I'm contemplating a project where I try to take a time series of maps of polygons (which have values) and predict the next map of polygon values.
If it were a regular grid, it'd be a straightforward to apply a TCN with a 3D convolution, and causal padding on the time dimension. But the grid is irregular.
It seems like the simplest thing to do would be to define a grid, and assign the centroids of the polygons to the nearest gridpoint, padding the rest with zeros. Depending on resolution of the grid I'd have a lot of sparsity, but I think that Keras can handle that pretty well, right?
Anyway, I'd appreciate suggestions on ways of dealing with this irregular topology. I see papers on generalizing CNNs to graphs, but those don't seem super well-developed. Are any of those methods well-suited to this purpose, are there other good methods for predicting spatial time series on irregular grids, or is my sparse grid idea a reasonable hack?