We present a taxonomy of interactive
dynamics that contribute to success-
ful analytic dialogues. The taxonomy
consists of 12 task types grouped into
three high-level categories, as shown
in the accompanying table: data and
view specification (visualize, filter,
sort, and derive); view manipulation
(select, navigate, coordinate, and orga-
nize); and analysis process and prov-
enance (record, annotate, share, and
guide). These categories incorporate
the critical tasks that enable iterative
visual analysis, including visualization
creation, interactive querying, multiv-
iew coordination, history, and collabo-
ration. Validating and evolving this
taxonomy is a community project that

taxonomy of interactive dynamics for visual analysis.

Data and View Specification View Manipulation Process and Provenance

visualize data by choosing visual encodings. filter out data to focus on relevant items. Sort items to expose patterns. Derive values or models from source data. Select items to highlight, filter, or manipulate them. navigate to examine high-level patterns and low-level detail. coordinate views for linked, multidimensional exploration. organize multiple windows and workspaces. Record analysis histories for revisitation, review, and sharing. annotate patterns to document findings. Share views and annotations to enable collaboration. Guide users through analysis tasks or stories.

proceeds through feedback, critique, and refinement.

Our focus on interactive elements presumes a basic familiarity with visualization design. The merits and frail-ties of bar charts, scatter plots, timelines, and node-link diagrams, and of the visual encoding decisions that underlie such graphics, are certainly a central concern, but we will largely pass over them here. A number of articles and books address these topics in great detail, 4, 5, 20 and we recommend them to interested readers.

Within each branch of the taxonomy, we describe example systems that exhibit useful interaction techniques. To be clear, these examples do not constitute an exhaustive survey; rather, each is intended to convey the nature and diversity of interactive operations. Throughout the article the term analyst refers to someone who uses visual analysis tools and not to a specific person or role. Our notion of analyst encompasses anyone seeking to understand data: traditional analysts investigating financial markets or terrorist networks, scientists uncovering new insights about their data, journalists piecing together a story, and people tracking various facets of their lives, including blood pressure, money spent, electricity used, or miles traveled.

 

figure 1. visual encoding via drag-and-drop actions in tableau.

figure 2. examples of dynamic query filter widgets from Spotfire (left) and Google hotel Search (right).

Data and view Specification

To enable analysts to explore large datasets involving varied data types (for example, multivariate, geospatial, textual, temporal, networked), flexible visual analysis tools must provide appropriate controls for specifying the data and views of interest. These controls enable analysts to selectively visualize the data, to filter out unrelated information to focus on relevant items, and to sort information to expose patterns. Analysts also need to derive new data from the input data, such as normalized values, statistical summaries, and aggregates.

Visualize. Perhaps the most fundamental operation in visual analysis is to specify a visualization of data: analysts must indicate which data is to be shown and how it should be depicted. Within user interfaces, such visualization “widgets” are often presented in a chart typology, a palette of available vi-