Exploring large data sets with the aim of extracting useful
information for decision making can be challenging. If the data
were collected at different locations and times, one important
question is how to obtain reliable estimates for missing data in
space or time. For example, measurements such as ozone
concentrations are usually collected only by a limited number of
monitoring stations and at different time instances. In order to
estimate the values at unmeasured locations or time instances,
interpolation in continuous space and time is needed. New and old
interpolation methods for exploring spatiotemporal data are
discussed in this book. The selected methods are useful for
Geographic Information Systems (GIS). This book also includes
comparisons of selected methods for several GIS case studies, as
well as some visualization and query examples.
General
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