by mahvish @ 9:18 pm 30 January 2012
Urban Remix
Urban Remix is a project aimed at allowing others to communicate the soundscape of their communities. The platform consists of a mobile phone and web interface for recording, browsing, and mixing audio sounds together. Although, the creators of the project do not identify this project as a visualization, I find that I can listen to the audio tracks to get a sense of the rhythm of the city or community they were recorded in: noisy vs quiet, trains vs birds. The project creators call this the acoustic identity of these communities. I think this might be something that’s missing when we’re looking/exploring a place on Google Maps, for example. One way this could be improved is to mash it using data associated with these soundscapes or make these soundscapes tangible. The audio clips are already laid out on a map, but there might be other information like timestamps that could be used. Acoustic Poetry (link) is one attempt at making soundscapes tangible: soundscapes are interpreted and mapped to poetry that are displayed to users (the primary target user is deaf).
Project Fühlometer
I’ve heard (but not seen) that the Gulf Building downtown (
link) has a beacon on the top that forecasts Pittsburgh’s weather: orange for fair weather, blue for rain. I think a giant emoticon installation that displays the mood of a city is a much better visualization and would be an awesome use of that space. This project uses face recognition software to recognize the “moods” of passer-bys from a strategically placed camera. With geo-tagged twitter feeds, I don’t think we need face recognition software: we can just do a streaming emoticon search to determine the mood of a place, or a set of places.
[youtube=https://www.youtube.com/watch?v=nBstJ6_HMac]
Shoplifters of the World Unite
Data from the Global Retail Theft Barometer was visualized in this infographic. Who knew that shaving products and cheese were one of the top shoplifted items? Apart from the interesting data, I thought the poster itself was presented well visually. I think retail loss is specific, so a lot of the information so it should be expected that shoplifting is one of the larger piece of the retail loss pie. That might not jump out immediately because initially I had associated retail loss with loss for the retailer (as a whole): not knowing how big of a pie, retail loss is, I can’t understand the impact of shoplifting in that bigger picture.
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