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

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. Boundaries of spatial units may evolve across time and that adds another layer of mismatches to a spatio-temporal level. 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. Bayesian model selection and model averaging. In particular, the workshop aims at integrating recent results from existing fields such as data mining, statistics, machine learning and relational databases to discuss and introduce new algorithmic foundations and representation formalisms in pattern discovery. Inference for stochastic processes. (This article was first published on Intelligent Trading, and kindly contributed to R-bloggers). Statistics for Spatio-Temporal Data (Wiley Desktop Editions) by Noel Cressie (Author), Christopher K. Epidemiology and Infection, 140 (9), 1663-1677. If there is spatial autocorrelation in model residuals, values are typically low and the semivariance increases with separation distance [30,31]. Complex patterns from text/hypertext data, networks and graphs, event or log data, biological data, spatio-temporal data, sensor data and streams, and so on. Network inference for protein microarray data. The main goal of the project is to combine spatio-temporal models for pollution and health data into a single large hierarchical Bayesian model. R package: Interventional inference for Dynamic Bayesian The spatial and temporal determinants of campylobacteriosis notifications in New Zealand, 2001–2007. 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. 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). Book: Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties (Wiley Series in Probability and Statistics) Author: Michael Sherman Pages: 294. Stochastic processes and applied probability. There are many visual methods used to identify patterns in space and time.