109 research outputs found
On Regulatory and Organizational Constraints in Visualization Design and Evaluation
Problem-based visualization research provides explicit guidance toward
identifying and designing for the needs of users, but absent is more concrete
guidance toward factors external to a user's needs that also have implications
for visualization design and evaluation. This lack of more explicit guidance
can leave visualization researchers and practitioners vulnerable to unforeseen
constraints beyond the user's needs that can affect the validity of
evaluations, or even lead to the premature termination of a project. Here we
explore two types of external constraints in depth, regulatory and
organizational constraints, and describe how these constraints impact
visualization design and evaluation. By borrowing from techniques in software
development, project management, and visualization research we recommend
strategies for identifying, mitigating, and evaluating these external
constraints through a design study methodology. Finally, we present an
application of those recommendations in a healthcare case study. We argue that
by explicitly incorporating external constraints into visualization design and
evaluation, researchers and practitioners can improve the utility and validity
of their visualization solution and improve the likelihood of successful
collaborations with industries where external constraints are more present.Comment: 9 pages, 2 figures, presented at BELIV workshop associated with IEEE
VIS 201
Data-First Visualization Design Studies
We introduce the notion of a data-first design study which is triggered by
the acquisition of real-world data instead of specific stakeholder analysis
questions. We propose an adaptation of the design study methodology framework
to provide practical guidance and to aid transferability to other data-first
design processes. We discuss opportunities and risks by reflecting on two of
our own data-first design studies. We review 64 previous design studies and
identify 16 of them as edge cases with characteristics that may indicate a
data-first design process in action
Reflections on QuestVis: A Visualization System for an Environmental Sustainability Model
We present lessons learned from the iterative design of QuestVis, a visualization interface for the QUEST environmental sustainability model. The QUEST model predicts the effects of policy choices in the present using scenarios of future outcomes that consist of several hundred indicators. QuestVis treats this information as a high-dimensional dataset, and shows the relationship between input choices and output indicators using linked views and a compact multilevel browser for indicator values. A first prototype also featured an overview of the space of all possible scenarios based on dimensionality reduction, but this representation was deemed to be be inappropriate for a target audience of people unfamiliar with data analysis. A second prototype with a considerably simplified and streamlined interface was created that supported comparison between multiple scenarios using a flexible approach to aggregation. However, QuestVis was not deployed because of a mismatch between the design goals of the project and the true needs of the target user community, who did not need to carry out detailed analysis of the high-dimensional dataset. We discuss this breakdown in the context of a nested model for visualization design and evaluation
A Nested Model for Visualization Design and Validation
Abstract-We present a nested model for the visualization design and validation with four layers: characterize the task and data in the vocabulary of the problem domain, abstract into operations and data types, design visual encoding and interaction techniques, and create algorithms to execute techniques efficiently. The output from a level above is input to the level below, bringing attention to the design challenge that an upstream error inevitably cascades to all downstream levels. This model provides prescriptive guidance for determining appropriate evaluation approaches by identifying threats to validity unique to each level. We also provide three recommendations motivated by this model: authors should distinguish between these levels when claiming contributions at more than one of them, authors should explicitly state upstream assumptions at levels above the focus of a paper, and visualization venues should accept more papers on domain characterization
Tugging Graphs Faster: Efficiently Modifying Path-Preserving Hierarchies for Browsing Paths
International audienceMany graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the underlying structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input
TugGraph: Path-Preserving Hierarchies for Browsing Proximity and Paths in Graphs
International audienceMany graph visualization systems use graph hierarchies to organize a large input graph into logical components. These approaches detect features globally in the data and place these features inside levels of a hierarchy. However, this feature detection is a global process and does not consider nodes of the graph near a feature of interest. TugGraph is a system for exploring paths and proximity around nodes and subgraphs in a graph. The approach modifies a pre-existing hierarchy in order to see how a node or subgraph of interest extends out into the larger graph. It is guaranteed to create path-preserving hierarchies, so that the abstraction shown is meaningful with respect to the structure of the graph. The system works well on graphs of hundreds of thousands of nodes and millions of edges. TugGraph is able to present views of this proximal information in the context of the entire graph in seconds, and does not require a layout of the full graph as input
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