Interactive Dynamics for Visual Analysis

Jeffrey Heer and Ben Shneiderman have written a great paper titled Interactive Dynamics for Visual Analysis: A taxonomy of tools supporting fluent, flexible use of visualizations where they present a taxonomy of interactive dynamics that contribute to successful analytic dialogues, consisting of 12 task types grouped into three categories:

To enable analysts to explore large data sets involving varied data types (e.g., multivariate, geospatial, textual, temporal, networked), flexible visual analysis tools must provide appropriate controls for specifying the data and views of interest.

  • 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. (Chart typology, data-flow graphs, formal grammar, Tableau. Read more about Tableau)
  • Filter: Filtering of data values is intrinsic to the visualization process, as analysts rarely visualize the entirety of a data set at once. Instead, they construct a variety of visualizations for selected data dimensions. Given an overview of selected dimensions, analysts then often want to shift their focus among different data subsets— for example, to examine different time slices or isolate specific categories of values.
  • Sort: Ordering (or sorting) is another fundamental operation within a visualization. A proper ordering can effectively surface trends and clusters of values or organize the data according to a familiar unit of  analysis (days of the week, financial quarters, etc.)
  • Derive: As an analysis proceeds in iterative cycles, users may find that the input data is insufficient: variables may need to be transformed or new attributes derived from existing values. Common cases include normalization or log transforms to enable more effective value comparisons. Visual analytics tools should include facilities for deriving new data from input data.

Once analysts have created a visualization through data and view specification actions, they should be able to manipulate the view to highlight patterns, investigate hypotheses, and drill down for more details.

  • Select: Pointing to an item or region of interest is common in everyday communication because it indicates the subject of conversation and action. In visual analysis, reference (or selection) remains of critical importance, but it is realized through a more limited set of actions, such as clicking or lassoing items of interest.
  • Navigate: Large information spaces may require analysts to scroll, pan, zoom, and otherwise navigate the view to examine both high-level patterns and fine-grained details. Visualizations often function as viewports onto an information space. Analysts need to manipulate these viewports to navigate the space.
  • Coordinate: Many analysis problems require coordinated multiple views that enable analysts to see their data from different perspectives. By selecting a single item or a group in one view, analysts might see related details or highlighted items in the other views. This powerful approach to exploring multivariate data also enables drilling down into subgroups, marking sets, and exporting selections. Though comparing multiple visualizations requires viewers to orchestrate their attention and mentally integrate patterns among views, this process is often more effective than cluttering a single visualization with too many dimensions.
  • Organize: When analysts make use of multiple views they face the corresponding challenge of managing a collection of visualizations. As larger and multiple displays become more common, layout organization tools will become decisive factors in creating effective user experiences.

Visual analytics is not limited to the generation and manipulation of visualizations— it involves a process of iterative data exploration and interpretation.

  • Record: To support iterative analysis, visual analysis tools can record and visualize analysts’ interaction histories.
  • Annotate: Interactive visualizations often serve not only as data exploration tools, but also as a means for recording, organizing, and communicating insights gained during exploration. Analysts may wish to “point” to specific items or regions within a visualization and associate these annotations with explanatory text or links to other views. (Read more about data-aware annotations)
  • Share: Real-world analysis is also a social process that may involve multiple interpretations, discussion, and dissemination of results. The implication is clear: to support the analysis life cycle fully, visual analytics tools should support social interaction.

Now I have 60 new books and papers that I have to read.


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