Sorry this is a bit long.

For a long time I’ve been tinkering with an app that uses the GPS on iPhones to 
show how far ‘as the crow flies’ the user is from some base point, which is set 
before the journey starts. It’s intended for walkers and cyclists, and I was 
using a beta version in the first French lockdown, which forbade one walking 
more than 1km from one’s house. I put the completion of this aside, but now 
we’re in the second French lockdown and it looks worth finishing - but I have 
some issues which I believe are not just down to my bad coding (though of 
course that’s more than possible), and I wonder if anyone else has thought 
about this stuff.

The basic idea is extremely simple, as you can imagine, and it wasn’t hard to 
code. The app keeps working out the point-to-point distance from the base and 
tests it against the permitted limit. If the limit is exceeded, it sounds an 
alarm. If the user goes back within the limit, the alarm stops. Everything else 
is just cosmetic, really.

The issue I have is that in every journey of say 40 minutes to an hour, the 
iPhone appears to generate a few ‘bad’ GPS readings. When I say bad, I mean way 
beyond the published accuracy of the GPS (which varies up to about 70m). 
Sometimes the GPS wants to tell me that I’ve walked half a kilometer in three 

I have analysed a lot of data (.gpx files) from other apps for walkers, drivers 
and cyclists and I don’t see such glaring anomalies; and using such apps on my 
main test phone (an iPhone X), I don’t see these strange readings affecting the 

Assuming this isn’t just a stupid coding error on my part (OK, a risky 
assumption), I need to find a solution, either by increasing the accuracy of 
the readings, or by finding the anomalies as they occur and eliminating them. I 
know no way of doing the first, and although I have ideas for the second (check 
apparent speed, for example), none seems foolproof. As the app works in real 
time, a retroactive curve-fitting solution is no good for me, even if my maths 
was up to coding it (Kalman filter, anyone?).

Has anyone tried anything like this, and if so, did you find any anomalies in 
the data, and how did you cope with them?

I’d be grateful for any ideas.

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