Using cluster analysis as alternative to point in polygon assignment
Discriminant analisys (a.k.a. supervised  classification) is the way to go. You adjust the model by using the coordinates of the points and the information on the node they belong to. As a result, you obtain a model you can use to predict the node for new points as they are known. Linear discriminant analysis is one of the simplest algorithms.
I'm interested to approach the confirming point in polygon problem from another direction. I have a dataframe containing series of coordinates, known to be in certain polygon (administrative area). I have other dataframes with coordinates not assigned to any admin area. Would using SciKit offer an alternate means to assign these to the admin area.
Example:
I know (x, y) point 1 is in admin area a if (x, y) point 2 is within specified radius of point (1, i) can assign it to the same admin area. Does this approach sound viable?