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dc.contributor.authorRoveri, Riccardoen_US
dc.contributor.authorLehmann, Dirk J.en_US
dc.contributor.authorGross, Markusen_US
dc.contributor.authorGünther, Tobiasen_US
dc.contributor.editorBeck, Fabian and Dachsbacher, Carsten and Sadlo, Filipen_US
dc.date.accessioned2018-10-10T04:52:17Z
dc.date.available2018-10-10T04:52:17Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-072-7
dc.identifier.urihttps://doi.org/10.2312/vmv.20181261
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/vmv20181261
dc.description.abstractMulti-variate visualizations of geospatial data often use combinations of different visual cues, such as color and texture. For textures, different point distributions (blue noise, regular grids, etc.) can encode nominal data. In this paper, we study the suitability of point distribution interpolation to encode quantitative information. For the interpolation, we use a texture synthesis algorithm, which paves the path towards an encoding of quantitative data using points. First, we conduct a user study to perceptually linearize the transitions between uniform point distributions, including blue noise, regular grids and hexagonal grids. Based on the linearization models, we implement a point sampling-based visualization for geospatial scalar fields and we assess the accuracy of the user perception abilities by comparing the perceived transition with the transition expected from our linearized models. We illustrate our technique on several real geospatial data sets, in which users identify regions with a certain distribution. Point distributions work well in combination with color data, as they require little space and allow the user to see through to the underlying color maps. We found that interpolations between blue noise and regular grids worked perceptively best among the tested candidates.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing Ñ Empirical studies in visualization
dc.subjectGeographic visualization
dc.titleCorrelated Point Sampling for Geospatial Scalar Field Visualizationen_US
dc.description.seriesinformationVision, Modeling and Visualization
dc.description.sectionheadersInformation and Geographic Visualization
dc.identifier.doi10.2312/vmv.20181261
dc.identifier.pages119-126


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    ISBN 978-3-03868-072-7

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