Dataset Information

Exploring High-D Spaces with Multiform Matrices and Small Multiples.

ABSTRACT: We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop a general, MultiForm, Bivariate Matrix and a complementary MultiForm, Bivariate Small Multiple plot in which different bivariate representation forms can be used in combination. We demonstrate the flexibility of this approach with matrices and small multiples that depict multivariate data through combinations of: scatterplots, bivariate maps, and space-filling displays. Second, we apply a measure of conditional entropy to (a) identify variables from a high-dimensional data set that are likely to display interesting relationships and (b) generate a default order of these variables in the matrix or small multiple display. Third, we add conditioning, a kind of dynamic query/filtering in which supplementary (undisplayed) variables are used to constrain the view onto variables that are displayed. Conditioning allows the effects of one or more well understood variables to be removed from the analysis, making relationships among remaining variables easier to explore. We illustrate the individual and combined functionality enabled by this approach through application to analysis of cancer diagnosis and mortality data and their associated covariates and risk factors.

SUBMITTER: Maceachren A

PROVIDER: S-EPMC3176663 | biostudies-literature | 2003

REPOSITORIES: biostudies-literature

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Exploring High-D Spaces with Multiform Matrices and Small Multiples.

Maceachren Alan A Dai Xiping X Hardisty Frank F Guo Diansheng D Guo Diansheng D Lengerich Gene G

IEEE Conference on Information Visualization : an International Conference on Computer Visualization & Graphics, proceedings ... IEEE Conference on Information Visualization 20030101

We introduce an approach to visual analysis of multivariate data that integrates several methods from information visualization, exploratory data analysis (EDA), and geovisualization. The approach leverages the component-based architecture implemented in GeoVISTA Studio to construct a flexible, multiview, tightly (but generically) coordinated, EDA toolkit. This toolkit builds upon traditional ideas behind both small multiples and scatterplot matrices in three fundamental ways. First, we develop ...[more]

PMID: 21947129

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Project description:Background and aimsEnlarged perivascular spaces (also known as Virchow-Robin spaces) on T2-weighted brain magnetic resonance imaging are common, but their etiology, and specificity to small vessel as opposed to general cerebrovascular disease or ageing, is unclear. We tested the association between enlarged perivascular spaces and ischemic stroke subtype, other markers of small vessel disease, and common vascular risk factors.MethodsWe prospectively recruited patients with acute stroke, diagnosed and subtyped by a stroke physician using clinical features and brain magnetic resonance imaging. A neuroradiologist rated basal ganglia and centrum semiovale enlarged perivascular spaces on a five-point scale, white matter lesions, recent and old infarcts, and cerebral atrophy. We assessed associations between basal ganglia-, centrum semiovale- and total (combined basal ganglia and centrum semiovale) enlarged perivascular spaces, stroke subtype, white matter lesions, atrophy, and vascular risk factors.ResultsAmong 298 patients (mean age 68 years), after adjusting for vascular risk factors and white matter lesions, basal ganglia-enlarged perivascular spaces were associated with increasing age (P = 0.001), centrum semiovale-enlarged perivascular spaces (P < 0.001), cerebral atrophy (P = 0.03), and lacunar stroke subtype (P = 0.04). Centrum semiovale-enlarged perivascular spaces were associated mainly with basal ganglia-enlarged perivascular spaces. Total enlarged perivascular spaces were associated with increasing age (P = 0.01), deep white matter lesions (P = 0.005), and previous stroke (P = 0.006).ConclusionsEnlarged perivascular spaces are associated with age, lacunar stroke subtype and white matter lesions and should be considered as another magnetic resonance imaging marker of cerebral small vessel disease. Further evaluation of enlarged perivascular spaces in studies of ageing, stroke, and dementia is needed to determine their pathophysiological importance.

Dataset Information

Exploring High-D Spaces with Multiform Matrices and Small Multiples.

Publications

Exploring High-D Spaces with Multiform Matrices and Small Multiples.

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