filename     : Rov18c.pdf
entry        : inproceedings
conference   : Vision, Modeling and Visualization, Stuttgart, Germany, 10-12 October, 2017
pages        : 
year         : 2018
month        : October
title        : Correlated Point Sampling for Geospatial Scalar Field Visualization
subtitle     :
author       : Riccardo Roveri and Dirk Joachim Lehmann and Markus Gross and Tobias Günther
booktitle    : 
ISSN/ISBN    :
editor       : 
publisher    : 
publ.place   :
volume       :
issue        :
language     : english
keywords     : computer vision, point processing
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.