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Data Vitualization 3

Page history last edited by Kanika Goenka 8 years, 12 months ago

Tian Qiu / Secondary editor: Cindy Lee / Tertiary editor: Kanika Goenka

 

Data Visualization

Overview

       Data visualization is now widely used in areas like science, engineering, biology, finance, media, entertainment,etc. The use of visualization to present data and related information is popular in many industry because it communicates with people more directly and efficiently. Compare with the traditional visual aid of diagram, visualization is more open in a sense that it does not presume the connections among data, rather it represents data in an illustrational way which encourages flexible and various interpretations from the viewers’ side.

 

Definition

     Any visual rendering of data, even the text in a terminal window, is an example of data visualization.      

 

Cause

     The need for visualization of data became apparent in 1980s as the digital storage became popular and the complexity of data found means to be managed, expressed, and understood for its viewers. While the material operations of software and data processing are objective and describable means of data representation, these methods are not quite directly accessible to viewers. In theory, visualization also adds some information about the composition of the raw/uninterpreted material, therefore, the algorithmic depends on the source of data while the perception of data is independent from the algorithm. Visualization is a mean of representation that is easily accessible (naturally acquired skills such as seeing/viewing and data collection in both conscious and unconscious ways) to viewers.

 


Characteristics/ Advantages

  1. visualization is able to represent large amounts of data

  2. show emergent features like patterns and structures

  3. compare small scale and large scale features simultaneously

  4. can detect mistakes/errors that are made during the gathering of data

  5. suggest more reasonable hypotheses

  6. efficient as it does not require special knowledge about computing and directly extract information at a glance

  7. are open to perceptual inference 
  8. knowledge is created through memory and latent imagination
  9. derives sensory expressions from the structures implicit in digital data 

 


Key Figures

  1. Edward Tufte, Early writers on visualization

Tufte developed basic guidelines for information graphic design. He suggests that an information graphic should “let the data speak for itself” without showing any unnecessary graphical ornamentation.

 

    2. Jacques Bertin, Semiologist

During 1970s, Bertin focused on studying how to organize a visualization that can both show the characteristic and relations between data.

 

   3. Chaomei Chen, Visualization scientist

In 2004, Chen suggests in one of her article that visualization is still defined as an art rather than a science, which make people reexamine the function and nature of data visualization.

 

  4.   Colin Ware, Data Visualization specialist

Redefine data visualization as a scientific discipline by relating it to the fields of physiology, human perception and cognitive studies. This idea later brought more cognition-related discussions on visualization, such as, “computational offloading”, “automatic processing”, etc, which brings up the hope of realizing “universal understand without training” through the use of visualization.

 

 

 

Works Cited

Wright, Richard. Software Studies/A Lexicon. Ed. Matthew Fuller. Cambridge: MIT, 2006. 78-86. Print.

 

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