Nowadays, smartphones are a major means of daily communication for many people. During the COVID-19 pandemic, it has rapidly increased that information on the newly infected persons is obtained in the form of graphs on the smartphones. While this style of information acquisition is good at getting an outline, it cannot be argued that it sufficiently grasps the details. Recently, there is a growing need for a novel visualization method that allows us to read not only an outline but also the details in a limited display space on the smartphone. In this article, we present two-tone pseudo colored sparklines, which are sparklines, known as a word-size visualization to be embedded in text, utilizing two-tone pseudo coloring, which is inherently capable of visualizing the outline and the details simultaneously. We also introduce a mobile application which interactively displays texts embedding two-tone colored sparklines. The effectiveness of the primary interactive functionalities of the application is proven with examples of the use for atmospheric particle concentration datasets.

Scale-aware maps visually represent geographic information according to the scale of interest. Large-scale maps show detailed features, while small-scale maps require abstraction to fit into the limited space. This abstraction process, often referred to as cartographic generalization, improves map readability by avoiding spatial conflicts between geographic features. Key techniques include displacement (moving features), selection (omitting features), aggregation (combining features), and simplification (reducing complexity). Aggregation and simplification are complex because they introduce macroscopic transformations into the spatial configuration of maps. We describe our recent advances in interactive optimization for aggregation of building features aggregation, specifically in residential maps, accommodating a variety of needs and conditions from cartographers.

  In recent years, extreme weather events such as guerrilla torrential rains have been on the increase, causing severe human losses and widespread damage to property and infrastructure around the world. In Japan in particular, heavy rains have often caused severe damage, and the importance of highly accurate weather forecasting to mitigate and prevent damage caused by heavy rains is increasing every year. In general, weather forecasting involves the numerical prediction of future atmospheric conditions by ensemble simulations based on physical weather models. In this paper, we present an overview of the complex spatio-temporal behavior inherent in ensemble data and our efforts to achieve interactive analysis. A unique feature of our approach is that we represent ensemble data as fourth-order tensor data consisting of four bases: ensemble members, physical quantities, space, and time, which allows for effective analysis combining simple data operations and visualizations.

This paper presents and discuss the large data visualization environment on the K computer and supercomputer Fugaku. These flagship-class supercomputers have been designed to provide maximum performance during simulation runs, and they are capable of executing extreme-scale numerical simulations, which can generate large amounts of simulation results. In addition to these simulation results, large amounts of sensor measurements and system data have also been stored in form of time-varying multi-variate log data. We will present and discuss some efforts to provide large data visualization-oriented tools and applications on the provided hardware infrastructure which includes the HPC system itself and auxiliary pre- and post-processing systems. This includes some results from collaborative research work done with domestic and international academic institutions.