Stay on track: Plotting GPS tracks with R

September 18, 2014
By

(This article was first published on Rcrastinate, and kindly contributed to R-bloggers)

Many GPS devices and apps have the capability to track your current position via GPS. If you go walking, running, cycling, flying or driving, you can take a look at your exact route and your average speed.

Some of these devices or apps also allow you to export your routes in various formats, e.g., the popular XML-based GPX format.

I want to show you my attempts to
– read in a GPX file using R and its XML package,
– calculate distances and speeds between points,
– plot elevation and speed,
– plot a track,
– plot a track on a map.

Here are the results. Everything below the plots shows you how to do it in R. Drop me a comment if you have any questions.

The altitude of the track in time, with smoother. Click to enlarge.

The speed during the run, with smoother. Click to enlarge.

The raw track without a map in the background. Click to enlarge.

The track with an OpenStreetMap of Stuttgart in the background.

The track with a Bing map of Stuttgart in the background. Click to enlarge.

The track with a Mapquest map of Stuttgart in the background. Click to enlarge.

Again the same track, now with Skobbler map of Stuttgart in the background. Click to enlarge.



If you want to replicate this script, you can use an example GPX file I uploaded. It’s one of my runs through Stuttgart. The file name is “run.gpx”.

We gonna need some packages.

library(XML)
library(OpenStreetMap)
library(lubridate)

And a function that shifts vectors conviniently (we gonna need this later):

shift.vec <- function (vec, shift) {
  if(length(vec) <= abs(shift)) {
    rep(NA ,length(vec))
  }else{
    if (shift >= 0) {
      c(rep(NA, shift), vec[1:(length(vec)-shift)]) }
    else {
      c(vec[(abs(shift)+1):length(vec)], rep(NA, abs(shift))) } } }

Now, we’re reading in the GPX file. If you want help on parsing XML files, check out this (German) tutorial I made a while ago.

# Parse the GPX file
pfile <- htmlTreeParse(“run.gpx”,
                      error = function (…) {}, useInternalNodes = T)
# Get all elevations, times and coordinates via the respective xpath
elevations <- as.numeric(xpathSApply(pfile, path = “//trkpt/ele”, xmlValue))
times <- xpathSApply(pfile, path = “//trkpt/time”, xmlValue)
coords <- xpathSApply(pfile, path = “//trkpt”, xmlAttrs)
# Extract latitude and longitude from the coordinates
lats <- as.numeric(coords[“lat”,])
lons <- as.numeric(coords[“lon”,])
# Put everything in a dataframe and get rid of old variables
geodf <- data.frame(lat = lats, lon = lons, ele = elevations, time = times)
rm(list=c(“elevations”, “lats”, “lons”, “pfile”, “times”, “coords”))
head(geodf)
       lat      lon ele                   time
1 48.78457 9.217300 312 2014-08-17T17:25:07.45
2 48.78457 9.217300 312 2014-08-17T17:25:07.52
3 48.78466 9.217295 311 2014-08-17T17:25:10.53
4 48.78475 9.217335 307 2014-08-17T17:25:13.50
5 48.78480 9.217410 310 2014-08-17T17:25:19.51
6 48.78486 9.217550 306 2014-08-17T17:25:22.49

We already have nice dataframe now with all the information available in the GPX file for each position. Each position is defined by the latitude and longitude and we also have the elevation (altitude) available. Note, that the altitude ist quite noisy with GPS, we will see this in a minute.

Now, let’s calculate the distances between successive positions and the respective speed in this segment.

# Shift vectors for lat and lon so that each row also contains the next position.
geodf$lat.p1 <- shift.vec(geodf$lat, -1)
geodf$lon.p1 <- shift.vec(geodf$lon, -1)
# Calculate distances (in metres) using the function pointDistance from the ‘raster’ package.
# Parameter ‘lonlat’ has to be TRUE!
geodf$dist.to.prev <- apply(geodf, 1, FUN = function (row) {
  pointDistance(c(as.numeric(row[“lat.p1”]),
  as.numeric(row[“lon.p1”])),
                c(as.numeric(row[“lat”]), as.numeric(row[“lon”])),
                lonlat = T)
})
# Transform the column ‘time’ so that R knows how to interpret it.
geodf$time <- strptime(geodf$time, format = “%Y-%m-%dT%H:%M:%OS”)
# Shift the time vector, too.
geodf$time.p1 <- shift.vec(geodf$time, -1)
# Calculate the number of seconds between two positions.
geodf$time.diff.to.prev <- as.numeric(difftime(geodf$time.p1, geodf$time))
# Calculate metres per seconds, kilometres per hour and two LOWESS smoothers to get rid of some noise.
geodf$speed.m.per.sec <- geodf$dist.to.prev / geodf$time.diff.to.prev
geodf$speed.km.per.h <- geodf$speed.m.per.sec * 3.6
geodf$speed.km.per.h <- ifelse(is.na(geodf$speed.km.per.h), 0, geodf$speed.km.per.h)
geodf$lowess.speed <- lowess(geodf$speed.km.per.h, f = 0.2)$y
geodf$lowess.ele <- lowess(geodf$ele, f = 0.2)$y

Now, let’s plot all the stuff!

# Plot elevations and smoother
plot(geodf$ele, type = “l”, bty = “n”, xaxt = “n”, ylab = “Elevation”, xlab = “”, col = “grey40”)
lines(geodf$lowess.ele, col = “red”, lwd = 3)
legend(x=”bottomright”, legend = c(“GPS elevation”, “LOWESS elevation”),
       col = c(“grey40”, “red”), lwd = c(1,3), bty = “n”)

# Plot speeds and smoother
plot(geodf$speed.km.per.h, type = “l”, bty = “n”, xaxt = “n”, ylab = “Speed (km/h)”, xlab = “”,
     col = “grey40”)
lines(geodf$lowess.speed, col = “blue”, lwd = 3)
legend(x=”bottom”, legend = c(“GPS speed”, “LOWESS speed”),
       col = c(“grey40”, “blue”), lwd = c(1,3), bty = “n”)
abline(h = mean(geodf$speed.km.per.h), lty = 2, col = “blue”)

# Plot the track without any map, the shape of the track is already visible.
plot(rev(geodf$lon), rev(geodf$lat), type = “l”, col = “red”, lwd = 3, bty = “n”, ylab = “Latitude”, xlab = “Longitude”)

We will now use the OpenStreetMap package to get some maps from the internet and use them as a background for the track we just converted. There are several blocks for each map type. Check the comments in the first block what the different calls do.

First, we need to get the specific map. The ‘type’ argument controls which type of map we get.

map <- openmap(as.numeric(c(max(geodf$lat), min(geodf$lon))),
               as.numeric(c(min(geodf$lat), max(geodf$lon))), type = “osm”)

This next step is important and it took me a while to figure this out. We need to convert the maps we got with the function openmap() to a projection that fits our longitude-latitude format of positions. This will distort the maps in the plots a little. But we need this step because the track has to fit the map!

transmap <- openproj(map, projection = “+proj=longlat”)

# Now for plotting…
png(“map1.png”, width = 1000, height = 800, res = 100)
par(mar = rep(0,4))
plot(transmap, raster=T)
lines(geodf$lon, geodf$lat, type = “l”, col = scales::alpha(“red”, .5), lwd = 4)
dev.off()

map <- openmap(as.numeric(c(max(geodf$lat), min(geodf$lon))),
               as.numeric(c(min(geodf$lat), max(geodf$lon))), type = “bing”)

transmap <- openproj(map, projection = “+proj=longlat”)
png(“map2.png”, width = 1000, height = 800, res = 100)
par(mar = rep(0,4))
plot(transmap, raster=T)
lines(geodf$lon, geodf$lat, type = “l”, col = scales::alpha(“yellow”, .5), lwd = 4)
dev.off()

map <- openmap(as.numeric(c(max(geodf$lat), min(geodf$lon))),
               as.numeric(c(min(geodf$lat), max(geodf$lon))), type = “mapquest”)

transmap <- openproj(map, projection = “+proj=longlat”)
png(“map3.png”, width = 1000, height = 800, res = 100)
par(mar = rep(0,4))
plot(transmap, raster=T)
lines(geodf$lon, geodf$lat, type = “l”, col = scales::alpha(“yellow”, .5), lwd = 4)
dev.off()

map <- openmap(as.numeric(c(max(geodf$lat), min(geodf$lon))),
               as.numeric(c(min(geodf$lat), max(geodf$lon))), type = “skobbler”)

transmap <- openproj(map, projection = “+proj=longlat”)
png(“map4.png”, width = 1000, height = 800, res = 100)
par(mar = rep(0,4))
plot(transmap, raster=T)
lines(geodf$lon, geodf$lat, type = “l”, col = scales::alpha(“blue”, .5), lwd = 4)
dev.off()

map <- openmap(as.numeric(c(max(geodf$lat), min(geodf$lon))),
               as.numeric(c(min(geodf$lat), max(geodf$lon))), type = “esri-topo”)

transmap <- openproj(map, projection = “+proj=longlat”)
png(“map5.png”, width = 1000, height = 800, res = 100)
par(mar = rep(0,4))
plot(transmap, raster=T)
lines(geodf$lon, geodf$lat, type = “l”, col = scales::alpha(“blue”, .5), lwd = 4)
dev.off()

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