Project Summary In humans, genetic variation is distributed geographically, reflecting the history of human movements across the continents. Understanding these spatial patterns is crucial for many fields in human population genomics, including the study of human evolutionary history and linking genotypes and phenotypes. Historically, limitations in the size and scope of empirical datasets have allowed researchers to employ models that ignore geography, but modern genomic datasets demand population genetic methods that incorporate geographic space. The failure to consider geography in population genetics has significant consequences, from incorrect inference of population structure to misleading conclusions in genome-wide association studies. Over the past five years since beginning this R35, we have developed and applied multiple new statistical meth- ods to understand the role of geography in structuring patterns of genetic diversity and relatedness. We have focused on the inference of important population genetic parameters, such as population dispersal and density, as well as on methods for recovering the geographic history of large-scale population movements. The flexibility of the R35 has also allowed us to explore novel research avenues, in particular the utility of Ancestral Recombina- tion Graphs, which encode the full genealogical history of a group of sampled individuals across their genomes, for evolutionary genetics inference. Over the next five years, we will continue to develop and publicly release statistical methods that explicitly in- corporate geography into the analysis of population genetic data. We will focus on tools for inferring spatial demographic history, including patterns of dispersal and ancestral geographic locations, as well as for the iden- tification of genomic regions involved in local adaptation to continuous landscapes. The vision of this research program is to transform how we analyze genetic variation in space and time. By deve