Regression Analysis for Spatial Data

About

This course focuses on the visualization and modeling of spatial data. Examples are taken from different research areas such as political science, empirical international trade, criminology, and real estate. It offers a detailed explanation of individual estimation methods and their implementation in R. In this course, students will learn:
• How to generate a variety of different maps that visualize the location of spatial units
• How maximum likelihood estimation works and how to set up and optimize a likelihood function in R
• How to deal with computational problems that are frequently accounted when working with spatial data
• How to increase computation speed using concentrated maximum likelihood and the matrix exponential spatial specification model
• How to estimate a spatial regression model both, with cross‑sectional and with time‑series data
• How to properly interpret the output from a spatial regression model and how to investigate policy interventions.
• A basic background on spatial interaction models, heterogeneous coefficient SAR models, and spatio‑temporal models

What students do NOT learn in this course:
• Estimation of spatial regression models with other estimation techniques such as IV, NLS, and Bayesian Methods
• The use of a specialized Geographic Information System such as ArcGIS

Course leader

Roland Füss & Zeno Adams

Target group

Master | PhD | Postdoc | Professional

Fee info