A fundamental goal of visualization is to produce images of data that support visual analysis, exploration, and discovery of novel insights. An important consideration during visualization design is the role of human visual perception. How we "see” details in an image can directly impact a viewer's efficiency and effectiveness. This paper surveys research on attention and visual perception, with a specific focus on results that have direct relevance to visualization and visual analytics. We discuss theories of low-level visual perception, then show how these findings form a foundation for more recent work on visual memory and visual attention. We conclude with a brief overview of how knowledge of visual attention and visual memory is being applied in visualization and graphics. We also discuss how challenges in visualization are motivating research in psychophysics.

Despite the improvement in health conditions across the world, communicable diseases remain among the leading mortality causes in many countries. Combating communicable diseases depends on surveillance, preventive measures, outbreak investigation, and the establishment of control mechanisms. Delays in obtaining country-level data of confirmed communicable disease cases, such as dengue fever, are prompting new efforts for short- to medium-term data. News articles highlight dengue infections, and they can reveal how public health messages, expert findings, and uncertainties are communicated to the public. In this article, we analyze dengue news articles in Asian countries, with a focus in India, for each month in 2014. We investigate how the reports cluster together, and uncover how dengue cases, public health messages, and research findings are communicated in the press. Our main contributions are to 1) uncover underlying topics from news articles that discuss dengue in Asian countries in 2014; 2) construct topic evolution graphs through the year; and 3) analyze the life cycle of dengue news articles in India, then relate them to rainfall, monthly reported dengue cases, and the Breteau Index. We show that the five main topics discussed in the newspapers in Asia in 2014 correspond to 1) prevention; 2) reported dengue cases; 3) politics; 4) prevention relative to other diseases; and 5) emergency plans. We identify that rainfall has 0.92 correlation with the reported dengue cases extracted from news articles. Based on our findings, we conclude that the proposed method facilitates the effective discovery of evolutionary dengue themes and patterns.

This paper describes a new method to explore and discover within a large data set. We apply techniques from preference elicitation to automatically identify data elements that are of potential interest to the viewer. These "elements of interest (EOI)” are bundled into spatially local clusters, and connected together to form a graph. The graph is used to build camera paths that allow viewers to "tour” areas of interest (AOI) within their data. It is also visualized to provide wayfinding cues. Our preference model uses Bayesian classification to tag elements in a data set as interesting or not interesting to the viewer. The model responds in real time, updating the elements of interest based on a viewer's actions. This allows us to track a viewer's interests as they change during exploration and analysis. Viewers can also interact directly with interest rules the preference model defines. We demonstrate our theoretical results by visualizing historical climatology data collected at locations throughout the world.

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To draw inferences from an ensemble, scientists need to compare data both within and between ensemble members. We propose two techniques to support ensemble exploration and comparison: a pairwise sequential animation method that visualizes locally neighboring members simultaneously, and a screen door tinting method that visualizes subsets of members using screen space subdivision. We demonstrate the capabilities of both techniques, first using synthetic data, then with simulation data of heavy ion collisions in high-energy physics. Results show that both techniques are capable of supporting meaningful comparisons of ensemble data.

The coronavirus disease COVID-19 was first reported in Wuhan, China, on December 31, 2019. The disease has since spread throughout the world, affecting 227.2 million individuals and resulting in 4,672,629 deaths as of September 9, 2021, according to the Johns Hopkins University Center for Systems Science and Engineering. Numerous sources track and report information on the disease, including Johns Hopkins itself, with its well-known Novel Coronavirus Dashboard. We were also interested in providing information on the pandemic. However, rather than duplicating existing resources, we focused on integrating sophisticated data analytics and visualization for region-to-region comparison, trend prediction, and testing and vaccination analysis. Our high-level goal is to provide visualizations of predictive analytics that offer policymakers and the general public insight into the current pandemic state and how it may progress into the future. Data are visualized using a web-based jQuery+Tableau dashboard.^† The dashboard allows both novice viewers and domain experts to gain useful insights into COVID-19's current and predicted future state for different countries and regions of interest throughout the world.

This paper proposes a method of abstractive summarization designed to scale to document collections instead of individual documents. Our approach combines semantic clustering, document size reduction within topic clusters, semantic chunking of a cluster's documents, GPT-based summarization and concatenation, and a combined sentiment and text visualization of each topic to support exploratory data analysis. A statistical comparison of our results with those of existing state-of-the-art systems, including BART, BRIO, PEGASUS, and MoCa, using ROUGE and METEOR summary scores showed statistically equivalent performance with BART and PEGASUS in the CNN/Daily Mail test dataset and with BART in the Gigaword test dataset. This finding is promising, since we view document collection summarization as more challenging than individual document summarization. We conclude with a discussion of how issues of scale are being addressed in the GPT large language model, then suggest potential areas for future work.