GPS track data reduction algorithm to remove redundant data?

Question

We are creating a GIS interface to display GPS track data, for example. Imagine an initial dataset from a guy cycling around for an hour. A dataset like this, possibly with a new point recorded every 5 seconds, will be large and display it in a browser, or the handheld device will be complicated. In addition, the display of each individual point is usually not required because the user cannot visually resolve so much data.

So, for performance reasons, we are looking for algorithms that are good for "reducing" data like this so that the number of displayed points is significantly reduced, but in such a way that it does not compromise the misinterpretation of the data. For example, if our fictional cyclist stops for a drink, we certainly do not want to collect 100 lats / lone points in a cluster around 7-Eleven.

We know about clustering, which is good in order to look at a bunch of disabled points, but we need something that relates to tracks , as described above. Thank.

Answer 1

Generally, the best way to do this is:

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Answer 2

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