Spatial intensity 2D/3D

As the 3d visualisation also has disadvantages, a combination with a 2D plot is useful to interpretate the data. The data used in this example is a gps record of the “everyday life” of a test person.

Code snippet:

###################################################
## 3d/2D Plot density with spatstat density.ppp
###################################################

bb_utm <- qbbox(lat = tripdata_utm_num[,2], lon = tripdata_utm_num[,1]) #Boundingbox
Rect <- owin(c(bb_utm$lonR[1]-500, bb_utm$lonR[2]+1800), c(bb_utm$latR[1]-500, bb_utm$latR[2]+500))
P_all <- ppp(tripdata_utm_num[,1], tripdata_utm_num[,2], window=Rect)   #  opt: marks=datetime
PPP_all <- as.ppp(P_all)
den <- density(PPP_all, sigma = 70)

cutted_den <- den  # Wertebereich abschneiden für Extreme
cutvalue <- 0.0020   # Erfahrungswert
cutted_den$v[cutted_den$v>=cutvalue] <-cutvalue

png(paste(plotpath,proband,”intensity_overview_spatstat_v3.png”, sep=”"),2400,1200);  # Plot Dichte 3D
layout(matrix(c(1,2), 2, 2, byrow = TRUE), widths=c(1,1))
persp(cutted_den , col=”grey”, d=1, ticktype=”detailed”, cex.lab=4, cex.axis=4, zlim=c(range(cutted_den$v)[1],cutvalue), phi=30, theta=-20, xlab=”longitude”, ylab=”latitude”, zlab=”density”, main=paste(proband, “Räumliche Intensität”))
couleurs  <- tail(topo.colors(trunc(1.4 * 100)),100)
couleurs[1] <- “#0400ff”
plot(cutted_den , col=couleurs, ticktype=”detailed”, xlab=”longitude”, ylab=”latitude”, cex.lab=5, cex.axis=5, main=paste(proband, “Räumliche Intensität 2D”))
#points(SP_UTM@coords[,1], SP_UTM@coords[,2], cex=0.05, col=”grey”)
points(SP_wp_UTM@coords[,1], SP_wp_UTM@coords[,2], cex=2, col=”red”)
text(SP_wp_UTM@coords[,1], SP_wp_UTM@coords[,2], labels=daten_wp$wpOpen, cex=1, adj=c(0,-1), col=”red”)

dev.off()

# If you use any code, please refer to or cite this site (see about geolabs)

Hägerstrand time-space cube with R

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[,"Date"])$mday, type=”l”, main=plottitle)

In the posted example individual waypoints were added by drawing vertical lines.

Here the direct comparison between a function of density and the time-space-cloud.

Time space Clowd

spatstat density plot

Code example:

cloud(time_hours ~ PPP_selection$x * PPP_selection$y, data = daten, zlim = c(23,0), xlim = c(653000,643000), screen = list(z = 160, x = 120), panel.aspect = 0.75, xlab = “Longitude”, ylab = “Latitude”, zlab = “Time”, scales = list(z = list(arrows = FALSE, distance = 2), x = list(arrows =FALSE, distance = 2), y = list(arrows = FALSE, distande = 2)),)

This examle is inspired by: http://lmdvr.r-forge.r-project.org/ (Figure 6.2)

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.

The GPX-Track was converted via JavaScript to MaxScript (with UTM coordinates). Finally the Track builds itself in 3dsMax.

gps track as timespace data in 3dsmax

“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 “move” to every point in the coordinate system. Each buy he takes a new position. The first dimension is the dimension of hedonism (buying – shopping), the second is the dimension of autonomy. They are both independent. 31 test persons took part in the survey. They filled out a diary about their buys.

This is a sample person with 16 buys. The arrows indicate the direction of time, the circle grows for each buy at the same position.

This graph compares the buys of different people in the same category of shops.
It shows a clustering towards hedonism in fashion shops and a clustering towards buying in drugstores.
More on the project homepage.