There are papers about this - it's not a trivial matter - it's not noise that you can simply filter out, it's a spatially- and temporarily-varying bias, because not only does it depend on where you are standing with your GPS unit downtown, but it also depends on where the GPS satellites are in their trajectory. That walk in downtown Seattle is right among the tallest skyscraper buildings in the city, and the GPS signals are known to reflect off those buildings and cause geometric effects like that for GPS in such downtown areas. However, there's also a very interesting phenomenon going on in the GPS data as seen in those maps above. Import and export GIS data, and go back in time with historical imagery. There may have there been a different instrument or method used by whoever entered that 3.52 into the title string, and also we did not take the elevation changes into account in the distance calculation (the elevation changes were available in that separate download link on the MapMyRun page and I didn't incorporate them into the dataframe here). Google Earth Pro on desktop is free for users with advanced feature needs. That 0.18 mile difference above is about 274 meters. head ()) print ( ' \n Num non-None Longitude records: ' str ( len ( df ))) print ( 'Num non-None Latitude records: ' str ( len ( df ))) print ( 'Num non-None Altitude records: ' str ( len ( df ))) print ( 'Num non-None Time records: ' str ( len ( df ))) print ( 'Num non-None Speed records: ' str ( len ( df ))) print ( ' \n Title string contained in track.name: ' track. DataFrame ( data, columns = columns ) print ( ' \n Dataframe head:' ) print ( df. append () import pandas as pd columns = df = pd. length_3d () for point_idx, point in enumerate ( segment. points ))) # Load the data into a Pandas dataframe (by way of a list) data = segment_length = segment. segments print ( 'Num segments: ' str ( len ( segment. tracks print ( 'Num segments: ' str ( len ( track. print ( 'Num tracks: ' str ( len ( gpx. parse ( open ( './route1141462231.gpx' )) # Files can have more than one track, which can have more than one segment, which have more than one point. # some of this cell's code came from this link - thank you! # import gpxpy gpx = gpxpy.
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