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I have a large collection (~160m pairs) of latitude and longitude values relating to e-scooter locations. Each lat/lon is to 6dp or more and as such many points lie very close to one another. I am trying to prune this dataset so that points within a certain distance (ideally 1m-5m) from one another are combined into a single new point. As well as this I am trying to remove outliers (points that are not close to any other) that occur due to GPS errors. The end goal is to use this pruned structure to create a GNN for route prediction.

I have tried rounding the points to 4dp and hashing them into a hashtable and taking the mean of the points that get hashed into the same slots, to round all points that lie within ~1.1m of each other into a single new point. However, due to outliers being present, this has either caused new points to sit inside buildings or be moved off footpaths and into the middle of roads.

AoNova
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  • Could you share what you've tried so far, and ideally illustrate what actual output you would like to produce from some given input? (https://stackoverflow.com/help/how-to-ask) – Vin Nov 10 '22 at 07:34

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