Spatial data mining is the process of discovering interesting and previously unknown but potentially useful patterns from large spatial data sets. The explosive growth of spatial data and widespread use of spatial databases emphasize the need for the automated discovery of spatial knowledge. Applications include location-based services; studying the effects of climate; land use classification; predicting the spread of disease; creating high resolution, three-dimensional maps from satellite imagery; finding crime hot spots; and detecting local instability in traffic. Extracting patterns from spatial data sets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. In this entry, spatial data mining methods for different spatial patterns are discussed, and ...