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I have datasets containing continuous GPS streams of data that represent routes or trails. For any given route, I probably have at least 5 (in some cases many more) different sets of data. Given that it's uncorrected GPS data, there can be a lot of variation in the location of the route.

Is there a way to algorithmically computing the "best" representation of the location of the route? Does anyone know of an implementation in existing softare? The only literature I've found thus far is this, which looks promising, but would be custom development.

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up vote 3 down vote accepted

This was briefly discussed on the Statistical Analysis forum at .

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Ummm. Sort of; to partially quote your answer "A direct combination of two paths is highly problematic.", which I take to mean that this is in the category of problems not readily solved for automatically. The trouble would appear to be identifying the discrete bits of ridgeline and then connecting them, is that correct? – Herb Sep 23 '10 at 19:28
@Herb: Yes, the trouble usually lies in connecting the bits. (The bits are fairly straightforward to identify.) Where many of the data streams diverge, there is little "consensus" about the actual route and you don't get a ridge, so the difficulty is related to the uncertainty in the data--which is a natural and intuitively obvious phenomenon. You could do something like spline the bits of ridge together, which would get you an automatic result, but whether it would be "best" in any sense is questionable. – whuber Sep 23 '10 at 21:15
I'm experimenting with a spatial analysis approach - adding values in a continuous space, in effect - as well as attempting discrete analysis by segmentation and cubic spline curve fitting. As you say, some combination of the two (yikes!) may be of interest. – Herb Sep 24 '10 at 13:37
@Herb: Sounds like fun! Creating a statistical model of variation among the multiple paths would provide excellent guidance for what is useful and meaningful to do. – whuber Sep 24 '10 at 16:00

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