To load track_points into a GeoDataFrame, gdf, I passed fname to geopanda's gpd.read_file(). Something I love about GeoDataFrames is it allows you to carry out spatial operations on spatial data while also working with a pandas.Įarlier I defined the data file gps_test_data.gpx as fname. Because I wanted to do some further analysis, I reloaded track_points into a special type of DataFrame called a GeoDataFrame. What follows is an explanation of how geopandas and shapely to transform my GPS track points from Runkeeper into a new, even more interesting dataset.Įarlier I had used fiona to load track_points so that I could take a quick look at the data. In this post I attempt pay forward what I've learned from others and contribute back in particular to community of geospatial Python practitioners. My ability to work with this data is the product of other people sharing what they've learned about GPX data, Python programming, pandas, geopandas, folium, shapely and more. However, I found that the data needed a bit of processing in order to enhance its usefulness. With data in hand, I sought to carry out some basic analysis of my rides.
Fortunately they allow users to export their data as GPX files.
Now, Runkeeper provides quite a bit of in-app analytics, but mostly as part of their premium service. Figuring I'd reached a data saturation point in my rides - I pretty much take the same route to and from work everyday - I sought to do some analysis of my rides. For the past 6 months or so, I have been collecting GPS of my bike rides data via Runkeeper.
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