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Statistics for Spatio-Temporal Data by Noel Cressie, Christopher K. Wikle

Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle ebook
ISBN: 0471692743, 9780471692744
Publisher: Wiley
Page: 624
Format: epub

There are many visual methods used to identify patterns in space and time. (This article was first published on Intelligent Trading, and kindly contributed to R-bloggers). The following is a partial look at an interesting but slightly pointy headed study published in Nature Magazine about how much identity information can be gleaned about the identity of a subject with merely four human data points. 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. A condition-of-the-artwork presentation of spatio-temporal processes, bridging vintage ideas with modern hierarchical statistical modeling principles and the most current computational approaches. We evaluate spatio-temporal correlation in the data and obtain appropriate standard errors. Department name when degree awarded. Hierarchical spatial, temporal, and spatio-temporal models allow for the simultaneous modeling of both first and second order processes, thus accounting for underlying autocorrelation in the system while still providing insight into overall Based on preliminary analysis, the data appeared to be overdispersed, containing a disproportionately high number of zeros along with a high variance relative to the mean. 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. We develop a suitable backfitting algorithm that permits efficient fitting of our model to large spatio-temporal data sets. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals.