An Evaluation-Guided Approach for Effective Data Visualization on Tablets

There is a rising trend of data analysis and visualization tasks being performed on a tablet device. Apps with interactive data visualization capabilities are available for a wide variety of domains. We investigate whether users grasp how to effectively interpret and interact with visualizations. We conducted a detailed user evaluation to study the abilities of individuals with respect to analyzing data on a tablet through an interactive visualization app. Based upon the results of the user evaluation, we find that most subjects performed well at understanding and interacting with simple visualizations, specifically tables and line charts. A majority of the subjects struggled with identifying interactive widgets, recognizing interactive widgets with overloaded functionality, and understanding visualizations which do not display data for sorted attributes. Based on our study, we identify guidelines for designers and developers of mobile data visualization apps that include recommendations for effective data representation and interaction

RSVP: Remote Sensing Visualization Platform for Data Fusion

Remote sensing involves the acquisition of data in terms of images, point clouds and so on. One of the major challenges with remote sensing datasets is managing and understanding the massive amounts of data that is collected. In many instances, scientists acquire data for the same region using varied sensing devices. Scientists would like to fuse and examine this data acquired from different sensing devices to further explore the region under investigation. Immersive visualization has emerged as an ideal solution for three-dimensional exploration of multimodal remote sensing data. The ability to manipulate data interactively in true 3D (using stereo) with interfaces designed specifically for the immersive environment can significantly speed up the exploration process. We have developed a visualization platform that facilitates the fusion of multiple modalities of remote sensing data and allows a scientist to learn more about the data obtained from different sensing devices. It is currently being used in research labs at Idaho State University and at the Idaho National Labs

Unboxing Cluster Heatmaps

Background: Cluster heatmaps are commonly used in biology and related fields to reveal hierarchical clusters in data matrices. This visualization technique has high data density and reveal clusters better than unordered heatmaps alone. However, cluster heatmaps have known issues making them both time consuming to use and prone to error. We hypothesize that visualization techniques without the rigid grid constraint of cluster heatmaps will perform better at clustering-related tasks. Results: We developed an approach to “unbox” the heatmap values and embed them directly in the hierarchical clustering results, allowing us to use standard hierarchical visualization techniques as alternatives to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45 practitioners to determine how cluster heatmaps are used, prototyping alternatives to cluster heatmaps using pair analytics with a computational biologist, and evaluating those alternatives with hour-long interviews of 5 practitioners and an Amazon Mechanical Turk user study with approximately 200 participants. We found statistically significant performance differences for most clustering-related tasks, and in the number of perceived visual clusters. Visit git.io/vw0t3 for our results. Conclusions: The optimal technique varied by task. However, gapmaps were preferred by the interviewed practitioners and outperformed or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps are similar to cluster heatmaps, but relax the heatmap grid constraints by introducing gaps between rows and/or columns that are not closely clustered. Based on these results, we recommend users adopt gapmaps as an alternative to cluster heatmaps

Visualization of Off-Screen Data on Tablets Using Context-Providing Bar Graphs and Scatter Plots

Visualizing data on tablets is challenging due to the relatively small screen size and limited user interaction capabilities. Standard data visualization apps provide support for pinch-and-zoom and scrolling operations, but do not provide context for data that is off-screen. When exploring data on tablets, the user must be able to focus on a region of interest and quickly find interesting patterns in the data. We present visualization techniques that facilitate seamless interaction with the region of interest on a tablet using context-providing bar graphs and scatter plots. Through aggregation, fisheye-style, and overview+detail representations, we provide context to the users as they explore a region of interest. We evaluated the efficacy of our techniques with the standard, interactive bar graph and scatter plot applications on a tablet, and found that one of our bargraph visualizations - Fisheye-style Focus+Context visualization (BG2) resulted in the fewest errors, least frustration and took the least amount of time. Similarly, one of our scatter plot visualizations - User Driven Overview+Detail (SP3) - resulted in the fewest errors, least frustration and took the least amount of time. Overall, users preferred the context-providing techniques over traditional bar graphs and scatter plots, that include pinch-and-zoom and fling-based scrolling capabilities

Rapid Development of Medical Imaging Tools with Open-Source Libraries

Rapid prototyping is an important element in researching new imaging analysis techniques and developing custom medical applications. In the last ten years, the open source community and the number of open source libraries and freely available frameworks for biomedical research have grown significantly. What they offer are now considered standards in medical image analysis, computer-aided diagnosis, and medical visualization. A cursory review of the peer-reviewed literature in imaging informatics (indeed, in almost any information technology-dependent scientific discipline) indicates the current reliance on open source libraries to accelerate development and validation of processes and techniques. In this survey paper, we review and compare a few of the most successful open source libraries and frameworks for medical application development. Our dual intentions are to provide evidence that these approaches already constitute a vital and essential part of medical image analysis, diagnosis, and visualization and to motivate the reader to use open source libraries and software for rapid prototyping of medical applications and tools

Generalized Framework and Algorithms for Illustrative Visualization of Time-Varying Data on Unstructured Meshes

Photo- and physically-realistic techniques are often insufficient for visualization of simulation results, especially for 3D and time-varying datasets. Substantial research efforts have been dedicated to the development of non-photorealistic and illustration-inspired visualization techniques for compact and intuitive presentation of such complex datasets. While these efforts have yielded valuable visualization results, a great deal of work has been reproduced in studies as individual research groups often develop purpose-built platforms. Additionally, interoperability between illustrative visualization software is limited due to specialized processing and rendering architectures employed in different studies. In this investigation, a generalized framework for illustrative visualization is proposed, and implemented in marmotViz, a ParaView plugin, enabling its use on variety of computing platforms with various data file formats and mesh geometries. Detailed descriptions of the region-of-interest identification and feature-tracking algorithms incorporated into this tool are provided. Additionally, implementations of multiple illustrative effect algorithms are presented to demonstrate the use and flexibility of this framework. By providing a framework and useful underlying functionality, the marmotViz tool can act as a springboard for future research in the field of illustrative visualization

Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms

Developing both graphical and commandline user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet their potential only if they can be easily and frequently used by their intended users. Deployment of a large suite of such algorithms on multiple platforms requires consistency of user interface controls, consistent results across various platforms and thorough testing. We present the design and implementation of a novel object-oriented framework that allows for rapid development of complex image analysis algorithms with many reusable components and the ability to easily add graphical user interface controls. Our framework also allows for simplified yet robust nightly testing of the algorithms to ensure stability and cross platform interoperability. All of the functionality is encapsulated into a software object requiring no separate source code for user interfaces, testing or deployment. This formulation makes our framework ideal for developing novel, stable and easy-to-use algorithms for medical image analysis and computer assisted interventions. The technological The framework has been both deployed at Yale and released for public use in the open source multi-platform image analysis software - BioImage Suite (bioimagesuite.org)

Art-inspired techniques for visualizing time-varying data

Time-varying data is huge and contains specific features of interest to an application expert. Standard techniques for visualizing time-varying data such as snapshots and animations are limited in their ability to convey change and draw the user's attention to regions of; interest. Art-inspired techniques are presented to provide temporal context to the domain expert. I present novel visualization techniques that convey change over time in a single image. Illustrators convey change over time using illustrative cues. Time-varying data, in which three-dimensional features are moving over time, can be effectively visualized using illustrative techniques such as speedlines, flow ribbons, strobe silhouettes and opacity based; techniques. I applied these techniques to computational fluid dynamics data and; evaluated the effectiveness of our techniques with the help of a formal user study. The illustration-inspired techniques were also applied to the field of atmospheric physics, where I generated novel hurricane visualizations that allowed collaborators to obtain insight into; the evolution and intensification of a hurricane.; For effectively visualizing time-varying data, such as infant mortality over eight years or global rainfall over an entire century, we developed a novel pointillism-based technique. This technique was inspired by paintings by Seurat, applied the visual color blending theory to place brush strokes close to each other. We applied techniques from pointillistic; paintings to convey variability in time-varying data and to convey trends over time in a single image. The formal user evaluation conducted to evaluate these techniques gave us insight into the strengths of our techniques and led us to the result that users though correct were less confident of their answers as compared to snapshots-based and animation-based techniques

Illustration-inspired techniques for visualizing time-varying data

Traditionally, time-varying data has been visualized using snapshots of the individual time steps or an animation of the snapshots shown in a sequential manner. For larger datasets with many timevarying features, animation can be limited in its use, as an observer can only track a limited number of features over the last few frames. Visually inspecting each snapshot is not practical either for a large number of time-steps. We propose new techniques inspired from the illustration literature to convey change over time more effectively in a time-varying dataset. Speedlines are used extensively by cartoonists to convey motion, speed, or change over different panels. Flow ribbons are another technique used by cartoonists to depict motion in a single frame. Strobe silhouettes are used to depict previous positions of an object to convey the previous positions of the object to the user. These illustration-inspired techniques can be used in conjunction with animation to convey change over time. Keywords: Flow visualization, Non-photorealistic rendering, time-varying data, illustratio

Art-inspired techniques for visualizing time-varying data

Time-varying data is huge and contains specific features of interest to an application expert. Standard techniques for visualizing time-varying data such as snapshots and animations are limited in their ability to convey change and draw the user's attention to regions of; interest. Art-inspired techniques are presented to provide temporal context to the domain expert. I present novel visualization techniques that convey change over time in a single image. Illustrators convey change over time using illustrative cues. Time-varying data, in which three-dimensional features are moving over time, can be effectively visualized using illustrative techniques such as speedlines, flow ribbons, strobe silhouettes and opacity based; techniques. I applied these techniques to computational fluid dynamics data and; evaluated the effectiveness of our techniques with the help of a formal user study. The illustration-inspired techniques were also applied to the field of atmospheric physics, where I generated novel hurricane visualizations that allowed collaborators to obtain insight into; the evolution and intensification of a hurricane.; For effectively visualizing time-varying data, such as infant mortality over eight years or global rainfall over an entire century, we developed a novel pointillism-based technique. This technique was inspired by paintings by Seurat, applied the visual color blending theory to place brush strokes close to each other. We applied techniques from pointillistic; paintings to convey variability in time-varying data and to convey trends over time in a single image. The formal user evaluation conducted to evaluate these techniques gave us insight into the strengths of our techniques and led us to the result that users though correct were less confident of their answers as compared to snapshots-based and animation-based techniques