We propose a technique to represent two-dimensional data using stipples. While stippling is often regarded as an illustrative method, we argue that it is worth investigating its suitability for the visualization domain. For this purpose, we generalize the Linde–Buzo–Gray stippling algorithm for information visualization purposes to encode continuous and discrete 2D data. Our proposed modifications provide more control over the resulting distribution of stipples for encoding additional information into the representation, such as contours. We show different approaches to depict contours in stipple drawings based on locally adjusting the stipple distribution. Combining stipple-based gradients and contours allows for simultaneous assessment of the overall structure of the data while preserving important local details. We discuss the applicability of our technique using datasets from different domains and conduct observation-validating studies to assess the perception of stippled representations.

Development of object detectors for video in applications such as autonomous driving requires an iterative training process with data that initially requires human labeling. Later stages of development require tuning a large set of parameters that may not have labeled data available. For each training iteration and parameter selection decision, insight is needed into object detector performance. This work presents a visualization method called Space-Time Slicing to assist a human developer in the development of object detectors for driving applications without requiring labeled data. Space-Time Slicing reveals patterns in the detection data that can suggest the presence of false positives and false negatives. It may be used to set such parameters as image pixel size in data preprocessing and confidence thresholds for object classifiers by comparing performance across different conditions.

We process information in memory and different people have different memory capacity. It is therefore important to understand possible impact of memory capacity when it comes to graph comprehension. In an attempt towards this direction, we conducted a user study investigating the impact of working memory capacity on graph reading task performance. Forty-six university students participated in the study performing a graph reading task with one hundred graph drawings of different complexity levels. Their working memory capacity and task performance (accuracy and time) were measured and recorded. The results of regression analyses indicated that working memory capacity was a significant predictor of performance accuracy, but not for response time. In this paper, we present the details of the study and discuss our findings and limitations of the study. Possible future research directions are also suggested.

In this paper, we propose a system to automatically generate corresponding visualization and answers when specific questions are raised for a tabular data. In our approach, the questions are classified into different task types, and related data items in the table are extracted by semantic parsing with the deep neural network, which can be visualized in different ways automatically. Tasks including questions on the comparison, trend, and aggregation are supported. We demonstrate a system which is capable of answering questions with reasonable accuracy.

Captions act an important role in guiding people to interpret the chart and conveying messages from the designer. But it requires labor efforts to make a proper caption. In this paper, we propose a novel automatic approach to generate captions from visualization charts powered by deep learning. The model learns to recognize significant features of the chart, which are mainly represented by subsets of its visual elements. Through a carefully designed summary template, each subset is converted into a descriptive sentence, i.e. data fact, and compose a complete caption for the chart.

This paper is an early study using bicycle sharing data to deduce the purpose of movements. We use the OSM map information to classify area features of the bike sharing stations. We, then, infer the purpose of movements according to the area features and apply them to the actual cases to visualize the purpose changes while people use the bicycles over time.

We demonstrate how data-driven visualization can be used to explain processes that are not directly observable. While the classic approach is to manually illustrate these processes, we created a procedural model which can be directly parametrized by the underlying data. We used this approach to explain the process of dynamic instability of microtubules. Our model of a microtubule is parametrized by the growth speed data provided by Wittmann et al. [6], while the models of the molecules are taken from the Protein Data Bank [2]. We used our real-time molecular visualization software marion [5] together with our procedural generator of molecular scenes [3] to create an intercellular environment. The dynamics of the molecules were informed by fundamental biological research [1, 7]. To put our visualization in perspective with reality, we included microscopy data of microtobule dynamics by Matov et al. [4].

"The Impatient List" is a storytelling piece that calls the general public 's attention to patients of the kidney transplant in the United States. Organ transplantation is a highly collaborative task, involving the patient, the donor, hospitals and organizations. The organization has been collecting this data for three decades which was the primary source of data for our visualization. With empathy to those patients, we want our designs to tell a story about those data to the general public. Therefore, to personalize the analysis for a group of varied users, our design allows users to filter the data based on their location, blood-group, and BMI. This way, the user can guide the story to their suited interests. To present the big picture, we adopted the design of a choropleth for waiting list information over the geo-location, and animation for waiting list change over the last two decades. You can view the data story at - https://xujunjiejack.github.io/