Finally made it out to Folsom Lake for a fine day of sailing and GPS track collecting. Once I was back in the lab, I downloaded the track data with gpsbabel, and was ready to import the data into GRASS.
# import GPX from GPS:gpsbabel -t -i garmin -f /dev/ttyS0 -o gpx -F trip1.gpx
I was particularly interested in how fast we were able to get the boat going, however my GPS does not keep track of its speed in the track log. Also, I had forgotten to set GPS up for a constant time interval between track points. Dang. In order to compute a velocity between sequential points from the track log I would need to first do two things: 1) convert the geographic coordinates into projected coordinates, and 2) compute the associated time interval between points.
The GDAL/OGR tools provide one of the simplest means of simultaneously converting between formats AND coordinate systems. The GPX file contains both a line and point representation of the log; the point version seems simpler to work with. Initially I used the default output format (ESRI shapefile), however I quickly discovered that the datetime information from the GPX file was silently truncated to just the date part. Turns out that GDAL/OGR can now write vector data inSQLite format-- storing both the geometry and attributes into a single file, with a table structure very similar to PostGIS. Seems like a great replacement for shapefiles, that can deal with complex data types, long field names, and probably very large geometries.
# convert into sqlite DB with geometry-- better preservation of attributesogr2ogr -F "SQLite" -t_srs '+proj=utm +zone=10 +datum=NAD83' trip1_track_points.db trip1.gpx track_points# import, we are using a sqlite DB back-endv.in.ogr dsn=trip1_track_points.db out=folsom_trip1_pts
Next, we import the data (in projected coordinates) into a GRASS location/mapset, with a SQLite database back-end. The data are now in a format that can be easily imported into R for velocity calculations, and then sent back to GRASS for further processing. An example R session is presented below. Track points are in chronological order, so we can use the diff() function for computing sequential time and distance intervals.
Figure: Velocity vs. Bearing
Further rocessing in R
library(spgrass6)library(RColorBrewer)library(plotrix)# read vector points# alternatively we probably could have read in the GPX format directlyx <- readVECT6('folsom_trip1_pts')# convert date/time fieldx@data$date_time <- as.POSIXct(strptime(x@data$time, format="%Y/%m/%d %H:%M:%S+00"))# distance intervals (meters)dx <- diff(x@coords[,1])dy <- diff(x@coords[,2])x.dist <- sqrt(dx^2 + dy^2)# time interval (seconds)x.dt <- diff(x@data$date_time)# compute velocity and convert to mphx.meters_per_second <- x.dist / as.numeric(x.dt)x.mph <- x.meters_per_second * 2.2369# compute bearingx.bearing <- atan2(dy, dx) * 180/pi# add back to original DF# note that first point has no velocity or bearingx@data$mph <- NAx@data$bearing <- NAx@data$mph[-1] <- x.mphx@data$bearing[-1] <- x.bearing# check visuallyspplot(x, 'mph', col.regions=brewer.pal('Spectral', n=5), cuts=5)spplot(x, 'bearing', col.regions=brewer.pal('Spectral', n=5), cuts=5)# keep only interesting information in the attribute table# note that this destroys the original object's tablex@data <- x@data[, c('cat','ele','time','date_time','mph','bearing')]# save data back to GRASSwriteVECT6(x, 'folsom_trip1_pts_dt')# fancy plotspng(file='speed_vs_bearing.png', width=1000, height=500)par(mfcol=c(1,2))plot(mph ~ bearing, data=x@data, xlim=c(-180,180), xlab="Angle from East (positive angles are CCW)",ylab="Velocity (mph)", main="Velocity Field", col=color.scale(mph,c(0,1,1),c(1,1,0),0), pch=16, cex=1.5)## polar plot:polar.plot(x@data$mph[-1], x@data$bearing[-1] ,main="Sailing Direction from East (0 Deg)", line.col='blue', lwd=2)dev.off()
