Studying Spatial Inequality Using Algorithmic Simulations in R

Public policy about geographic space impacts how inequalities manifest between our cities, neighborhoods, and schools. In this talk, I will introduce a series of R packages designed to investigate spatial inequality in a range of policy settings, as developed by members of The Algorithm-Assisted Redistricting Methodology (ALARM) Project.

I will devote particular attention to how algorithmic simulations can be useful for evaluating policy questions related to topics like legislative districts (as “gerrymandering"), school / residential segregation, and privacy protection systems (as in the US Census).

Tyler Simko is a Postdoctoral Research Associate in the Department of Politics at Princeton University, and an incoming Assistant Professor of Political Science at the University of Michigan. His research focuses on how the design of public policy influences geographic inequality and US state and local politics.

Please register here. Lunch will be provided.