Presentation at Center for Spatial Data Science at University of Chicago on May 6, 2022

Super-Charging your (Regression) Models with Space and Geographical Context

This talk will present an overview of different strategies to embed space and geographical context in (regression) models to improve their performance. Often, where a phenomenon takes place is relevant to both understand it and predict it, two of the main reasons for building empirical models. This talk will introduce you to two broad sets of techniques to do so in a systematic way: explicitly spatial regression, and spatial feature engineering. Spatial regression incorporates information about the spatial distribution of observations in the structural form of the model; spatial feature engineering does so in the “features” or variables that are then plugged into a model or algorithm. We will take a hands-on approach and illustrate several of these concepts using the modern Python stack for data science and rely on the upcoming book Geographic Data Science with Python. The only requirement for this session will be to bring an internet-connected laptop. Optionally, you may want to skim over chapter 12 of the book.

Reading:

Chapter 12 on spatial feature engineering which covers strategies to build space into your features/variables that you can then plug into non-spatial models (traditional regression or ML):
https://geographicdata.science/book/n...