Although there are already many projects of data visualization, why we want to/must engage make messy data into maps, charts, grids, or other elaborate pictures, is not clear enough — at least for me.
And the conclusion is that data visualization can build a bridge between humans and computers, complementing with each other.
1)How? The name itself has the key to understand it. So say “What is Data Visualization?”
In fact, “data” is everywhere — and everything you can perceive is data. But in this field, “data” mostly represent the digital inputs which can be properly formatted, parsed, and processed by algorithms, typically in the forms of JSON, csv, tsv and so forth. Many Infographics or other works of art are similar at glance, but lacking it.
Computers can deal with data automatically that way. That’s a great advantage. But disadvantage is that computers aren’t good at recognizing pattern or relationship between different data – algorithm has difficulty even discerning faces of people that humans are easily doing in everyday lives.
And computer can’t see anything but 0 or 1. They don’t need any visualization. And they probably don’t want to render any charts or maps, if had self-awareness; “Why must I bother to do that?”
So computers are clinging to “data”.
“Visualization” is a friend of us, humanity. We humans can find pattern by seeing picture. But the greatest feature is ability of making analogy. When you see a chart, you recognize not only patterns, but also analogic image: gravity, flood, building, traffic signal, day and night, are there implicitly. And analogy burst our brain to relate utterly different data type. Line chart may be the analogy came from shape of mountain or road of slope, and you can use the simple analogy to explain how GPD grows over time, or how atom turn into another one. Sophisticated analogy allows people to communicate easily, and different type of data to join it. That is the effort computers can’t.
Data visualization combine the human’s power of pattern recognition, with computer’s power of automation in this way: computer making big data into visual picture; then we find pattern; and join it to previously unrelated data; finally we can return the new dataset to computer – good feedback loop comes.
2)Data Visualization fills the gap of both humans and computer. Can we do that in reality?
I think a few examples are already existing in citizen science. Galaxy Zoo and Foldit are like gaming websites, providing users with a picture or a CGI image, and letting them play the recognition game. Galaxy Zoo is a project for classifying galaxies with blur pictures of space. It has been done by the use of citizen contributors, because this sort of pattern recognition is difficult for computer. A member of the team Kevin Schawinski told that “The human brain is actually much better than a computer at these pattern-recognition tasks,”
Foldit is similar. It aims to find the most fitted folding position of proteins, by the hands of users.
The two web applications offer data as the form where we can see patterns of them, tweek them, and end up with creating better data than before. Not exactly data visualization, but these are the sort of attempts I meant above.
Data visualization is not only art, but also data mining, making human and computer helping each other. I think this is one of the main purpose of making data visualization project.