Recent advances in machine learning have brought with them considerable attention in applying such methods to complex prediction problems. However, in extremely large dataspaces, a single neural network covering that space may not be effective, and generating large numbers of deep neural networks is not feasible. In this paper, we analyze deep networks trained from stacked autoencoders in a spatio-temporal application area to determine the extent to which knowledge can be transferred to similar regions. Our analysis applies methods from functional data analysis and spatial statistics to identify such correlation. We apply this work in the context of numerical weather prediction in analyzing large-scale data from Hurricane Sandy. Results of our analysis indicate high likelihood that spatial correlation can be exploited if it can be identified prior to training.