spectral dimension) that uses Principal Component Analysis (PCA) to express the data in the form of reduced dimensionality, facilitating efficient information extraction from very large data sets. PCA transforms the system of correlated coordinates into a system of uncorrelated coordinates ordered by principal components of decreasing variance. The new coordinates are referred to as eigenvectors, and the projections of the data on to these coordinates produce images we will call tomograms. The association of the tomograms (images) to eigenvectors (spectra) is important for the interpretation of both. The eigenvectors are mutually orthogonal, and this information is fundamental for their handling and interpretation. When the data cube shows objects that present uncorrelated physical phenomena, the eigenvector`s orthogonality may be instrumental in separating and identifying them. By handling eigenvectors and tomograms, one can enhance features, extract noise, compress data, extract spectra, etc. We applied the method, for illustration purpose only, to the central region of the low ionization nuclear emission region (LINER) galaxy NGC 4736, and demonstrate that it has a type 1 active nucleus, not known before. Furthermore, we show that it is displaced from the centre of its stellar bulge.

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Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework`s applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

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In this paper a new parametric method to deal with discrepant experimental results is developed. The method is based on the fit of a probability density function to the data. This paper also compares the characteristics of different methods used to deduce recommended values and uncertainties from a discrepant set of experimental data. The methods are applied to the (137)Cs and (90)Sr published half-lives and special emphasis is given to the deduced confidence intervals. The obtained results are analyzed considering two fundamental properties expected from an experimental result: the probability content of confidence intervals and the statistical consistency between different recommended values. The recommended values and uncertainties for the (137)Cs and (90)Sr half-lives are 10,984 (24) days and 10,523 (70) days, respectively. (C) 2009 Elsevier B.V. All rights reserved.

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The study of complex systems has become a prestigious area of science, although relatively young . Its importance was demonstrated by the diversity of applications that several studies have already provided to various fields such as biology , economics and Climatology . In physics , the approach of complex systems is creating paradigms that influence markedly the new methods , bringing to Statistical Physics problems macroscopic level no longer restricted to classical studies such as those of thermodynamics . The present work aims to make a comparison and verification of statistical data on clusters of profiles Sonic ( DT ) , Gamma Ray ( GR ) , induction ( ILD ) , neutron ( NPHI ) and density ( RHOB ) to be physical measured quantities during exploratory drilling of fundamental importance to locate , identify and characterize oil reservoirs . Software were used : Statistica , Matlab R2006a , Origin 6.1 and Fortran for comparison and verification of the data profiles of oil wells ceded the field Namorado School by ANP ( National Petroleum Agency ) . It was possible to demonstrate the importance of the DFA method and that it proved quite satisfactory in that work, coming to the conclusion that the data H ( Hurst exponent ) produce spatial data with greater congestion . Therefore , we find that it is possible to find spatial pattern using the Hurst coefficient . The profiles of 56 wells have confirmed the existence of spatial patterns of Hurst exponents , ie parameter B. The profile does not directly assessed catalogs verification of geological lithology , but reveals a non-random spatial distribution

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This work aims to study the problem of the formal job in the Brazilian Northeast region and its effect in the social inclusion, taking for base the analysis of variables defined in the Atlas of Social Exclusion, which is based on the 2000 Brazilian Census, choosing the county as unit of analysis. As methodological options, an exploratory data analysis was performed, followed by multivariate statistical techniques, such as weighted multiple regression analysis, cluster analysis and exploratory analysis of spatial data. The results pointed out to low rates of formal job for the active age population as well as low indexes of social inclusion in the Northeast region of Brazil. A strong association of the formal job with the indicators of social inclusion under investigation, was evidenced (schooling, inequality, poverty, youth and income form government transfers), as well as a strong association of the formal job with the new index of social inclusion (IIS), modified from the IES. At the Federative Units, in which better levels of formal job had been found, good indexes of social inclusion are also observed. Highlights for the state of the Rio Grande do Norte, with the best conditions of life, and for the states of the Maranhão and Piauí, with the worst conditions. The situation of the Northeast region, facing the indicators under study, is very precarious, claiming for the necessity of emphasizing programs and governmental actions, specially directed to the raise of formal job levels of the region, reflecting, thus, in improvements on the income inequality, as well as in the social inclusion of the population of Northeastern natives.

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OBJETIVO: Avaliar a prevalência de tracoma em escolares de Botucatu/SP-Brasil e a distribuição espacial dos casos. MÉTODOS: Foi realizado um estudo transversal, em crianças de 7-14 anos, que frequentavam as escolas do ensino fundamental de Botucatu/SP, em novembro/2005. O tamanho da amostra foi estimado em 2.092 crianças, considerando-se a prevalência histórica de 11,2%, aceitando-se erro de estimação de 10% e nível de confiança de 95%. A amostra foi probabilística, ponderada e acrescida de 20%, devido à possível ocorrência de perdas. Examinaram-se 2.692 crianças. O diagnóstico foi clínico, baseado na normatização da Organização Mundial da Saúde (OMS). Para avaliação dos dados espaciais, utilizou-se o programa CartaLinx (v1.2), sendo os setores de demanda escolar digitalizados de acordo com as divisões do planejamento da Secretaria de Educação. Os dados foram analisados estatisticamente, sendo a análise da estrutura espacial dos eventos calculadas usando o programa Geoda. RESULTADOS: A prevalência de tracoma nos escolares de Botucatu foi de 2,9%, tendo sido detectados casos de tracoma folicular. A análise exploratória espacial não permitiu rejeitar a hipótese nula de aleatoriedade (I= -0,45, p>0,05), não havendo setores de demanda significativos. A análise feita para os polígonos de Thiessen também mostrou que o padrão global foi aleatório (I= -0,07; p=0,49). Entretanto, os indicadores locais apontaram um agrupamento do tipo baixo-baixo para um polígono ao norte da área urbana. CONCLUSÃO: A prevalência de tracoma em escolares de Botucatu foi de 2,9%. A análise da distribuição espacial não revelou áreas de maior aglomeração de casos. Embora o padrão global da doença não reproduza as condições socioeconômicas da população, a prevalência mais baixa do tracoma foi encontrada em setores de menor vulnerabilidade social.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The objective of this study was to analyze, using the geoestatistic and a system of classification fuzzy, the fertility of an experimental area with base in chemical attributes of the soil and its relationship with the productivity of the conilon coffee. The study was accomplished in the experimental farm of the INCAPER - ES. The soil samples were collected in the depth of 0 - 0.2 m, being analyzed the attributes: matches, potassium, calcium and magnesium, aluminum, sum of bases, cation exchange capacity (pH 7), and saturation percentage. The data were submitted to a descriptive, exploratory, and geostatistical analysis. A system of fuzzy classification was applied using the attributes described to infer about the fertility of the soil and its relationship with the productivity of the culture. The fertility possibility presented positive spatial relationship with the productivity of the culture, with higher values of this where the possibility of fertile soil is superior.

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We report a renormalized zero-range interaction approach to estimate the size of generic weakly bound three-body systems where two particles are identical. We present results for the neutron-neutron root-mean-square distances of the halo nuclei 6He, 11Li, 14Be and 20C, where the systems are taken as two halo neutrons with an inert point-like core. We also report an approach to obtain the neutron-neutron correlation function in halo nuclei. In this case, our results suggest a review of the corresponding experimental data analysis. © 2007 American Institute of Physics.

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Como a malária ainda se mantém como um grave problema de saúde pública e continua afetando grupos da população que vive na região amazônica, principalmente os que moram em áreas de assentamentos e de exploração de garimpos é que decidiu-se desenvolver estudos voltados para a expansão da endemia e sua vinculação com o processo migratório. Outras questões, como a atividade ocupacional, moradia, e a situação da malária importada pela fronteira internacional, também foram consideradas. O universo alvo do estudo está no estado do Amapá, mais especificamente nos municípios de Ferreira Gomes, Porto Grande, Pedra Branca do Amapari, Serra do Navio e Oiapoque com enfoque no contexto urbano de cada município e nas áreas de assentamentos e garimpos onde, no período de 1990 2003, ocorreu a maior incidência parasitária anual e o maior índice de lâminas positivas, para a infecção pelo Plasmodium falciparum. O estudo exploratório - descritivo vinculado ao estudo ecológico foram utilizados para descrever as áreas geográficas e os dados obtidos pelo Sistema de Informações de Malária (SISMAL) e Sistema de Vigilância Epidemiológica (SIVEP), deram suporte para as análises quantitativas e abordagem qualitativa da situação da malária nas áreas de estudo. Os mapas temáticos foram gerados a partir do georreferenciamento da população estudada utilizando-se a ferramenta ArquiGiz e a base cartográfica digital da Secretaria de Meio Ambiente SEMA , que possibilitaram através de uma visão espacial a discussão, analise e descrição dos fenômenos observados. Mediante os resultados obtidos concluiu-se que a atividade ocupacional e moradia têm relação direta com a incidência da malária, pois no período analisado mesmo com as políticas que foram utilizadas para o controle da endemia as áreas estudadas se mantiveram como de alto risco e a infecção pelo P. falciparum aumentou, principalmente no Oiapoque, fronteira internacional, onde os casos importados apresentaram a mesma tendência.