Many social, environmental, and health processes exhibit spatial patterns. Nearby places may be related, and relationships between variables may vary across locations. When these spatial patterns are ignored, key assumptions of traditional statistical methods can be violated, leading to biased or misleading results.

This workshop introduces concepts and methods in spatial statistics and GeoAI for analyzing such patterns and relationships. Participants will learn about spatial dependence, where nearby observations tend to be related, and spatial heterogeneity and non-stationarity, where relationships between variables vary across space. Through a hands-on case study, participants will use mapping and spatial statistical methods to identify these patterns.

The workshop will also introduce XGBoost as a machine-learning approach for modeling complex and nonlinear relationships and use SHAP (Shapley Additive Explanations) to interpret how different variables contribute to model predictions in the social, environmental, and health sciences.

Analyses will be conducted in Python using Google Colab. Some familiarity with quantitative data analysis and spatial data science is encouraged. Prior experience with machine learning or programming is not required.

Operator Theory Seminar