With the recently relased version of the package RGL (V. 0.92.879) there is a new option to publish 3D plots as an interactive WebGL graphic. Nearly every 3D plot you set up in a RGL window can be exportet via a very easy command. Just plot as usual into your rgl device and then use 
To get a quick impression about the temporal stay of places it is helpful to generate a plot of the trackpoints spatial density (intensity). As the 3d visualisation has both advatages and disadvantages, a combination with a 2D plot is useful to interpret the data. The data used in this example is a gps record 
With the rgl package it’s possible to interact with the 3d visualization of the timespace tracks. Code example: plot3d(lon,lat,timedate, xlim=range(lon), ylim=range(lat), zlim=range(timedate), ticktype=”detailed”, xlab=”longitude”, ylab=”latitude”, zlab=”Date”, col= as.POSIXlt(daten)$mday, type=”l”, main=plottitle) In the posted example individual waypoints were added by drawing vertical lines.
Here comes another option to analyze a TimeSpace-Track with R. A lattice cloud plots every recorded trackpoint into a 3d-time-space-cube. As the data (planar point pattern) is marked with the daytime, cluster of everyday routines become visible. Here the direct comparison between a function of density and the time-space-cloud. Code example: cloud(time_hours ~ PPP_selection$x * 
This is an recent project about consumption and the categorization of consumers. The whole survey you can explore on: http://www.geographie.uni-erlangen.de/projects/konsum/ (german!). For the evaluation, R was used. “Der Verhaltensraum des Konsumenten – The Space of Consumer Behaviour” The Space shows the consumer in the center of a two dimensional abstract space. He is able to 
Beside the visualisation of TimeSpace Tracks, I’m trying to find a way to analyze GPX-Tracks with statistical software. This are the first results with R (The R Project for Statistical Computing): ^This graph is a result of the analysis with the package trip (Spatial analysis of animal track data). Unfortunatelly i’m do not understand 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
