Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle
Publisher: Wiley

NeuroImage, 2013 Increasing Statistical Power by Modeling Spatiotemporal Correlations in Longitudinal Neuroimage Data. If there is spatial autocorrelation in model residuals, values are typically low and the semivariance increases with separation distance [30,31]. Experience and/or coursework in ArcGIS (or other GIS), field methods, data assimilation, statistical analysis, spatial statistics, and/or remote sensing are highly desirable. In this presentation, NCVA introduces “OECD eXplorer” – an interactive tool for analyzing and communicating gained insights and discoveries about spatial-temporal and multivariate OECD regional data. Dimensional geo-referenced visualization but spatio-temporal data requires at least four dimensions for visualization. Are there better metaphors for spatio-temporal data visualization than a time-evolving heat map? Spatiotemporal Linear Mixed Effects Modeling for the Mass-univariate Analysis of Longitudinal Neuroimage Data. Spatio-temporal datasets are becoming increasingly common, more complex and larger. Applicants initially seeking an M.S. Will hurt me · Sunday data/statistics link roundup (2/17/2013) → Once in a while though, I come across data sets with a spatial or spatio-temporal component and I get the opportunity to leverage my experience in that area. Their analysis, “Unique in the Crowd: the privacy bounds of human mobility” showed that data from just four, randomly chosen “spatio-temporal points” (for example, mobile device pings to carrier antennas) was enough to uniquely identify 95% of the individuals, Using a complex mathematical and statistical analysis of that data, the researchers discovered that it is possible to find one formula to express what they call the “uniqueness of human mobility”: e 5 a 2 (nh).