Rough set theory partitions a universe using single-layered granulation. The equivalence classes induced by rough sets are based on discretized values. Considering the fact that the spatial data are continuous at large, discretizing them may cause loss of data. Neighborhood approximations can lead to closely related coverings using continuous values. Besides, the spatial attributes also need to be given due consideration and should be handled unlike non-spatial attributes in the process of dimensionality reduction. This chapter analyzes the use of neighborhood rough sets for continuous data and handling spatially correlated attributes using rough sets.