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Penn State Computational Science Lectures February 2009
Spatial Models I
Lots of scientific questions involve analyzing data that are spatially dependent: Data points close together are more closely related (dependent) than data further away.
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Examples: I
Concentrations of PM2.5 (air pollutants) across the U.S.
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Disease rates by county.
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Abundance of plant/animal species across Pennsylvania.
‘Space’ does not always mean physical distance. Similar ideas are applicable to many other research areas: I
Machine learning: two objects may be close in ‘feature space’.
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Approximations to computationally expensive computer models.
