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

We evaluate spatio-temporal correlation in the data and obtain appropriate standard errors. Thesis Most of my recent books and papers deal with statistical inference and computational methods for spatial and spatio-temporal point processes. Based on this hypothesis, we combined spatial statistical methods with genetic analytic techniques and explicitly used geographic space to explore genetic evolution of H5N1 highly pathogenic avian influenza viruses at the sub-national scale in Vietnam. 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. High-Dimensional Statistical Inference; Spatio-Temporal Data Applications; Computational Algorithms for High-Dimensional Data; Genomic Applications. Abstract: In this paper we present a visual analytics approach for deriving spatio-temporal patterns of collective human mobility from a vast mobile network traffic data set. JOB ASSIGNMENTS The goal of the position is to apply and develop statistical models for interpolation, reconstruction and prediction of climatological and environmental spatio-temporal data. Each virus was assigned a unique identification number, allowing us to link geographic location, genetic sequence and temporal data in later analyses, and the dataset was sorted in ascending order by this unique ID. It's About Space and Time: From the Modifiable Areal Unit Problem (MAUP) to the Modifiable Temporal Unit Problem (MTUP) to the Modifiable Spatio-Temporal Unit Problem (MSTUP) many facets of space-time dynamics, from semantics and ontology (how we think about the system), to representation of space-time objects and space-time fields (how they move, morph and change) to the statistical and mathematical modeling of time-dynamic geographic systems. Statistics for Spatio-Temporal Data (Wiley Desktop Editions) by Noel Cressie (Author), Christopher K. My main focus of research is in mathematical statistics and applied probability, particularly in relation to spatial data sets and computational problems as covered in the research areas known as spatial statistics, stochastic geometry, simulation- based inference, Markov chain Monte Carlo methods, and perfect simulation. Risk maps have been defined in [47] as “outcomes of models of disease transmission based on spatial and temporal data”, incorporating “to varying degrees, epidemiological, entomological, climatic and environmental information”, and they have been applied to numerous diseases for . A GIS was built within ArcGIS 9.2 (Environmental Research Systems Institute, Redlands, CA, USA) and statistical analyses were performed using Stata 11 (Stata Corporation, College Station, Texas). If there is spatial autocorrelation in model residuals, values are typically low and the semivariance increases with separation distance [30,31]. We develop a suitable backfitting algorithm that permits efficient fitting of our model to large spatio-temporal data sets.