Visualizations of My Interest

The reason why I’ve picked such two visualizations is because, at a glance, they looked pretty cool and profound, and both of them are very interesting ones for me





For the first one, the Musicovery, it provides a two-dimensional plane to represent two different aspects of the music in the library: mood, either a dark or a positive one, and the pace, either an energetic or calm one, of the music. Besides, they assigned some color to the music to represent the genre of it. The most interesting feature is that, by clicking on the places that represents different combinations of mood and pace, it would shows up a list of music that best represent such combination. Just as its name shows, it could help the user discover new music.





The second one is a static visualization about Oscar winning actors. It shows the directors of the movies in which the actors get their prize for, and other non-Oscar winning actors that have worked with the directors. Interestingly, it is somehow a tree diagram: with the very top as the Oscar-winning movie, followed by their directors, Oscar-winning actors, and actors worked with them.  As aforementioned, it seems to be very beautiful and cool visualization. But nonetheless, it sufficiently demonstrate the question that motivate this visualization: the relationship was between directors, Oscar winning actors and non-Oscar winning actors to see if they could find patterns in who actors work with. As the figure clearly shows, that only few of the outermost names have lots of connections with the inner names. However, unlike the pervious one, where the viewer, this one only serves for its purpose, and it is very hard to view it in another perspective; whereas the first one, as one could click on somewhere to get a detailed music that represents the combination of mood and pace, different aspects could be obtained from it, such as how rock spread on the mood-pace plane, or how is the popularity trend of different music in different time. Also, its main aim is to help user to explore the data: the music, wheras the first one is aming to provide the relationship between Oscar prize and the directors and actors.


Beauty #1, Two visualizations that catch me

Romeo & Juliet



When we talk about visualization, we are talking about beautiful pictures. They may be useful or i.e. not only beautiful pictures. But I think as a good visualization, beautiful and well-design are prerequisites. Therefore the first visualization I choose is a visualization of Romeo and Juliet. It is a massive relation graph between Romeo and Juliet. Artist prints Romeo and Juliet in a large paper and connect “Romeo” and “Juliet” with red lines. It is the prettiest static visualization I saw in visual complexity. Red lines represent love and a feeling of massiveness makes me feel the massive love between Romeo and Juliet. I love this because it is not pure data accumulation. The idea reflects a kind of concise design pattern. This visualization offers me only an impression but it can let me interpret something beyond. First of all I will say is that “Romeo and Juliet’s love is massive and a little bit heavy”. Then from the density of color changing in the text, we can interpret their love story from crashing to tragedy ending. As a static visualization, It successfully provides a snapshot/screenshot of a limited time and space. Compared to the original text, which is telling the actual story, It gives a more direct shock to readers/audiences about love. Though we still need to admit that the information is not big enough in amount to let us know everything.

Another example is a dynamic visualization website that deals with real time twitter post. I choose this again, HumanitysNAPbecause of its beauty.Twitter orographies visualizes twitter posts’ word with other high-frequency word connections and show them on a very well designed dynamically website. It is a very interesting product to use. With a single word, we can find how most people in twitter’s way to interpret it and use it. I will stop talking about how good it is now because the potential of this website is very obvious. Instead, I would like to talk about 2 limitations behind the beauty. 1:The low frequency word. If I type in “Porn”, “Digital Humanity”(sadly it is a low frequency word). I get very boring result with no big words (other high frequency words) connected. 2: limitation of “real time”. In real time, It is somehow useful but it is also a limitation in time. For example, I typed in Humanity, It connects with refugees, Germany and ISIS. It should be a result of today’s refugee crisis in Europe. However, humanity in general should connects with DIGITAL. 🙂



Complexity Without Perplexity: The Functional Visualization



Musicovery combines two online music suggestion platforms (MusicPlasma and Pandora) into a visual music discovery system and internet radio. It does so by taking the music information and connection system created by Pandora and combining it with MusicPlasma’s novel interface for music discovery. What you get is a new system in which users can choose different musical categories as well as moods spatially on a map of all songs that fall into your current category. The user selects a central node to work off of, and Musicovery selects songs that fall within the proximity of your chosen node based off song popularity and mood.

I chose this visualization because I honestly found it really useful, even aside from the assignment itself. I’ve been listening to the service for the past hour and have already found new enjoyable music that I’ve never heard before.

It also serves as a great example of a dynamic visualization of data, allowing the user to select completely different data sets to predict from. This allows for the user to discover new music in proximity to songs that they like in order to broaden their musical horizons. As well, viewing favorite songs from a perspective of mood and genre all in relation to each other presents a unique new perspective on the data itself.



World of Music

World of Music was created by mining data from the Yahoo Music service and using it to create and map a network of connections between various artists found on the service. Artists that are more popular or have shared listeners are mapped according to space in relation to each other and color of popularity.

I found this visualization very interesting because of its immediate beauty but ultimate failure at doing that much with it. It is essentially a static visualization, only allowing the user to view the network as a whole and the ability to zoom in on certain parts of the network to see more detailed information. So, while it does make for a nice display and gives an interesting look at artist relationships, it doesn’t allow the user to do much than admire how pretty it looks.