IMAGE INTERFACE and DATA VISUALIZATION

 

We approach data visualization as a kind of contemporary image. If we are concerned with the graphical representation of data, we can identify four typical visual models: Cartesian graphs, network diagrams, cartography and experimental models, tree maps, flowcharts, Voronoi diagram.

 

The variety of information to be visualized; Dates, places, people, financial transactions, social exchanges, personal preferences, connection times, etc. includes quantification.

 

A data visualization image regularly combines graphic elements (lines, dots, colors) organized in some form (a technical or visual model). It is enriched with elements that help communication and understanding. (texts, legends, pictures, figures)

 

Analog images are digitized and become databases.

 

We have a wide variety of tools available for the generation and dynamic exploration of data visualization images. 

 

These; (Software like Tableau and Gephi, as well as JavaScript libraries for the web like d3.js and sigma.js, among others.) The images highlight their interactive nature.

 

 

We can now perform several actions on any element or set of graphics: select, click, move, hide, group, color, animate, etc.

 

Now we can talk about display interfaces as they have gained software-specific features.

 

They virtualize data representation possibilities according to the potential of the computer environment. 

 

In this sense, a data visualization image can be understood as a medium: it presents a vision of the world with its metaphors (aesthetics, ethics, politics), operations (algorithms), and the ability to evolve and change.

 

Viewing Visual Media

 

Historically, we attach great importance to images. On the one hand, devices for their acquisition and visualization have become ubiquitous: portable devices, satellites, surveillance cameras, displays, sensors.

 

On the other hand, images that previously existed only in an analog format have become digitized. 

 

They have become public databases: books, catalogues, pictures, photographs on paper, 35 mm films, etc. In both cases, digital images involve the processing of visual features.

 

Indeed, an image can be identified by its metadata (author, date, place, production technique) and recognizable figures (people, objects) on its surface. But when we talk about visual properties, we refer to qualities and plastic parameters. (colors, shapes and textures).

 

In the encounter of plastic semiotics and computer science, it is user interfaces that enable conventions of human-machine interaction to manipulate them.

 

Buttons, sliders, toolboxes, and panels provide access to the image's chromemes (hue, saturation, brightness) and shapes (size, position, orientation). It is important to look at the current typology of possible actions on visual features.

 

We observe the result of the scientific and technological tradition in the analysis, extraction, classification, research and restoration of digital images: increase contrast, correct tones, add filters, resize, crop, compress, etc.

 

The challenge visual media poses to data visualization models is that they are graphic objects. So, we can talk about visualizing visual media.

 

 

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This can get complicated. Even though they belong to the same production, it is difficult to represent multiple images as they share a common feature (same author, same technique, same date, etc.).

 

Visual media is therefore a matter of incorporating a data visualization image as an intrinsic element.