Mon premier blog

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

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. Boundaries of spatial units may evolve across time and that adds another layer of mismatches to a spatio-temporal level. 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). 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. Therefore, whether statistical methods are useful for early event detection within spatiotemporal biosurveillance still is an open question even to the greater extent, than for temporal surveillance. Thesis Most of my recent books and papers deal with statistical inference and computational methods for spatial and spatio-temporal point processes. Arc Diagram and spatiotemporal data mining visualization. The main task will be the development and evaluation of dynamic visualisation methods for spatio-temporal data by combining techniques of computer graphics and statistical analysis. 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.