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):

GPS track analized with R package "trip"

density plot 3D

^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?

trackdata density plot 3D

^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

density plot 2D

^Another function of density (2D).

qqcout plot

^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.

*Related*

To

**leave a comment** for the author, please follow the link and comment on their blog:

** geolabs » R**.

R-bloggers.com offers

**daily e-mail updates** about

R news and

tutorials on topics such as:

Data science,

Big Data, R jobs, visualization (

ggplot2,

Boxplots,

maps,

animation), programming (

RStudio,

Sweave,

LaTeX,

SQL,

Eclipse,

git,

hadoop,

Web Scraping) statistics (

regression,

PCA,

time series,

trading) and more...

If you got this far, why not

__subscribe for updates__ from the site? Choose your flavor:

e-mail,

twitter,

RSS, or

facebook...