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Say I have two pandas dataframes in a format similar to this one where each 'Point' represents a person and where they were at. Assume that there are multiple line strings representing different people walking in a specific path. I want to compare based on foot traffic between the two days, whether there is 'similarity' (e.g., based on deviation parameter that could be set) between those of one day vs another one.

Name, Latitude, Longitude, Date
Jordan,<lat>,<lon>, 2017-08-01T00:00:05
Jordan,<lat>,<lon>, 2017-08-01T00:00:08
Jordan,<lat>,<lon>, 2017-08-01T00:00:10
Jordan,<lat>,<lon>, 2017-08-01T00:00:16 
Sarah,<lat>,<lon>, 2017-08-01T00:00:20
Sarah,<lat>,<lon>, 2017-08-01T00:00:30
Jordan,<lat>,<lon>, 2017-08-01T00:00:32

I use shapely to construct paths/lines that represent where each person was at on a given day and time.

How I generate the lines...

dayonegeom = [Point(ab) for ab in zip(dayonedataframe.longitude, daytwodataframe.latitude)]

dayonegeodataframe = GeoDataFrame(dayonedataframe, geometry=dayonegeom)
daytwogeodataframe = GeoDataFrame(dayonetwoframe, geometry=daytwogeom)

What is the best way for me to filter the dataframe or GeoDataFrame such that only the paths that are the most 'similar' to each other are kept, while eliminating the ones that are not?

Looking for the best way to do this, be it in pandas before the data gets converted to a geodataframe, or geopandas after it's been converted.

  • Does it matter who the person is from day 1 to day 2 or just the path taken? How would you handle multiple people taking the same path on day 1 and day 2? Or, completely random paths? Also, your title borders on "too broad" or "opinion based" and may result in the closure of your question. Please try to focus your post to a single question that is not based on opinion. – Aaron Aug 14 at 1:51
  • Doesn't matter who the person is. If there were multiple people taking same path that is similar enough, it would be good to keep. If the name of the people were 'accessible', that would be helpful. – Rolando Aug 14 at 2:30

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