Can slope filtering be improved?

Started by Andrew Heard, November 22, 2017, 11:13:09

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Andrew Heard

Our hard working friend @0709 has created a very interesting and insightful test track to expose some current shortcomings with Locus slope filtering. What it shows is a smearing of track slope resulting a deceptively lower % at places where the slope is changing. Because SRTM elevation data is only every 30 x 40 metres it may not be possible to improve the situation much but I thought I'd ask...

Does this really matter? Well say there is a short sharp upcoming hill, say +20% slope, but only for 300 metres. Locus may only show this tortuous climb as +10%.

As shown here in Garmin Basecamp (also similar in Gps Visualizer) the attached test track has precise transitions
from 0% to 25%

Basecamp displays the slope accurately as 0%, +25% and -25%.

Locus on the other hand has quite aggressive filtering. The first +25% 'hill' starts at exactly 1.9km but the Locus chart shows the slope slowing increasing at 1.5km and only reaches the proper +25% at 2.3km.


If the track style is set to manual slope coloring -25% to +25% you would expect the color to be green from 0km to 1.9km then dark red/ brown after 1.9km. But instead the color starts changing thru various shades over a distance of 500 metres, starting before the hill even begins:


One real-world shortcoming is when a steep slope is less than 500m long. For example below the 25% slope is 200m long, then flat again. Locus calculates the slope as only 15%. And the track color is pale green/ orange for only 100m.

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Menion

Good day Andrew,
thank you for a precise description of a problem.

Unfortunately only what I may do now, is to create an new task in our "task system", that will collect problems related to filtering of measured variables with a small promise, that I'll look at it more completely during next year. Thanks for understanding.
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Andrew Heard

Quote from: menion on November 24, 2017, 08:29:21
Good day Andrew,
thank you for a precise description of a problem.
@menion - many more thanks to @0709 for the detailed analysis than to me (just reporting his work).
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