[Data Art] Lost in Translation

As the final project for the class, Data Art, I and Ellen are teamed up and were discussing about the project through the mobile messenger. While we were having conversation in Korean, we found out we were using lots of ‘ㅋ’ as most Korean does.

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Naturally the discussion went to emoji, another kind of abstract, compact literation widely used in mobile. We are using it unconsciously everyday, but we might not perfectly understand the meaning of it, even though it is supposed to mean certain thing. We thought there’s complicated emotion behind these kinds of literation in mobile, leading ‘lost in translation’. We decided to analyze the ‘ㅋ’ as a prototype project, collecting and representing data of emotion behind it.

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‘ㅋ’ is originally literation of sound of laughter, something similar with ‘kekeke’ in English. But people use it far beyond just laughter, each person using it differently. There’s even joke about different meaning according to the number of it.

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We wanted to find undertone of this letter, so we did a self-analysis to see emotion behind this letter by breaking down into several emotion from joy to fear. The criteria we picked is a standard in computer based emotion analysis. But it’s done by us, human being. The process of this human analysis was interesting to both of us, as we felt there’s misinterpretation when we did the each of own project with the computerized sentimental analysis.

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We also did a mutual analysis. Each letter was analyzed by myself first, and then also by opponent again so that we could figure out difference between intention of sender and interpretation of receiver.

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We set rules, guidelines of visualization, how to represent the emotion. The amount of each emotion decides each typographical feature of it, which are size, width, weight, and shear. Base on the result of analysis, each letter had it’s own unique form. In doing so, we wanted to embed complicated emotion in the letter itself.

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Below is the summary of the result. Every letter has distinctive form, different from each other. We found several interesting take away from the result. 

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First, as we guess before we start, there’s indeed lost in translation. There is huge gap between the intention and the interpretation. Secondly, it actually reflects the personality of one who uses it. Almost every form of my letter is quite similar no matter what the context of the phrase is. We can assume each person use this letter with similar emotion regardless of the context. We assured this little theory once again when we saw the result in a cross way. For example, my analysis of my own word is similar with my interpretation on Ellen’s word, meaning that I used this letter and also recognized this letter in my own way with certain emotion. The similarity of form didn’t depends on the context but rather on the person. So our conclusion of this weird experiment is that this letter contains quite diverse emotion behind it, and it’s mainly affected by each one’s personality.

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We thought it makes sense if it is made into some kind of accessories as it strongly show the personality of one who use it. We prototyped it by doing a laser cut to make elements of design of any kind of thing to represent the personality. This small prototype experiment between two of us would be extended to large number of people, so more meaningful or interesting results might be unfolded in the next step. 

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[Data Art] 3rd assignment – UNTAKEN

Thanks to Google Street view, we could explore the almost entire world in my own room only if I have a mobile phone. Before I came to NYC, I already experienced the central park’s great lawn. But is it really the central park? A 360 view is enough to seem to be like real but not enough to be real. I can see that place, but don’t feel or experience it. I kind of dislike Google Street View, because it stop us exploring more places physically.

When I played with Google’s Street View API, I found out there’s plenty of location points that are not taken by Google even in the big city like New York. In fact, I was glad to see the screenshot of ‘no result’. At least there’s reason for us to go that place to actually to experience and feel the place.

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I made a simple tool to find untaken places using Google Street View API conversely. When you pick one city, it will randomly find you one of the place without the street view image within the city. As a prototype, I chose 6 cities from highly developed cities like New York expected not to have any single untaken point, which was not true, to barren or infamous? cities such as Bagdad where we hardly expect for Google to take photo of it.

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Dutchman_in_telescope

In order to make it feel like seeing the untaken place from distance before exploring, the zoomed satellite image appears as if pirates uses a telescope. Of course, the satellite image is also digitized one, but at least it triggers some amount of curiosity for us to go there physically.

While exploring the results, I somewhat relieved there’s still lots of places that is not digitized so that it is yet to be defined in single photo with one instant moment.

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project website

source code 

 

[Data Art] 2nd assignment – ARTicle

Articles in news website is text that we most frequently see in everyday life. It’s primarily regarded as source of objective information, but there’s strong, subjective emotion that comes out very often, especially these days of huge confliction between the media and the government. Articles sometimes seem to go beyond just text, but rather sort of an artwork filled with powerful, complex feelings. ARTicle is an chrome extension that helps emotional appreciation of an article while you are reading a news in NYT.

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source code

[Data Art] 1st assignment – U.S. Immigrants data visualization

Staring with a question, ‘how many immigrants are there in U.S?”, I found an interesting figure showing the total immigrant population only accounts for 13.3% of the total population of U.S. I was surprised by the relatively small number than I expected.

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As I hate bar chart that always distorts reality while compressing data too much to represent it simply, I tried to reinterpret the figure, ‘13.3%’ in a different way. With the methodology below, every group of 10,000 immigrants becomes 1 rectangle with 5px X 5px and is scattered into a grid consisted of almost 31890 cells representing the total number of U.S population. Because we live everywhere.

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Below is the reinterpreted scattered chart of showing the rate of immigrants in U.S., which is now more plausible to me like the former president said. “We are and will be a nation of immigrants.” Unfortunately, I couldn’t figure out how to prevent each rectangle to be overlapped each other, meaning that it definitely should be even denser than now.

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The source code