Analysing Spatio-Temporal Autocorrelation with LISTA-Viz Frank Hardisty, Alexander Klippel GeoVISTA Center Department of Geography 302 Walker Building John A. Dutton e-Education Institute 2217 Earth & Engineering Sciences Building The Pennsylvania State University, University Park, PA, 16801, USA hardisty@psu.edu klippel@psu.edu Abstract — Many interesting analysis problems (for example, disease surveillance) would become more tractable if their spatio-temporal structure was better understood. Specifically, it would be helpful to be able to identify autocorrelation in space and time simultaneously. Some of the most commonly used measures of spatial association are LISA statistics, such as the Local Moran’s I or the Getis-Ord Gi*, however these have not been applied to the spatio-temporal case (including many time steps) due to computational limitations. We have implemented a spatio-temporal version of the Local Moran’s I, and claim two advances: First, we exploit the fact that there are a limited number of topological relationships present in the data to make Monte Carlo estimation of probability densities computationally practical, and thereby bypass the “curse of dimensionality”. We term this approach “spatial memoization”. Second, we developed a tool (LISTA-Viz) for interacting with the spatio- temporal structure uncovered by the statistics which contains a novel coordination strategy. The potential usefulness of the method and associated tool are illustrated by an analysis of the 2009 H1N1 pandemic, with the finding that there was a critical spatio-temporal “inflection point” at which the pandemic changed its character in the United States. Keywords—Spatio-temporal autocorrelation, Monte Carlo simulation, Moran’s I Introduction Effective methods for exploring space-time structure are needed to make sense of phenomena which contain both spatial and temporal referents (Andrienko et al. 2001). There is a particular need for methods which can not only represent spatio-temporal data visually and interactively, but can offer analytic judgements, by finding which spatio-temporal patterns in the data are significant in the statistical sense (Andrienko et al. 2007), also known as geovisual analytics (Kraak 2008). An example of a pressing spatio- temporal problem which could benefit from a geovisual analytics approach is that of understanding the spread of infectious disease. We have therefore created a method for exploring spatio-temporal structure using an extension to one of the most popular statistical methods for spatial autocorrelation, the local Moran’s I (Anselin 1995). This could be thought of as one of a class of LISTA statistics (for Local Indicators of Spatio-Temporal Association, after Anselin’s LISA). We suggest that to make the computational problem of finding