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Matthias Templ Beat Hulliger Alexander Kowarik Karin Fürst 《International journal of geographical information science》2013,27(4):685-698
The checkerplot is a new type of graphical display that combines geographical information and statistical plots. Hereby, the traditional plots like barplots or polygon lines are visualized in geographical order on a grid. The checkerplots can be seen as a mixture between thematic maps and the grid representation in trellis plots. In a checkerplot, any complex statistical graphics that are produced for geographical areas are placed in a grid. An interpretable checkerplot requires an arrangement of areas on the grid that reflects the underlying geography. A loss function is proposed that represents the distortion of the underlying geography needed in order to place the areas onto a grid. It is demonstrated that the minimization of the loss function does indeed produce interpretable checkerplots. Moreover, the optimization problem can be formulated as a linear programming problem that can be solved using standard linear programming solvers. The proposed checkerplot is applied to US health insurance data to analyze the development of the coverage rate in the health system per state with respect to the different health care programs. Moreover, an example related to the EU-member states is also given. Additional information like the national flags of countries are placed in each grid to allow better visibility and recognition of the countries. The checkerplot is implemented in the R-package sparkTable (Kowarik, Meindl, and Templ, 2012. sparkTable: sparklines and graphical tables for tex and html [online]. R package version 0.9.3 [Accessed 7 February 2012]) and can be freely downloaded from the CRAN repository. It is implemented in a flexible manner and not restricted to the examples given in this contribution. 相似文献
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Cluster analysis can be used to group samples and to develop ideas about the multivariate geochemistry of the data set at hand. Due to the complex nature of regional geochemical data (neither normal nor log-normal, strongly skewed, often multi-modal data distributions, data closure), cluster analysis results often strongly depend on the preparation of the data (e.g. choice of the transformation) and on the clustering algorithm selected. Different variants of cluster analysis can lead to surprisingly different cluster centroids, cluster sizes and classifications even when using exactly the same input data. Cluster analysis should not be misused as a statistical “proof” of certain relationships in the data. The use of cluster analysis as an exploratory data analysis tool requires a powerful program system to test different data preparation, processing and clustering methods, including the ability to present the results in a number of easy to grasp graphics. Such a tool has been developed as a package for the R statistical software. Two example data sets from geochemistry are used to demonstrate how the results change with different data preparation and clustering methods. A data set from S-Norway with a known number of clusters and cluster membership is used to test the performance of different clustering and data preparation techniques. For a complex data set from the Kola Peninsula, cluster analysis is applied to explore regional data structures. 相似文献
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van den Boogaart K. G. Filzmoser P. Hron K. Templ M. Tolosana-Delgado R. 《Mathematical Geosciences》2021,53(5):823-858
Compositional data carry their relevant information in the relationships (logratios) between the compositional parts. It is shown how this source of information can be used in regression modeling, where the composition could either form the response, or the explanatory part, or even both. An essential step to set up a regression model is the way how the composition(s) enter the model. Here, balance coordinates will be constructed that support an interpretation of the regression coefficients and allow for testing hypotheses of subcompositional independence. Both classical least-squares regression and robust MM regression are treated, and they are compared within different regression models at a real data set from a geochemical mapping project.
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