This is a two part series that will look at how social media data can be downloaded and analyzed spatially to understand urban patterns in places where more traditional data sets are hard to come by. We will be focusing on Shenzhen, a Chinese city that has gone through rapid urban development over the last 20 years. The work of both modules will be exhibited as part of the Shenzhen / Hong Kong Biennale. The first module (11.S946) will be led by Luc Anselin, a leader in Spatial Statistics. This module will focus on using spatial statistics to understand how “Big Data” data can be used to understand cities, using the geo-located social media data download for Shenzhen as an example. The second module (11.S947) will be led by Sarah Williams, a leader in data visualization. This module will take the analysis from module # 1 and transform them into compelling visualizations. The best student visualizations will be included in the Shenzhen /Hong Kong Biennale.
One of the biggest issues for “Big Data” is the ability to visualize the results of the complex analysis that can be performed on these datasets. Geo-located social media data is a “Big Data” that can tell us much about cities where no other data exists. In this class students will learn data visualization strategies for working with social media data using the results of spatial analytics developed in the partnering course “Dynamic Urban Neighborhoods: City Scale Spatial Analytics : Using Shenzhen Social Media As Case". The best student visualizations will be displayed in the Shenzhen/Hong Kong Biennale in December. Students who are interested in spatial analytics can take the partnering course, but it is not a requirement.
Class meets October 23rd-December 11th.