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.

Many social science questions have a spatial dimension. Exploring spatial patterns can help researchers generate hypotheses about how geography shapes social and environmental processes in their studies. This workshop is for beginners to learn a practical workflow for exploring spatial patterns of socio-demographic and environmental data. Through a hands-on case study, participants will retrieve socio-demographic data through the U.S. Census API and integrate it with spatial datasets. They will then use interactive visualization to explore geographic patterns and apply spatial statistical methods to identify spatial clusters, where nearby areas have similar values, and spatial outliers, where locations differ from their neighbors. Participants will learn to conduct these analyses in Google Colab using Jupyter notebooks based on Python. The cloud-based environment allows them to run analyses and scale computation on large datasets without local software installation. Basic familiarity with Python is helpful but not required.

This workshop will cover the fundamental concepts of programming using Python. Using demonstrations and hands-on exercises, participants will learn how to write simple scripts, understand basic syntax, and work with core concepts like variables, loops, and data types. By the end of the session, they will have the foundation and be able to continue exploring Python on their own. The workshop is designed for undergraduate students, graduate students, and staff from non-computer science backgrounds, but anyone with no or limited experience with Python is encouraged to attend. The workshop will use Google Colab, so no software installation is required.

This workshop is an introduction to social network analysis. Participants will discuss core concepts (e.g., density and transitivity) as well as descriptive and inferential techniques, such as community detection, that can be used to understand and analyze networks. All coding will be done in R. Familiarity with R is encouraged but not required to participate in the workshop.