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I am trying to train a model to predict the location of a storm at a given time. The dataset includes the longitude and latitude of the storm at the given "timestamps" but I am not sure if that is the best way to represent the location as it doesn't likely have a linear relationship.

Is there a way to combine the longitude and latitude into a feature that can be used for training? I was thinking about creating "grids" to represent spaces but I'm not sure how I would go about creating these grids or converting them back to a long/lat range.

Any help is appreciated. Thanks.

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There are a lot of things one could do with that information.

Lat. and long. are the most precise data one can get on location, but that data itself may not say much about anything, really.

When I read this:

predict the location of a storm at a given time

the first things that come into my mind are: what would define each coordinate point? You could get terrain height, closeness to some geographic accident... Things that would actually impact on the weather, more precisely, storms.

This is all intuitive. I'm not sure this can help in any way, but hopefully, it will at least help to think of something actually useful.

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