For effective visualizations, there are many types of design to consider. Visualization design focuses on core theory of tasks, data and visual encodings. Workflow design, user interface design and graphic design all contribute to successful visualizations. All design aspects range from initial design exploration to iterative design refinement. Guidelines can help, but have limitations.
Field
Visualization Theory
2023
2022
-
Causeworks: A Mixed-Initiative Framework for Causal Modeling
The construction of computational causal models for complex systems has typically been completed manually by domain experts and is a time-consuming, cumbersome process. We introduce Causeworks, an application in which operators “sketch” complex systems, leverage AI tools and expert knowledge to transform the sketches into computational causal models, and then apply analytics to understand how to influence the system.
-
Summarizing Text to Embed Qualitative Data into Visualizations
Qualitative data can be conveyed with strings of text. Fitting longer text into visualizations requires a) space to place the text inside the visualization; and b) appropriate text to fit the space available. To fit text within these layouts is a function for emerging NLP capabilities such as summarization.
-
Multimodal Analogs to Infer Humanities Visualization Requirements
Gaps and requirements for multi-modal interfaces for humanities can be explored by observing the configuration of real-world environments and the tasks of visitors within them compared to digital environments. Some of these capabilities exist, but not routinely available in implementations.
-
Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery
Visual Analytics of Hierarchical and Network Timeseries Models
Dual Y Axes Charts Defended: Case Studies, Domain Analysis and a Method
VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifier
Uncharted contributed the following chapters to Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery:
Visual Analytics of Hierarchical and Network Timeseries Models: Visual analytics that represent many aspects of timeseries models in one holistic application are perceptually scalable to exploration of millions of nodes.
Dual Y Axes Charts Defended: Case Studies, Domain Analysis and a Method: We show dual axes charts are necessary for fine-grained correlation analysis that is not made obvious within single axis charts or other means.
VisIRML: Visualization with an Interactive Information Retrieval and Machine Learning Classifier: VisIRML, a system to classify and display unstructured data, produces higher quality labels than semi-supervised learning techniques.
-
3Dify: Extruding Common 2D Charts with Timeseries Data
3D charts are not common in financial services. We review chart use in practice. We create 3D financial visualizations starting with 2D charts used extensively in financial services, then extend into the third dimension with timeseries data. We embed the 2D view into the the 3D scene; constrain interaction and add depth cues to facilitate comprehension. Usage and extensions indicate success.
-
Self-Supervised Learning for Timeseries from Multi-Spectral Satellite Imagery
Distil is a system for constructing point-and-click machine learning models, here extended for multi-spectral satellite imagery for timeseries data leveraging an autoML pipeline, adding embedding model trained using self-supervised learning; rapid data labeling facilitated with image query; hierarchical geospatial timeseries modeling; and sub-image feature extraction using weakly-supervised segmentation.
2021
-
Surveying Wonderland to Uncover More Literature Visualization Techniques
There are still many potential literature visualizations to be discovered. By focusing on a single text, the author surveys many existing visualizations across domains, in the wild, and creates new visualizations. Many dozen techniques are indicated, suggesting a wider variety of potential visualizations beyond research disciplines.
-
A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling
Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems.
-
A Multi-scale Visual Analytics Approach for Exploring Biomedical Knowledge
Best Paper, VAHC Workshop, IEEE VisWeek 2021This paper describes an ongoing multi-scale visual analytics approach for exploring and analyzing biomedical knowledge at scale. We utilize global and local views, hierarchical and flow-based graph layouts, multi-faceted search, neighborhood recommendations, and document visualizations to help researchers interactively explore, query, and analyze biological graphs against the backdrop of biomedical knowledge.
-
Useable Machine Learning for Sentinel-2 Multispectral Satellite Imagery
One of the challenges when building Machine Learning (ML) models using satellite imagery is building sufficiently labeled data sets for training. In the past, this problem has been addressed by adapting computer vision approaches to GIS data with significant recent contributions to the field. But when trying to adapt these models to Sentinel-2 multi-spectral satellite imagery these approaches fall short. To address this deficit, we present Distil, and demonstrate a specific method using our system for training models with all available Sentinel-2 channels.
-
A Multi-scale Approach for Biological Graph Visualization: Local Analysis in Global Context
Biologists grapple with large multi-scale graphs to find relevant subgraphs for answering a range of biological questions. Our approach for scalable graph analysis enables biologists to interactively explore, query, and analyze biological graphs at different scales. Computational biologists see promise in our approach for various use cases such as drug interactions and disease propagation.
-
Automated Insights on Visualizations with Natural Language Generation
Quantitative data, such as a 10k financial report, requires cognitive load to scan the columns and rows and identify patterns and important takeaways, whether novice or subject matter expert. Visualizations can be used to summarize and reveal patterns. However, it may still be difficult to understand what is most meaningful. What should the viewer pay attention to? In this research, we reduce the cognitive load in understanding tabular data by combining charts with ranked natural language generated (NLG) bullet point statements that summarize the top takeaways.
-
Causeworks Collaboration: Simultaneous Causal Model Construction and Analysis
Military planners use “Operational Design” (OD) to understand systems and relationships in complex operational environments. Causeworks is a visual analytics application for OD teams to collaboratively build causal models of environments to understand and find solutions to affect them. Collaborative causal modeling can help teams craft better plans, but there are unique challenges in developing synchronous collaboration tools for building and using causal models. Causeworks overlays analytics inputs and outputs over a shared causal model to flexibly support multiple modeling tasks simultaneously in a collaborative environment with minimal state management burden on users.
-
Causeworks: A Framework for Transforming User Hypotheses into a Computational Causal Model
Causal Model building for complex problems has typically been completed manually by domain experts and is currently a time-consuming, cumbersome process. The resulting models are simple diagrams produced on whiteboards, and do not support computational analytics, thus limiting usefulness. Causeworks helps operators “sketch” complex systems, and transforms sketches into computational causal models using automatic and semi-automatic causal model construction from knowledge extracted from unstructured and structured documents. Causeworks integrates computational analytics to assist users in understanding and influencing the system.
2020
-
Visualizing with Text
Visualizing with Text uncovers the rich palette of text elements usable in visualizations from simple labels through to documents. Using a multidisciplinary research effort spanning across fields including visualization, typography, and cartography, it builds a solid foundation for the design space of text in visualization.
-
Literal Encoding: Text Is a First-Class Data Encoding
Digital humanities are rooted in text analysis. However, most visualization paradigms use only categoric, ordered or quantitative data. Literal text must be considered a base data type to encode into visualizations. Literal text offers functional, perceptual, cognitive, semantic and operational benefits. These are briefly illustrated with a subset of sample visualizations focused on semantic word sequences, indicating benefits over standard graphs, maps, treemaps, bar charts and narrative layouts.
2019
-
Industry-Driven Visual Analytics for Understanding Financial Timeseries Models
Timeseries models are used extensively in financial services, for example, to quantify risk and predict economics. However, analysts also need to comprehend the structure and behavior of these models to better understand and explain results. We present a methodology, derived from extensive industry experience, to aid explanation through integrated interactive visualizations that reveal model structure and behavior of constituent timeseries factors, thereby increasing understanding of the model, the domain and the sensitivities. Expert feedback indicates alignment with mental models.
-
The Design Space of SparkWords
SparkWords are consistently-sized words embedded in prose, lists or tables; enhanced with additional data including (a) categoric, ordered or quantitative data; (b) encoded by a variety of attributes (singular or multiple); and (c) applied to words or letters. Historic examples and sample implementations show a range of novel techniques and different uses.
2016
-
Typographic Sets: Labeled Set Elements with Font Attributes
We show that many different set visualization techniques can be extended with the addition of labeled elements using font attributes. Elements labeled with font attributes can: uniquely identify elements; encode membership in ten sets; use size to indicate proportions among set relations; can scale to thousands on clearly labeled elements; and use intuitive mappings to facilitate decoding.
-
Using Typography to Expand the Design Space of Data Visualization
This article is a systematic exploration and expansion of the data visualization design space focusing on the role of text. A critical analysis of text usage in data visualizations reveals gaps in existing frameworks and practice. A cross-disciplinary review including the fields of typography, cartography, and coding interfaces yields various typographic techniques to encode data into text, and provides scope for an expanded design space.
2015
2020
-
Towards a Paradigm for Visual Modeling
Visualization has recently gained a foothold in the field of artificial intelligence research. Typically, this work has focused on visualizing modules or specific dynamics of machine learning models, doing so for the purposes of model explainability or for visual debugging. Drawing from ongoing projects involving pandemic analysis and famine shocks, this seminar describes research efforts on graphical modeling where visualization functions as the medium for modeling itself.
-
The Vast Potential for New Text Visualizations
More data is text than quantitative. Beyond the current standard visualization techniques, so much more is feasible looking across domains and history. Designers, computer scientists and analysts of texts need an expanded visualization vocabulary. This talk illustrates the breadth of design possibilities for using text with visualizations. First, the design space is defined by a review of historic exemplars. Then, using this new design space, text and typography are used to create new kinds of visualization techniques.
2017
-
Text Analytics and New Visualization Techniques
Text analytics have significantly advanced with techniques such as entity extraction and characterization, topic and opinion analysis, and sentiment and emotion extraction. But the visualization of text has advanced much more slowly. Recent visualization techniques for text, however, are providing new capabilities. In this talk we offer an overview of these new ways to organize massive volumes of text, characterize subjects, score synopses, and skim through a lot of documents. Together, these techniques can improve workflows for users focused on documents.
-
System and Method for Processing Map Data
-
System and Method for Data Visualization Using a Synchronous Display of Sequential Time Data and On-Map Planning
-
System and Method for Interactive Multi-dimensional Visual Representation of Information Content and Properties
-
System and Method for Processing Map Data
-
System and Method for Large Scale Information Analysis Using Data Visualization Techniques
-
System and Method for Interactive 3D Air Regions
-
System and Method for Interactive Visual Representation of Information Content and Relationships Using Layout and Gestures