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. The workshop, Introduction to Spatial Data Science, on September 29, 2026 provides an introduction to the techniques and programs used in this workshop.

Dr. Caglar Koylu is an Associate Professor in the School of Earth, Environment, and Sustainability at the University of Iowa. His research focuses on geographic information science (GIS), cartography, spatial data science, and geovisual analytics. He develops methods for analyzing and visualizing spatial data across applications in environmental science, public health, historical demography, and human mobility. He teaches undergraduate and graduate courses in GIS and spatial data science and regularly leads hands-on workshops on mapping, spatial analysis, and Python-based geospatial workflows.