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.

Jinyi Cai is a CSSI graduate consultant and a Ph.D. candidate in Geographic Information Science and Cartography at the University of Iowa. Her expertise includes spatial analysis and geovisualization. She has taught introductory GIS labs and geovisualization as a teaching assistant. As a research assistant, she has analyzed socio-demographic and environmental health data. Her work examines social vulnerability to private-well nitrate exposure and spatial associations between pesticide use and cancer incidence. She has also developed a web-based small-area cancer mapping application for the Iowa Cancer Registry.