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 witch scale is used by the package.
^Trackpoints as a function of density.
Since there is a trackpoint recorded every 10 sec., it is possible to interpretate the density of the trackpoints as time-spend.
This is a two day track. The highest peak in the right corner is my home (Nuremberg). The peaks in the backstage are both university in Erlangen. The path on the rigth side I did with my bicycle, the left one with the train.
But how to examine specific areas?
^1500 m arround my house in the city center.
With clickppp() from the spatstat package it’s possible to choose e.g. a point with the mouse:
####### Example Code:
plot(tripdata_utm) # plots the recorded trackpoints (converted to UTM)
P_center <- clickppp(n=1, win=Rect, add=TRUE, main=NULL, hook=NULL) # Select a point in the plot with the mouse
center <- as.data.frame(P_center)
D <- disc(radius = 1500, centre = c(center[,1], center[,2])) # create a disc window
P_selection <- ppp(tripdata_utm_num[,1], tripdata_utm_num[,2], window=D) # reduce the data with the window
^Another function of density (2D).
^Trackpoints as a function of time.
Here the trackpoints are divided by a grid and counted. Since the device records the position every 10 sec. The qqcount can be clearly interpreted as time-spend.
The next step is to add this data to a gis layer.