“What is Visualization?”

“What is Visualization?” by Lev Manovich (2010)

Found at http://manovich.net/index.php/projects/what-is-visualization

“What is information visualization? […] So lets start with a provisional definition that we can modify later. Lets define information visualization as a mapping between discrete data and a visual representation. We can also use different concepts besides ‘representation,’ each bringing an additional meaning.”

Information Visualization–often abbreviated infovis–is a multidisciplinary field that concerns the representation of information or data mapped as visual elements. Strict definitions elude the infovis community because of the diversity of its contextual range. Computer Scientists often classify infovis in terms of interactive displays of data, but this is a narrow view for the potential of graphic form.

The difference between the scientific visualization community and the information visualization community differ moreso in technologies and techniques applied to visualizations, rather than the visualizations themselves. Scientific visualizations developed during the 80s, while 3D technologies were being created; Infovis developed in the 90s and 2000s, when the monitor was abstracted into its natural two dimensions and the rise of big data processing capabilities.

“Infovis uses arbitrary spatial arrangements of elements to represent the relationships between data objects. Scientific, medical and geovisualization typically work with a priori fixed spatial layout of the real physical objects such as a brain, a coastline, a galaxy, etc. Since the layout in such visualizations is already fixed and can’t be arbitrary manipulated, color and/or other non-spatial parameters are used instead to show new information.”

Information Design and Information Visualization differ mostly between data structures: known or undiscovered, respectively.

“By employing graphical primitives (or, to use the language of contemporary digital media, vector graphics), infovis is able to reveal patterns and structures in the data objects that these primitives represent. However, the price being paid for this power is extreme schematization. We throw away  %99 of what is specific about each object to represent only %1- in the hope of revealing patterns across this %1 of objects’ characteristics.”

Manovich suggests that a reductionist abstraction of data is “throwing it away,” but I would argue that is making the data implicit, or potentially obscuring it. There is a valid argument to be made that most infovis efforts are a reduction of the original, but it is a optimist-pessimist dichotomy that describes the nature of the debate: one sees it as an intensification or focusing on the information that is present, the other sees it as a hiding, or disregard of information no longer present.

For years now, the infovis community has privileged encoding of spatial variables (size, position, shape curvature, motion, etc.). This hierarchy places content emphasis on these spatial variables, rather than characteristic (color, texture, transparency) variables. Historically, one can see a similar hierarchy of variables in traditional schools for painting, in which sketches are laid out elaborately first, then shading and color is layered on only after such a spatial encoding has been decided upon. Psychologically and physiologically, the basis of object recognition is closely tied or relate to 2D scene analysis–a valuable result of identification, classification, and comparison that allows to thrive and survive.

“I think that this key of spatial variables for human perception maybe the reason why all standard techniques for making graphs and charts developed in the 18th – 20th centuries use spatial dimensions to represent the key aspects of the data, and reserve other visual dimensions for less important aspects.”

At the end of the 20th century, visualization without reduction became a style all its own. Tag clouds–or word clouds–were an early form of “direct visualizations” that utilized the media of text, and left it text, but offered a new notational system of value.

In Brendan Dawes’s Cinema Redux, frames from a film are made pixelated miniatures that are arranged in a matrix. This visualization method removes one from the experience of film, but presents a visual form that permits temporal pattern recognition. Here, the reduction occurs upon the import, and it is for this reason we can keep their likeness and resist the mapping onto visual primitives. The sampling pattern (of one frame per second) is not an act of reduction, as it is an act of sampling, that still has a one-to-one representation with the source material, but it only a representational fraction of the whole. Sampling should be acknowledged, but should not disqualify a visualization as a synecdoche and lacking nuance.

Direct visualization often utilizes sampling, but does not require it; advancements in interactivity can help to hide away the entirety of raw information, revealing it only when a user has a query or chooses to zoom. While not a requirement, structure and layout of time-based variables is appropriate; space can orchestrate an appreciation for temporally-experienced patterns.

“Thus, space turns to play a crucial role in direct visualization after all: it allows us to see patterns between media elements that are normally separated by time.”

In rethinking information visualization in the modern times, one may conclude the primary focus on spatial variables in visual encoding is a relic of technological limitations.

“I believe that direct visualizations method will be particularly important for humanities, media studies and cultural institutions which now are just beginning to discoverer the use of visualization but which eventually may adopt it as a basic tool for research, teaching and exhibition of cultural artifacts.”

The future promotion of direct visualizations in the humanities and media studies will promote the deeper understanding of meaning and it connections to patterns.



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