Honeycomb Plots: Visual Enhancements for Hexagonal Maps

Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.publishedVersio

Accumulation as a tool for efficient visualization of geographical and temporal data

International audienceIn this paper, we describe a set of visualization methods with an accumulation tool to perform interactive data exploration. Accumulation maps or Kernel Density Estimation (KDE) maps, count the amount of data accumulated at a certain location. The accumulation tool addresses the cluttering issues when displaying large amounts of data. But the accumulation tool can also be used to unveil patterns, to detect outliers and flaws in datasets. Through these applied examples, we show how the accumulation tool and its real time applications take advantage of human vision and are therefore assets for data exploration and validation. As the accumulation tool uses GPU techniques, it can be used in real-time with large datasets

Constructing and Visualizing High-Quality Classifier Decision Boundary Maps dagger

Visualizing decision boundaries of machine learning classifiers can help in classifier design, testing and fine-tuning. Decision maps are visualization techniques that overcome the key sparsity-related limitation of scatterplots for this task. To increase the trustworthiness of decision map use, we perform an extensive evaluation considering the dimensionality-reduction (DR) projection techniques underlying decision map construction. We extend the visual accuracy of decision maps by proposing additional techniques to suppress errors caused by projection distortions. Additionally, we propose ways to estimate and visually encode the distance-to-decision-boundary in decision maps, thereby enriching the conveyed information. We demonstrate our improvements and the insights that decision maps convey on several real-world datasets

Using multiple attribute-based explanations of multidimensional projections to explore high-dimensional data

Multidimensional projections (MPs) are effective methods for visualizing high-dimensional datasets to find structures in the data like groups of similar points and outliers. The insights obtained from MPs can be amplified by complementing these techniques by several so-called explanatory mechanisms. We present and discuss a set of six such mechanisms that explain MPs in terms of similar dimensions, local dimensionality, and dimension correlations. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate how the provided explanatory views can be combined to augment each other's value and thereby lead to refined insights in the data for several high-dimensional datasets, and how these insights correlate with known facts about the data under study

A depth-cueing scheme based on linear transformations in tristimulus space

We propose a generic and flexible depth-cueing scheme which subsumes many well-known and new color-based depth-cueing approaches. In particular, it includes standard intensity depth-cueing and rather neglected pure saturation depth-cueing. A couple of new combinations and variations of depth cues are presented. Their usefulness is demonstrated in many different fields of application, reaching from non-photorealistic rendering to information visualization. In addition to cues based on a geometric concept of depth, an abstract visualization approach in the form of semantic depth-cueing is proposed. Our depth-cueing scheme is based on linear transformations in the 3D tristimulus space of colors and on weighted sums of colors. Since all of the required operations are supported by contemporary consumer graphics hardware, the depth-cueing scheme can be implemented without performance cutbacks. Therefore, any real-time rendering application can be enriched by sophisticated depth-cueing