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# Handling GPS Data with Python

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#### Automatisierte Medienanalyse

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course so was there seems to be excess something really visual we want plotted and just to give you an idea of this was 1 of around i did in in in hamburg so this is I just the longitude and latitude with the help of matplotlib at and this was so just to track just the line what happens if we lots not only aligned with the actual points with the Boston we see that we have a lot of points we have more than 10 thousand points actually so at this point I might think OK maybe 10 thousand points for small run is not there is way too much so so how can we produce it how can we reduce the number of points that we have to do with less data without actually destroying the gym what geometry of the of the track and so the really simple trick
is we use only every 150 points and in that case it works because it was running with the ball always the same as speed but this would not work if a and I have done this with a car or we also see that on straight lines like here of course you would need less points to describe straight line only 2 points actually but in a curve you would need more information described a curve C C Ds need to somehow using added them to to simplify it should be a track and 1 other than that it's quite often used is the so-called much last point out of him and I think it's best explained in the US in how it works so let's assume we have this GPS tracking and we want to simplify it and what a simplified means so we have to give some kind of of epsilon some kind of error that we say let's take to make that kind of error but anymore not not more than a certain epsilon
so that didn't just words stating the 1st point to the last strong straight line and this is epsilon um surroundings this excellent wine from the environment around that straight line and that it goes if now all points would be inside this epsilon environment then we could just use all points in between if it's not the case then take the point which is the furthest away with from this language that 1 and apply the same algorithm recursively
on the true the segments that's our by splitting at that point so 1 segment is that 1 year and the other related to you that 1 so we start with inspecting that when we see OK at all points are in this epsilon environment we can just remove that and now we do the
same here we have the problem of all points are included we take the 1 that is the 1st way so that once we got is played out from here true there and then from there to there so 1st this 1 we can remove that point here we have the same problem again and this is how this over them recursively works and we see that we we have simplified the track region reduced number of points and if we go back and forth see just even better and this is an idea that we can use and which is quite often actually used them to simplify and GPS tracker we will later make use of this so In this case I'd just use this this algorithm and the and and that point 1 also making it's the 1 where of course and so never ever use recursion in Python you will always run into problems and if you go to if do your recursions from you so if you have more than uh recursion the death of more than 100 1 thousand then you will get into trouble so it's better to reformulate this other them iteratively which makes more complicated but this is what I did here and yeah so I used an implementation of this iterative and they're just run it on the longer latitude and they can reduce the number of points from more and 12 thousand getting to less than 200 and this year the outcome we see as expected straight lines are really straight lines and here for for instance during a curfew more that's actually used and yeah that's it about this simplification so when I was doing this project with the the trucks I had only longitudinal latitude of course I was interested in the kind of finding out OK what was the heights uphill and downhill distance since the cobbler's actually doing but OK where it would make it the generation from was was looking around in in my tracks so this is detract from a from a bicycle rights and did so in in in Italy and then there have to elevation but I will now just remove it so that it's like it was in the project and I don't have in that case there's a really cool cool and extensions to expired called S R T and pi and as RTN tight stands for shuttle radar topography mission to the recent data type and that was mapping and maybe some of you remember in the year 2 thousand there was this huge idea not summation where they were uh using radar and find out the elevation almost everywhere on earth and this data is publicly available on this is just the yen interface for it and you can really easily use it to get on the relational data so you just imported as object and get paid and you say at elevation to the to the X files this
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