Zen Cycle writes:

On Monday, March 11, 2019 at 3:28:58 PM UTC-4, Radey Shouman wrote:
Zen Cycle writes:

On Sunday, March 10, 2019 at 6:35:02 PM UTC-4, Roger Merriman wrote:
Sir Ridesalot wrote:
Talking about GPS units on another thread reminded me of something else I
wondered if they do. Does a bicycle GPS unit show you the steepness of
roads? There's an area that I frequently ride where on road has short but
very steep hills, another road a mile or so east of it has much more
gradual hills whilst a third road to the west of the first one is a major
highway that can be ridden with a bicycle. What I'm wondering is this: if
someone unfamiliar with the area got there and used a GPS unit to show
those three roads, would the GPS unit show them the different gradients
of the roads? Or is that another function that they'd need to download or
otherwise install?

Cheers


Various mapping sites will show the gradient, and some GPS units will show
the gradient, in the same way that it can give improbable maximum speeds
they can also give improbable max gradients or sometimes on very short
ramps not notice it, there is a nasty little ramp nr my folks place, which
is the software flattens claiming 12% when it’s a fair cruel 25/30% even
more cruel this weekend with a 50mph headwind.

It's probably an averaging issue - taking enough samples before and
after the section so that it flattens the pitch.

It's the same basic issue as the speedometer kerfluffle. Numerical
differentiation amplifies noise.

Interesting how you characterize it as 'noise'.

For the speedometer the main source of noise is quantization error,
resulting from reducing a continuous wheel position to an integer number
of revolutions. For the 2-d field of altitudes obtained from a map I
suspect that the quantization of position, ie, the limited number of
data points, perhaps at irregular places, is the main source of noise.

How to turn topographical survey data into something that looks like a
continuous function is a whole field of study -- there are many ways to
go wrong, and no one perfect way to do it right.

In either case, errors that would be fairly small in altitude or
distance become larger when differentiated to estimate speed or
gradient.


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