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The following people contributed to this task view: Roger Bivand, Achim Zeileis, Michael Sumner, Ping Yang. Although one could argue that all data are spatio-temporal, as they must have been taken somewhere and at some point in time, in many cases the spatial locations or times of observation are not registered, and irrelevant to the purpose of the study. Here, we will address the cases where both location and time of observation are registered, and relevant for the analysis of the data.

The Spatial and TimeSeries task views shed light on spatial, and temporal data handling and analysis, individually. This task view aims at presenting R packages that are useful for the analysis of spatio-temporal data. Representing data In long tables: In some cases, spatio-temporal data can be held in tables ( data. For instance, data sets in package plm for linear panel models have repeated observations for observational units, where these units often refer Doxazosin Mesylate (Cardura)- Multum spatial areas (countries, states) by an index.

This index (a name, or number) can be matched to the spatial coordinates (polygons) of the corresponding area, an example of this is given by Pebesma (2012, Journal of Statistical Software). As these data sets usually contain more than one attribute, to hold the data in a two-dimensional table a long table form is chosen, where each record contains the index of the observational Doxazosin Mesylate (Cardura)- Multum, observation time, and all attributes.

In time-wide tables: When a single attribute is considered, another layout Doxazosin Mesylate (Cardura)- Multum that of the time-wide tablewhere each observational unit forms a record and each column an observation time. In space-wide tables: An example of a space-wide table is the Irish wind data set, obtained by data(wind) in package gstat. It has time series as different columns, each column representing one location (weather station).

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