DATA VISUALIZATION

By

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S.V.V.D.Jagadeesh

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

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LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

• Session Outcomes
• Modern Integrated Visualization Systems

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Previously Discussed Topics

LBRCE

DATA VISUALIZATION

At the end of this session, Student will be able to:

• Understand the Interaction Operators(Understand-L2)

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Session Outcomes

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Interaction Concepts

• Interaction within the data and information visualization context is a mechanism for modifying what the users see and how they see it.
• Many classes of interaction techniques exist , including:
• navigation—user controls for altering the position of the camera and for scaling the view (what gets mapped to the screen) such as panning, rotating, and zooming.
• selection—user controls for identifying an object, a collection of objects, or regions of interest to be the subject of some operation, such as highlighting, deleting, and modifying.
• filtering—user controls for reducing the size of the data being mapped to the screen, either by eliminating records, dimensions, or both

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Interaction Concepts

• reconfiguring—user controls for changing the way data is mapped to graphical entities or attributes, such as reordering the data or layouts, thereby providing a different way of viewing a data subset.
• encoding—user controls for changing the graphical attributes, such as point size or line color, to potentially reveal different features.
• connecting—user controls for linking different views or objects to show related items.
• abstracting/elaborating—user controls for modifying the level of detail
• hybrid—user controls combining several of the above in one technique, for example, increasing the screen space assigned to one or more focus areas to enable users to see details, while showing the other areas of data in a smaller space, in a way that preserves context

LBRCE

DATA VISUALIZATION

• Navigation Operators
• Selector Operators
• Filtering Operators
• Reconfiguring Operators
• Encoding Operators
• Connection Operators
• Abstraction/Elaboration Operators

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Interaction Operators

LBRCE

DATA VISUALIZATION

• Navigation (also sometimes referred to as exploration) is used to search for a subset of data to be viewed, the orientation of this view, and the level of detail(LOD).
• The subset in question may be one that is recognized by some visual pattern or one on which further or more detailed exploration is desired.
• In a typical three-dimensional space, this can be specified using a camera location, a viewing direction, the shape and size of the viewing frustrum, and an LOD indicator.
• In multiresolution visualizations, LOD changes can correspond to drilling down or rolling up hierarchical representations of the data.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Navigation Operators

LBRCE

DATA VISUALIZATION

• Navigation operators can work in absolute or relative coordinates within their particular spaces.
• Incremental navigation may have different granularities, depending on whether the user wants a small or significant change.
• Navigation can be user-driven or automatic; a good example of automated exploration is the grand tour, where multidimensional data is explored by flying along a path that smoothly covers many or all possible orientations of the data space, as projected onto two dimensions.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Navigation Operators

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Navigation Operators

LBRCE

DATA VISUALIZATION

• In selection, the user isolates a subset of the display components, which will then be subjected to some other operation, such as highlighting, deleting, masking, or moving to the center of focus.
• Many variations on selection have been developed to date, and decisions need to be made on what the results should be for a sequence of selections.
• The granularity of selection is also an issue.
• Clicking on an entity in the display might result in selection of the smallest addressable component (e.g., a vertex or edge) or might target a broader region around the specified location (e.g., a surface, region of the screen, or object)

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Selection Operators

LBRCE

DATA VISUALIZATION

• Selection can be articulated in many different ways.
• The user may click on entities, paint over a selection of entities (e.g., holding the mouse button down while moving over the entities of interest), or otherwise isolate the entities via techniques such as bounding boxes and lassoes.
• Finally, selections may be performed in an indirect manner, where the system selects elements that match a user’s input set of constraints

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Selection Operators

LBRCE

DATA VISUALIZATION

• Filtering, as the name implies, reduces the volume of data to be visualized by setting constraints specifying the data to be preserved or removed.
• One- or two-handled sliders are used to specify a range of interest, and the visualization is immediately updated to reflect the changes made by the user.
• Range queries are just one form of filtering, however.
• One might also select items from a set or list to preserve or hide, such as the column hiding operation in Excel.
• The distinction between filtering and selection followed by deletion or masking is a subtle, but important point.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Filtering Operators

LBRCE

DATA VISUALIZATION

• Filtering, in general, is most often done in an indirect manner, e.g., the filter specification is not performed on the data visualization itself, but via a separate interface or dialog box.
• In fact, filtering is often done prior to viewing the data, to avoid overloading the data display.
• Selection is most often done in a direct manner, by indicating objects on the visualization via mouse motions

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Filtering Operators

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Filtering Operators

LBRCE

DATA VISUALIZATION

• Reconfiguring the data within a particular visualization can often be used to expose features or cope with complexity or scale.
• By reorganizing the data, say by filtering some dimensions and reordering those that remain, different views are provided to the user.
• For example, a powerful tool with table-based visualizations is to sort the rows or columns of the data to highlight trends and correlations.
• Other types of reconfiguration might be to change the dimensions being used to control the x- and y-coordinates of a plotted marker, or even to derive positions based on transformations of the data.
• Popular instances of this include the use of principal component analysis (PCA) or multidimensional scaling (MDS) to layout data points so that relationships among all the dimensions are best conveyed in the 2D layout.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Reconfiguring Operators

LBRCE

DATA VISUALIZATION

• Any given data set can be used to generate countless different visualizations.
• Recoding can provide the user a library of possible different types of visualization features of the data that are difficult or impossible to see with one such mapping might become quite apparent in another.
• For example, a scatterplot with one axis representing years may have many points overlap, whereas a parallel coordinate display would represent these uniquely.
• Many visualization tools today support multiple types of visualization, because no single visualization is effective for all tasks that need to be carried out by the user.
• Each visualization is most suitable for a subset of data types, data set characteristics, and user tasks.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Encoding Operators

LBRCE

DATA VISUALIZATION

• While some work has been done to identify or select the best visualization, it is apparent that such guidelines are at best suggestions, and the analyst is most likely to benefit from examining their data using a number of different mappings and views.
• Other forms of encoding operations include those that modify the color map used, the size of graphical entities, and their shape.
• These can be considered variations within a particular type of visualization, and can be used to emphasize or reveal features of interest.
• Even limitations of some visualizations can be overcome using variations.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Encoding Operators

LBRCE

DATA VISUALIZATION

• The overlapping issue in scatterplots where occluded points are not visible can be overcome by jittering the points or making the size of the points reflect the number of points at that same position.
• Other attributes of graphical entities that can be controlled include opacity, textures, line or fill style, and dynamic attributes such as fade or flashing rate.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Encoding Operators

LBRCE

DATA VISUALIZATION

• A frequent use for selection operations is to link the selected data in one view to the corresponding data in other views.
• While other forms of connection between subwindows of an application exist, such as when opening a new data file, linked selection is probably the most common form of communication between windows found in modern visualization tools.
• Its popularity stems in large part from the fact that each view of one’s data can reveal interesting features, and that by highlighting such a feature in one view, it is possible to build a more complete mental model of the feature by seeing how it appears in other views.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

• This can also help reveal relationships between this feature and others in the data set.
• For example, when examining multivariate spatial data, it is often useful to jump between the spatially referenced view and the dependent variable view, which often does not preserve the spatial attributes.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

• When the selection data is allowed to be interactively changed, the operator is called brushing, in which case the user is continuously changing the selection in one view, and the corresponding linked data in one or more other views is highlighted.
• The resulting interactive and dynamic display provides information about the changes in values in the linked displays.
• Another strength of linked brushing is in specifying complex constraints on one’s selection.
• Each type of view is optimized for conveying certain types of information, as well as for specifying conditions on particular types and with a particular degree of accuracy.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

• Thus, for example, one might specify a temporal constraint using a visualization containing a timeline, a constraint on a name field using a sorted list view, and a geographic constraint using a map.
• While each is effective as a tool for accurate and intuitive specification of a part of a query, none could be used for the complete query.
• In some situations, the user may want to unlink some visualizations in order to maintain a given view while exploring a different area of the data or different data set.
• Some systems allow the user to indicate for each window whether it is transmitting information to other views, and from which other windows it will receive input.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

• A user may also want to constrain the type of information being communicated, as well as its direction.
• Some types of interaction may be local to a particular window, e.g., zooming in and out, while others are meant to be shared, such as reordering dimensions.
• Also, in some situations, such as with hierarchically related windows, it may make more sense for the information to move from parent to child, but not the other way.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Connection Operators

LBRCE

DATA VISUALIZATION

• In dense data and information displays, it is often desirable to focus in on a subset of the data to acquire details (elaboration) while reducing the level of detail (abstraction) on other parts of the data set.
• One of the most popular techniques of this type is using distortion operators. While some researchers classify distortion as a visualization technique, it is actually a transformation that can be applied to any type of visualization.
• Like panning and zooming, distortion is useful for interactive exploration.
• Many distortion operators (also called functions) have been proposed in the past which include methods that distort the entire space being analyzed, and others that have more localized effects.

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Abstraction/Elaboration Operators

LBRCE

DATA VISUALIZATION

• The distortion may take place within the original visualization, or may appear in a separate window.
• Distortions vary in the features that are preserved and the amount of context maintained.
• For example, text distortion techniques strive for readability within a small region of interest, with the rest of the text positioned to reinforce document structure, but not generally readable.
• For other types of distortion, it is important that the undistorted and compressed regions continue to convey useful information, while details are provided in the focus area.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Abstraction/Elaboration Operators

LBRCE

DATA VISUALIZATION

• Distortion operators may be linear or nonlinear, with 0th, 1st, or 2nd order continuity (discontinuous operators are also possible).
• Operators may also operate on structures, rather than on continuous spaces, and thus may be specific to a particular type of operand (see the next section for details).
• Different operators have different footprints, e.g., the shape and extents of the space affected by the transformation.
• Common footprint shapes include rectangular and circular, and their analogous hyperboxes and hyperellipses for higher dimensional spaces.

S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Abstraction/Elaboration Operators

LBRCE

DATA VISUALIZATION

• Session Outcomes
• Interaction Concepts
• Interaction Operators
• Navigation Operators
• Selection Operators
• Filtering Operators
• Reconfiguring Operators
• Encoding Operators
• Connection Operators
• Abstraction/Elaboration Operators

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S.V.V.D.Jagadeesh

Tuesday, September 16, 2025

Summary

LBRCE

DATA VISUALIZATION