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Got coordinates in your data? Feeling lost in space? Spatial data arise in a wealth of scientific fields; their stochastic structure poses unique analytical challenges that are different from most modeling and prediction methodologies. SAS/STAT ® 9.22 introduces exciting updates to the SAS suite of spatial procedures to augment, simplify, and streamline(More)
—Atmospheric studies often require the generation of high-resolution maps of ozone distribution across space and time. The high natural variability of ozone concentrations and the different levels of accuracy of the algorithms used to generate data from remote sensing instruments introduce major sources of uncertainty in ozone modeling and mapping. These(More)
This paper is concerned with the modeling of infectious disease spread in a composite space-time domain under conditions of uncertainty. We focus on stochastic modeling that accounts for basic mechanisms of disease distribution and multi-sourced in situ uncertainties. Starting from the general formulation of population migration dynamics and the(More)
Spatial analysis and maps are a perfect match. Spatial analysis adds intelligence to your maps; maps provide context for your spatial analysis. The geostatistical tools in SAS/STAT ® software can model and predict a variety of spatial data. SAS Mapping facilities enable you to create rich visualizations from that material. This presentation introduces a new(More)
Stochastic analysis and prediction is an important component of space-time data processing for a broad spectrum of Geographic Information Systems scientists and end users. For this task, a variety of numerical tools are available that are based on established statistical techniques. We present an original software tool that implements stochastic data(More)
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