This course is about different techniques used in assembling, managing, analyzing and predicting using heterogeneous data sets in urban environments. These data sets that are inherently messy and incomplete. Types of data include, point, polygon, raster, vector, text, image and network data; data sets with high cadence and high spatial resolution. This is a survey course for different techniques and approaches in dealing with these data to make short term operational decisions as well as long term planning. As such, the emphasis is on practical urban data analytics rather than in-depth discussion about the suitability and appropriateness of techniques and their associated theoretical assumptions.
This is a companion course to PLAN 673: The Ethics and Politics of New Urban Analytics (Seminar), which deals with the ethics and politics of data in urban settings.
Students are encouraged to take them both.
Prerequisites & Preparation
The course will move quickly, cover a large number of analytical techniques, data sets, use cases and disciplinary domains. It requires significant investment on the part of the students to learn the technical skills as well as to learn about substantive urban and regional analyses.
Much of the work in this course will be done using Open Source Software that is usually free.
While it is not a prerequisite, the course assumes a working knowledge of R. R is a programming language and a free software environment for statistical computing and graphics. There are a number of online resources that will help you with getting up to speed with R.