. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . "M-GEO 5.0"@en . "34" . "5.0" . "140.0" . "10.0" . "Yes"@en . "No"@en . "2.0" . "The premise of the course is motivated by the recent advancements in geoinformation data acquisition and\nstorage and their intended use for evidence-based planning and monitoring. The spatial references of geoinformation data may be attributed to the exact locations of measurements or over a fixed set of contiguous\nregions or lattices. This course seeks to handle the three main classes of spatial data/processes:\ngeostatistical data/spatially continuous process, lattice data/discrete process, and point pattern data/point\nprocess. Such data appear common in diverse application fields like environmental science, agriculture,\nnatural resources, environmental epidemiology, and so on. The aim is to present methods that can be used\nto explore and model such data. Naturally, data vary in space and in time; hence data close to each other\n(either in space or time) are more similar than those farther. Geostatistical modeling based on the\nsemivariance and/or covariances and interpolation (kriging) in space and time will therefore be introduced.\nThe methods will be extended and applied to data aggregated over contagious regions. The uncertainty is\nquantified, and attention will be given to making maps showing the probabilities that thresholds are\nexceeded. Attention is also given to optimal sampling and monitoring. Further, data that arise out of the\noccurrences of events; thus point pattern data will be considered. The significance of exploring the first\nand second-order properties of point patterns in diverse application domains like environmental and\ndisaster (like earthquakes) modeling will be explained and applied. The last focus will be on lattice data; in\nprinciple, this kind of data consists of observed values over a set of fixed contiguous regions. This kind of\ndata is rather easy to acquire and is mostly applied in health studies where data aggregation is a standard\nform of protecting locational privacy."@en . "Geostatistics"@en . . "Geostatistics"@en . "Geostatistics"@en .