(Over)Analyze that ride!

I am a bit of a map geek. Or rather I am a bit of a geophysical information geek. Since I was a wee tyke I have collected maps and sundry related items. I even started a course in cartography in my early college days, but then the infamous “Greetings” letter came from my Uncle Sam and I was shipped off to exotic places before I got too far into those studies. Never got back to the academic realm of map making, but I did have a lot of practical experience using maps in those days.

On the geek equipment front I acquired a handheld GPS units once they became relatively affordable and small enough to mount on the handlebars. I use it frequently, with downloable ‘map’ sets that provide a lot of functionality. Stranded under an overhang in Pisa during a horrendous thunderstorm and I wanted to know how to find the train station so I can get back to our room in Lucca so I queried the GPS and it showed me two relatively close to where we were standing. Riding into Bolzano for the first time, a quick query helped me locate the Museum of Anthropology then another helped me find a hotel. Cool stuff.

Beyond planning rides, we often use maps, GPS data, and other GIS sources to analyze rides after the fact. It gets a bit tricky, what with sampling errors, incorrect data, and such, but occasionally it is useful. Sometimes the data just hints at possibilities; when I was injured in a suspected hit and run in 2002, one curious piece of data was the GPS interval showing a max speed of 45MPH near the site I was found. That was either a major error in the data, or I got drop kicked by something going faster than the 35MPH limit. As a mis-matched couple, The Wife being a non-climber and I more of one, there are ongoing discussions about hills we climb. For example:

her: that was a gruesome climb, had to be at least a 15% grade.

me: naw, not more than 10% max.

And back and forth it goes until we sit down with maps, software, etc. and get some reasonable, independent estimate of the grade in question. Old days it would have been a map wheel on a USGS topo map then some mental calculations (or a slide rule), with all the attendant problems of sampling error for the map wheel over short distances, interpolation error reading the topo map, etc. These days it is all software driven, either from mapping programs or using data collected by the GPS unit. And for the record, the truth in most debates is usually in the middle: The Wife tends to overestimate, I tend to underestimate.

So far there have been no debates on the gradient of our climb up Nacimiento-Ferguson road from Highway 1 last week. Easy to calculate the average grade for the climb from Hwy 1 to the summit; right around 2600′ of climbing in 7.3 road miles so 6.7% average. But we knew there were spots where the grade was a bit steeper, and some more slack spots. But nothing to tweak the usual gradient arguments. The recorded GPS data from that ride is pretty clean; had good satellite coverage, no major weather events, etc. so a lot of relatively good data. A quick look at the elevation profile shows that in the interior the barometric pressure rose slightly so equivalent ‘peaks’ are higher when we rode in the morning than in the afternoon but interestingly the summit and Hwy 1 elevations are relatively equal coming and going.

I have used GPSVisualizer to produce maps from GPS data for a few years now, e.g. the route of our honeymoon tour. A relatively new feature is slope (i.e., gradient) analysis of a GPS track. They do have one slight problem with this part of the analysis: they labeled the Y axis as degrees of slope, but I am quite certain that we never hit a 24% slope [a 13.5 degree grade is 24.0%; for the mathematicians, the conversion is 100*tan(slope-angle)]. The 13.5 seems more reasonable if I assume it is a percent gradient number. Overall, the graph looks like a pleasant, but challenging, climb. A few tougher spots, some slack spots, lots of ups and downs. Now I need to run a few of my older GPS tracks through this analysis. The map geek lives!