Yunhai Wang

Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series

Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.

Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization

We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes — an approach which previous methods cannot support.

A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization

Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional representations of the data help, for instance, to explore the class separability and the spatial distribution of the data. Widely-used unsupervised DR methods like PCA do not aim to maximize the class separation, while supervised DR methods like LDA often assume certain spatial distributions and do not take perceptual capabilities of humans into account. These issues make them ineffective for complicated class structures. Towards filling this gap, we present a perception-driven linear dimensionality reduction approach that maximizes the perceived class separation in projections. Our approach builds on recent developments in perception-based separation measures that have achieved good results in imitating human perception. We extend these measures to be density-aware and incorporate them into a customized simulated annealing algorithm, which can rapidly generate a near optimal DR projection. We demonstrate the effectiveness of our approach by comparing it to state-of-the-art DR methods on 93 datasets, using both quantitative measure and human judgments. We also provide case studies with class-imbalanced and unlabeled data.

Forecast Verification and Visualization based on Gaussian Mixture Model Co-estimation

Precipitation forecast verification is essential to the quality of a forecast. The Gaussian mixture model (GMM) can be used to approximate the precipitation of several rain bands and provide a concise view of the data, which is especially useful for comparing forecast and observation data. The robustness of such comparison mainly depends on the consistency of and the correspondence between the extracted rain bands in the forecast and observation data. We propose a novel co‐estimation approach based on GMM in which forecast and observation data are analysed simultaneously. This approach naturally increases the consistency of and correspondence between the extracted rain bands by exploiting the similarity between both forecast and observation data. Moreover, a novel visualization and exploration framework is implemented to help the meteorologists gain insight from the forecast. The proposed approach was applied to the forecast and observation data provided by the China Meteorological Administration. The results are evaluated by meteorologists and novel insight has been gained.

EdWordle: Consistency-Preserving Word Cloud Editing

We present EdWordle, a method for consistently editing word clouds. At its heart, EdWordle allows users to move and edit words while preserving the neighborhoods of other words. To do so, we combine a constrained rigid body simulation with a neighborhood-aware local Wordle algorithm to update the cloud and to create very compact layouts. The consistent and stable behavior of EdWordle enables users to create new forms of word clouds such as storytelling clouds in which the position of words is carefully edited. We compare our approach with state-of-the-art methods and show that we can improve user performance, user satisfaction, as well as the layout itself.

Mathematical foundations of arc length-based aspect ratio selection

The aspect ratio of a plot can strongly influence the perception of trends in the data. Arc length based aspect ratio selection (AL) has demonstrated many empirical advantages over previous methods. However, it is still not clear why and when this method works. In this paper, we attempt to unravel its mystery by exploring its mathematical foundation. First, we explain the rationale why this method is parameterization invariant and follow the same rationale to extend previous methods which are not parameterization invariant. As such, we propose maximizing weighted local curvature (MLC), a parameterization invariant form of local orientation resolution (LOR) and reveal the theoretical connection between average slope (AS) and resultant vector (RV). Furthermore, we establish a mathematical connection between AL and banking to 45 degrees and derive the upper and lower bounds of its average absolute slopes. Finally, we conduct a quantitative comparison that revises the understanding of aspect ratio selection methods in three aspects: (1) showing that AL, AWO and RV always perform very similarly while MS is not; (2) demonstrating the advantages in the robustness of RV over AL; (3) providing a counterexample where all previous methods produce poor results while MLC works well.

Analogy-based volume exploration using ellipsoidal Gaussian transfer functions

Much effort has been made on multidimensional transfer function, which is designed for effective exploration of 3D scalar datasets. But now, existing solution for designing transfer function typically focuses on exploring volume independently without any prior knowledge. It remains, however, a big challenge for us to reuse the explored knowledge, experience and results in scientific visualization. In this paper, we present a novel technique that employs an analogy-based approach. It aims to facilitate automatic volume exploration for multiple datasets which may share common context or features. The kernel of our approach is using the template scheme. With the introduction of the Gaussian Mixture Model, we adopt this new scheme to modeling, designing and transferring—they are processed in the data histogram space. Then, we integrate this scheme into two-dimensional transfer function design. The result shows that the interesting features can easily be captured with little user workload after adopting our approach.Graphical Abstract

Visual analysis of haze evolution and correlation in Beijing

Haze is a hazardous atmospheric phenomenon that threatens human health and leads to severe economic problems. A number of weather factors are relevant to the emergence and evolvement of haze. In this paper, we present a visual analytics system for haze study, including its evolution and correlations to a number of weather factors. Specifically, we introduce a haze event detection algorithm based on common haze identification rules in meteorology using the PM2.5 concentration data. We develop a comparative visualization to consistently overview trends of scalar variables and wind directions, in which wind patterns are extracted via clustering streamlines at user-given sampling time. To study the correlation between wind and PM2.5, we decompose time steps into time intervals according to the temporal similarity of streamlines. Additionally, we develop a 1D function dissimilarity measurement to study the temporal correlation between PM2.5 concentrations and relevant weather factors, such as wind strength, relatively humidity and planetary boundary layer. Furthermore, we employ particle advection using pathline computation within the wind field to locate the origins and destinations of particles seeded in user-interested areas. We applied our system to study of a number of hazes occurring in January 2013 in Beijing, (China). Interpretations and evaluations from domain experts demonstrate the effectiveness of our system in facilitating haze study.Graphical abstract

Cluster aware Star Coordinates

Abstract Star coordinates is an important visualization tool for exploring high-dimensional data. By carefully manipulating the star-coordinate axes, users can obtain a good projection matrix to reveal the cluster structures in the high-dimensional data. However, finding a good projection matrix through axes manipulation is often a very tedious and trial-and-error process. This paper presents cluster aware star coordinates plot , which not only improves the efficiency of axes manipulation with higher cluster quality, but also enables users to learn the relations between cluster and data attributes. Based on the proposed approximated visual silhouette index, we introduce the silhouette index view, which interactively informs the user of the cluster quality of the projection. However, the user may still have no clue on how to manipulate the axes to improve the cluster quality. To resolve this issue, we propose a dimensionality reduction technique for visualization to progressively modify the projection matrix and improve the cluster results. Through this technique including a family of cluster-aware interactions, users can highlight important features of interest, such as points, clusters and dimensions, effectively investigate the change of cluster structures, and steer their relationship with the dimensions. In the end, we employ twelve high-dimensional data sets and demonstrate the effectiveness of our method through a series of experiments: comparison with state-of-the-art methods, interactive outlier detection, and exploration of cluster-dimension relationship.

Image-Based Aspect Ratio Selection

Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federers co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.