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Thursday, March 4, 2010

alpha version of new Cultural Analytics software

We are happy to announce the alpha release of a new Cultural Analytics software.

It was developed in Multimodal Analysis Lab at National University of Singapore (director: Kay O’Halloran) in collaboration with Software Studies Initiative at Calit2/UCSD (director: Lev Manovich).

Conceptual design: Lev Manovich (Calit2/UCSD), Bertrand Grandgeorge (NUS), Jeremy Douglass (Calit2)
Programming: Bertrand Grandgeorge.



cultural analytics chart selection 2
Screenshot: Exploring a set of 450 Time covers (sampled from the complete set of 4553 cover, 1923-2009 by taking every 10th image). The images with red frame around them are the covers representing women - selected by using a bar chart (bottom left corner).


cultural analytics chart selection 3
Screenshot: Exploring a set of 450 Time covers (sampled from the complete set of 4553 cover, 1923-2009 by taking every 10th image). Mousing over points reveals larger images and metadata.


Our software is developed in Flash/Flex so it can run on any PC or MAC. Some of its distinctive features:

- software supports all standard graph types - however if standard graphing/visualization application represent data as vector primitives, our graphs can also show the actual images. For instance, you can make a scatter plot where images are placed on top of the points. You can change the size of images at any point; mousing over reveals the larger image.

- it supports one of the most powerful techniques for data exploration - coordinated views. Selecting subset of data in one graph automatically highlights this subset in all our graphs. In our implementation this feature works both with vector graphics (bar charts, pie charts, histograms) and image graphs.

- software can be used for both interactive explorations of patterns in visual artifacts (images of art, design, photography, web pages, films, animation, video of gameplay etc.) and producing high-res publication and exhibition quality images. You can specify the size and position of each visualization on a canvas and then save the results at 300dpi.

- It is very easy to prepare the data. You only need is a directory of images and a text file (.csv) describing locations of the images (filenames) and metadata. It can be created in Excel, Google Docs or any text processor.