VISUALISATION

The invisible has more functionality nowadays. We are required to process huge amount of data silently passes us all the time. Thus visualization has become a very powerful tool that simplifies many tasks in our lives. As the name suggests, the term itself implies a descriptive concepts that is to make the invisible visible. Although the term conveys a technical sense, practical examples can be easily found around us. They can be as simple as those symbols instruct how to use a product (i.e. image of a dash line with a scissors at one end means ‘cut here’ (Timo, 2006)). More sophisticated visualizations would be a financial graph of stock exchange movements or an x-ray image reflecting our inside bodies. Therefore, visualization is a production of data-based images which are abstracts of some theories. It is a sensory signal of invisible phenomenon. One of the most important applications of visualization is pattern recognition. Since visualization represents a replicating set of data, graphics routines can be applied with temporal accuracy (http://impromptu.moso.com.au). Once a particular pattern is exploited, professionals are more equipped in discovering the roots of problems currently facing. Thus approaches to fix / to improve are more apparent. Images that show how 200 calories looks like (Infosthetics, 2007) would help us to adjust our eating habits to have healthier diets.  In addition, the effectiveness of those visualizations relies on its interactivity with audiences (Gates, 2008). The visualization should not only engage with audiences, but also with history, technology and other creators. Successful example would include the Christian Aid ‘History of Poverty’ chart. This is a sophisticated 3D world map that reveals the development of countries over the last few hundred years in terms of poverty. The representation is appealing because interactions with the data are enabled by an annotated timeline (Infostethics, 2010). On the other hand, it must also be warned that those interactive projects do not guarantee the completed demonstration of any particular issue (Fredbeig, n.d.). Archrival data only tells stories of the past to predict possibilities in the future. But the accuracy is ambiguous. Therefore, users should be aware in insisting any predictions relied on those visualizations. Further research about the relationships of variables represented should be followed to assure its truthfulness.

References

Editors and Friedberg, Anne (2007) The Virtual Window Interactive Vectors [Online]. Available at http://www.vectorsjournal.org/index.php?page=7&projectId=79

Gates, Carrie (2009) Vague Terrain 09: Rise of the VJ [Online]. Available at http://vagueterrain.net/journal09

Arnell, Timo (2006) The dashed line in use [Online]. Available at  http://www.nearfield.org/2006/09/the-dashed-line-in-use

Impromptu (n.d.) What is impromptu [Online]. Available at http://impromptu.moso.com.au/

Information aesthetics (2011) A History of Poverty: Charting International Development over Time [Online]. Available at http://infosthetics.com/archives/2011/03/a_history_of_poverty_charting_international_development_over_time.html

Information aesthetics (2007) How does 200 calories look like? [online]. Available at http://infosthetics.com/archives/2007/01/how_does_200_calories_look_like.html

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