Correlated point sampling for geospatial scalar field visualization

Roveri R, Lehmann DJ, Gross M, Günther T (2018)


Publication Type: Conference contribution

Publication year: 2018

Publisher: Eurographics Association

Conference Proceedings Title: Vision, Modeling and Visualization, VMV 2018

Event location: Stuttgart DE

ISBN: 9783038680727

DOI: 10.2312/vmv.20181261

Abstract

Multi-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.

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How to cite

APA:

Roveri, R., Lehmann, D.J., Gross, M., & Günther, T. (2018). Correlated point sampling for geospatial scalar field visualization. In Fabian Beck, Carsten Dachsbacher, Filip Sadlo (Eds.), Vision, Modeling and Visualization, VMV 2018. Stuttgart, DE: Eurographics Association.

MLA:

Roveri, Riccardo, et al. "Correlated point sampling for geospatial scalar field visualization." Proceedings of the 2018 Conference on Vision, Modeling and Visualization, VMV 2018, Stuttgart Ed. Fabian Beck, Carsten Dachsbacher, Filip Sadlo, Eurographics Association, 2018.

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