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spacetime-Spatio-Temporal Data in R.pdf
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[size=13.333333015441895px]This document describes classes and methods designed to deal with different types of spatio-temporal data in R implemented in the R package spacetime, and provides examples for analyzing them. It builds upon the classes and methods for spatial data[size=13.333333015441895px] from package sp, and for time series data from package xts. The goal is to cover a number of useful representations for spatio-temporal sensor data, and results from predicting (spatial and/or temporal interpolation or smoothing), aggregating, or subsetting them, and to represent trajectories. The goals of this paper is to explore how spatio-temporal data can be sensibly represented in classes, and to find out which analysis and visualisation methods are useful and feasible. We discuss the time series convention of representing time intervals by their starting time only. This document is the main reference for the R package spacetime, and is available (in updated form) as a vignette in this package.
GeoXp-An R Package for Exploratory Spatial Data Analysis.pdf
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[size=13.333333015441895px]We present GeoXp, an R package implementing interactive graphics for exploratory [size=13.333333015441895px] spatial data[size=13.333333015441895px] analysis. We use a data set concerning public schools of the French Midi- Pyr [size=13.333333015441895px]en 攃攀猀嬀/size][size=13.333333015441895px] region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.
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