11.S947 : VISUALIZING SHENZHEN’S SOCIAL MEDIA TO UNDERSTAND CITY DYNAMICS

Wednesdays : 9:30 – 11 am

Instructor: Sarah Williams ()

Room: 9-451

Graded, can be taken pass fail

TA: Liqun Chen
Pre-Reqs :Knowledge of GIS, either 11.205 or previous work in GIS
Credits : 6

Class Description:

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. Thefirst module 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 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.

Course Objectives:

  1. Learn visualization Strategies for Geo-locative Data.
  2. Get an introduction to the latest visualization software Processing, D3.js, Map box and other tools for visualization.
  3. Experience with presentation quality graphics which will be developed for the Shenzhen Biennale.
  4. Learn to communicate with complex spatial data

Grading:

ASSIGNMENT / % of TOTAL
EXERCISES (3) 10% EACH / 30%
CLASS BLOG / 10%
FINAL PROJECT / 55%
CLASS PARTICIPATION / 5%

Materials:

Hard Drive: It is recommended that everyone get an external hard drive to hold data for your assignment and final project. I suggest a minimum of a 40 GB hard drive, but even the cheapest mobile drive comes at 1TB. The “WD My Passport 1 TB” costs roughly $69.99 on amazon, which is an amazing amount of data. This drive will be useful for this class and beyond.

WEEKLY SCHEDULE:

WEEK / DAY / TOPIC / TAKE HOME
Week 1 / 10/23 / Introduction of topic and analysis of previous class. / 1. Students make first attempts of visualization with the results from the previous class.
2. Make interactive maps Catodb and Mapbox.
3. “Industry in Motion” working paper (Williams)
Week 2 / 10/30 / Visualization Strategies Discussed / Next Steps Determined / 1. Interactive Visualization Techniques.
2. D3.js tutorials / Work with Data and D3.js
Week 3 / 11/6 / Visualization Strategies Presented / 2. Working with Processing and Spatial Data.
Week 4 / 11/13 / Working Sessions for Visualizations / Preparation for Final Review
Week 5 / 11/20 / Final Project Review / Make revisions needed for approval for selection into Shenzhen Biennale.
Week 6 / 11/27 / Revision of Final Projects for Shenzhen Biennale. / Student work translated in production quality print for biennale.
Week 7 / 12/4 / Presentation Quality Images Produced for Biennale - Open of Biennale December 8th. / Opening of biennale - whomever can join.
Week 8 / 12/11 / Last Day of Class Wrap-Up