The three papers in this dissertation contribute to research that seeks to characterize the complex and multi-dimensional relation between the physical environment and human learning. The first paper outlines a new approach to describe, represent, and interpret people’s interaction as they move within and across physical environments. I call this approach interaction geography. It encompasses Mondrian Transcription, a method to map people’s movement and conversation over space and time, and the Interaction Geography Slicer (IGS), a dynamic visualization tool that supports new forms of interaction and multi-modal analysis. The second paper extends this work to provide a conceptual framework to expand interaction geography in studies of learning. I show how interaction geography offers resources to integrate four historically separate research perspectives in order to study how people’s interaction, movement, and responses to, and actions on, the physical environment lead people to learn. The third paper adapts and uses the IGS to visualize and discuss data about New York City’s Stop-And-Frisk Program. I show how the IGS provides new ways to view, interact with, and query large-scale data sets of stop-and-frisk and crime data over space and through time to support analyses of and public discussion about a controversial social and political issue.