Re-scheduled. Date TBA.

Abstract: Spatial statistical analysis builds upon the premise that where something happens can influence what happens, i.e., the location of observations can provide information on the observations themselves.  Location can be defined on geographic maps and in geometric space, but geography often involves information beyond simple location, distance, and direction.  In this presentation, we will explore how geography influences inference in spatial statistical analyses and offer geographic insights on familiar statistical constructs such as data visualization, asymptotics, classical and Bayesian inference, weighted estimation, model diagnostics, and compromises between design and modeling.  We will discuss compromises between geographic and statistical precision, statistical precision and local and global probabilistic strategies for ensuring data confidentiality.  Using historical and contemporary examples, we will illustrate how maps provide a critical context for data visualization and interpretation, ranging from the known (“You are here") to the unknown (“Here be dragons”).
 
About the Speaker:
Lance A. Waller, Ph.D. is a Professor and former Chair in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, a co-chair of National Academies’ Committee on Applied and Theoretical Statistics, and a member of the U.S. Census Scientific Advisory Committee.  He serves on Emory University’s AI Humanity Steering Committee and is co-directing Emory’s new Center for AI Learning.  Dr. Waller is a Fellow of the American Statistical Association and a Fellow of the Royal Geographical Society.  His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, and disease ecology.  His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley).