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DATA VISUALIZATION

By

S.V.V.D.Jagadeesh

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

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  • Unit-III Outcomes
  • Chapter Preface
  • Types of Systems
  • Systems Based on Data Types
  • Scientific Data
  • Multivariate Data
  • Graph Data

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Previously Discussed Topics

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

  • Understand the visualization systems based on analysis type (Understand-L2)

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Thursday, August 21, 2025

Session Outcomes

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Thursday, August 21, 2025

Systems Based on Analysis Type

  • Statistics
  • Spatio-Temporal

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S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Statistical Analysis Systems

  • GGobi is an interactive tool for multivariate data visualization and analysis developed by Deborah Swayne, Dianne Cook, and Andreas Buja in the early 90s while they were at Bellcore, Inc.
  • The system continues to evolve, with many others contributing to its development.
  • It supports a number of different visualizations, including scatter plots, scatterplot matrices, bar charts, graphs, and parallel coordinates.
  • For each visualization, a control panel specific to that view is shown; clicking on any visualization exposes its control panel.

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Thursday, August 21, 2025

Statistical Analysis Systems

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  • Color is used to link data between multiple views, and the user has a wide range of options for controlling the colors assigned to graphical entities.
  • The user starts by selecting a data dimension to control the color; an interactive histogram can then be used to adjust the ranges of values assigned to each color .
  • Options are available to either use uniform bin width or bin count to create the initial histogram.
  • All views of the data set will then use this color assignment.

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Thursday, August 21, 2025

Statistical Analysis Systems

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Thursday, August 21, 2025

Statistical Analysis Systems

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  • One of the most powerful tools within GGobi is the ability to generate and view so-called grand tours of the data, using a path through projection space to show the data from all views, or from user-constrained subsets of views.
  • Users can change the speed of the movement and pause it to examine features of interest, as well as the viewing parameters that generated the view.
  • Many other analytic tools have been added to GGobi over the years, including linkage to the R statistics package, support for several graph drawing techniques, imputation methods for handling missing values, and dimension reduction methods such as PCA and MDS.

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Statistical Analysis Systems

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DATA VISUALIZATION

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Thursday, August 21, 2025

Statistical Analysis Systems

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  • Macrofocus has produced a number of powerful interactive tools for visually exploring data and information.
  • One such tool is InfoScope,which links geographic views with several other visual and textual representations of information.
  • In the sample example shown below, information compiled by the United Nations on human development can be explored in a wide assortment of ways.

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Spatio-Temporal Analysis Systems

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Spatio-Temporal Analysis Systems

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  • The geographic view shows either a global or local view of the geographic components of the data set.
  • A bifocal fisheye lens is used to perform context-preserving zooming by shift-dragging the mouse.

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Spatio-Temporal Analysis Systems

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  • The theme view displays data points that have been laid out based on similarities using an optimized spring-based MDS algorithm.

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Spatio-Temporal Analysis Systems

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  • The data view can show the data either as parallel coordinates or in textual tables.

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Spatio-Temporal Analysis Systems

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DATA VISUALIZATION

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  • Session Outcomes
  • Systems Based on Analysis Types
  • Statistical Analysis Systems
  • Spatio-Temporal Analysis Systems

S.V.V.D.Jagadeesh

Thursday, August 21, 2025

Summary

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DATA VISUALIZATION