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.

This workshop introduces directed acyclic graphs (DAGs) as a practical tool for selecting variables to include in analyses. Participants will learn how DAGs can help identify potential confounding and support transparent analytic decisions. Using examples from epidemiological research, the workshop will demonstrate how to develop and interpret DAGs, apply them to covariate selection, and perform sensitivity analyses to evaluate the robustness of the covariate selection process. This workshop is intended for students, researchers, and public health professionals with a basic understanding of regression modeling. No prior experience with DAGs or causal inference methods is required.

This virtual workshop introduces participants to the basics of creating clear and effective data visualizations in Stata. Through demonstrations, example code, and hands-on exercises, participants will learn how to prepare data for visualization, create and customize common figures (including bar graphs, scatterplots, and other commonly used visualizations), and communicate findings through well-designed figures. The workshop will use Stata to demonstrate visualization techniques, but the principles for choosing, preparing, and effectively presenting visualizations are applicable across other statistical programs. This workshop is intended for students, researchers, and anyone who works with data and wants to improve their ability to present results visually. Basic familiarity with Stata is encouraged, but not required.