Description of the course:
The recent explosion of spatially explicit data and analytical tools, such as "Geographic Information Systems" (GIS) and spatial econometrics, have aided researchers and decision- makers faced with a variety of challenges. This course introduces students to spatial data and its analysis, as well as the modeling of spatially dependent social processes and policy problems. Students will be introduced to the types, sources, and display of spatial data. Through hands-on analysis, students will learn to extract quantitative information from spatial data for applied research and public policy. Students will be introduced to spatial statistics, spatially dependent simulation, and spatial optimization. Students will learn to think creatively about spatial problems through examples drawn from economics, politics, epidemiology, criminology, agriculture, social networks, and the environment. The goal of the course is to equip advanced masters students and doctoral students with tools that will help them be effective analysts and communicators of spatial information in their future research or policy-related work. Because hands-on analysis plays a central role in the class, students will benefit from prior experience with basic computer programming -- although prior experience is not required. Prerequisites: introductory statistics or equivalent.