Spatial Analytics: From the Margins to the Mainstream
Luc Anselin, University of Chicago
This talk is a personal reflection on the evolution of spatial analysis I experienced over the past 40+ years. Many consequential changes have taken place in terms of the mechanics of analysis, moving from a small data to a big data world, from mainframe and desktop computing to cloud environments, and from analytical proofs to AI agents. In reviewing this evolution, I will focus on the role of some fundamental spatial concepts, such as spatial interaction, Tobler’s law, scale and MAUP, and spatial heterogeneity. I will relate this to my own experience in software development, moving from SpaceStat, to desktop GeoDa and geoda.ai. I will close by offering some speculations about the future.
Bio: Luc Anselin is Stein-Freiler Distinguished Service Professor of Spatial Data Science in the Social Science Division of the University of Chicago, where he founded and directs the Center for Spatial Data Science. His research deals with the development of methods and software for the analysis of spatial data, including spatial econometrics, local indicators of spatial association and exploratory spatial data analysis. He is an elected member of the US National Academy of Sciences and American Academy of Arts and Sciences.
