• The Korean Journal of Applied Statistics
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• v.21 no.4
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• pp.695-704
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• 2008

The parallel coordinate plot is a graphical data analysis technique for plotting multivariate data. The parallel coordinate plot overcomes the visualization problem of the Cartesian coordinate system for dimensions greater than 4. But, using different ordering of coordinate axes in the parallel coordinate plot of the same data may make different interpretations. Hence, we can use the regular polyprism parallel coordinate plot as an alternative for overcoming the variable arrangement problem of the parallel coordinate plot.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.911-921
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• 2008

Visualization of the gene expression data on a low-dimensional graph is helpful in uncovering biological information contained in the data. In this study, we focus on two modified versions of the parallel coordinate plot. First one is the ePCP(enhanced parallel coordinate plot) which shows "near smooth" connecting curves between axes spaced proportionately to the proximity of re-ordered variables. Second one is APCP(Andrews' type parallel coordinate plot) which is obtained by rotating Andrews' plot that has a form of the parallel coordinate plot. Visualization procdures using ePCP and APCP are given for the lymphoma data case.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.587-595
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• 2008

Parallel coordinate plot of Inselberg (1985) is useful for visualizing dozens of variables, but so far the plot's applicability is limited to the variables of numerical type. The aim of this study is to extend the parallel coordinate plot so that it can accommodate both numerical and categorical variables. We combine Hayashi's (1950, 1952) quantification method of categorical variables and Hurley's (2004) endlink algorithm of ordering variables for the parallel coordinate plot. In line with our former study (Kwak and Huh, 2008), we develop Andrews' type modification of conventional straight-lines parallel coordinate plot to visualize the mixed-type data.

• The Korean Journal of Applied Statistics
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• v.9 no.1
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• pp.45-52
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• 1996

In this paper, we describe the basic properties of the parallel coordinate plots and propose a dynamic parallel coordinate plot. This dynamic parallel coordinate plot can be used as a dynamic graphics tool for multivariate data analysis.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.87-94
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• 2008

Andrews (1972) proposed to combine trigonometric functions to represent n observations of p variates, where the coefficients in linear sums are taken from the values of corresponding observation's respective variates. By viewing Andrews' plot as a collection of n trajectories of p-dimensional objects (observations) as a weighting point loaded with dimensional weights moves along a certain path on the hyper-dimensional sphere, we develop graphical techniques for further uses in data visualization. Specifically, we show that the parallel coordinate plot is a special case of Andrews' plot and we demonstrate the versatility of Andrews' plot with a projection pursuit engine.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.44-53
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• 2011

This study designed and implemented a web-based computing environment for geovisual analytics using HTML5 canvas. The computing environment supports visualization tools and user's interaction. The visualization tools are cluster map, animated map, temporal parallel coordinate plot, and temporal heat map chart. Users can explore the temporal changes of cluster using multiple view and brushing technique. The computing environment that works well across browsers is used in the computing environment with multiple devices.

• Spatial Information Research
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• v.14 no.1 s.36
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• pp.163-176
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• 2006

The concept of 'spatial association' explains spatial distribution pattern of geographical phenomenon based on similarity with neighborhoods, as in the Tobler's Law of Geography: 'Everything is related to everything else, but near things are more related than distant things.' In this study, we develop a time-series exploratory analysis tool for discovering temporal patterns of spatial association by combining spatial statistics and geo-visualization, and thus present a possibility to support spatial decision-making process. As for the spatial proximity weight matrix indispensable to measuring global and local spatial association, we employ a variety of flexible weighting schemes using geometric characteristics of areal unit. In addition, we renovate the existing visualization methods for more effective understanding of the procedures and results of time-series analysis on spatial association: for instance, temporal parallel coordinate plot with box plot, animated map for spatial association, and 3D Moran scatterplot. The feasibility of our system is verified by time-series analysis experiments on the spatial association of land price fluctuation rate for all administrative units in Korea, $1995{\sim}2004$.