All the World’s A Stage: Elements for Visualization of Multivariate Spatiotemporal Data in R by Emily Dodwell. Visit https://rstats.ai/nyr/ to learn more. Abstract: Visualization of multivariate spatiotemporal data has represented a nontrivial challenge. Displaying the data visually requires the capacity for the user to examine different perspectives and make comparisons among and between features, including use of multiple displays to re-focus attention on each component and linked brushing to connect the plots. In this talk, Emily will discuss considerations for slicing on space and time to reveal spatial and temporal dependencies at varying granularities, e.g. day of week vs. hour of day and urban v. rural. She will demonstrate a combination of graphical tools in R that support such an analysis, and their implementation to visualize the Australian bushfires that devastated parts of the country throughout the 2019-2020 season. Generous funding from ACEMS supported Emily’s visit to Melbourne this past spring for this collaboration with Professor Di Cook and Weihao Li of Monash University.a Bio: Emily Dodwell is a Principal Inventive Scientist in the Data Science and AI Research organization at AT&T Labs, where she currently focuses on predictive modeling for advertising applications, the creation of interactive tools for data analysis and visualization, and research concerning ethics and fairness in machine learning. Emily is an R enthusiast committed to promoting gender diversity in the community, and she is an organizer of R-Ladies New York City and a member of R Forwards, the R Foundation task force on women and other underrepresented groups. Prior to joining AT&T Labs in 2015, Emily taught high school math for three years at Choate Rosemary Hall. She received her M.A. in statistics from Yale University and B.A. in mathematics from Smith College. Twitter: https://twitter.com/emdodwell Presented at the 2020 R Conference | New York (August 14th, 2020)