Spatial association analysis: A literature review

The immense explosion of geographically referenced data calls for efficient discovery of spatial knowledge. Spatial association analysis is a typical data mining approach for discovering spatial knowledge. Associate rules are patterns of form X→Y, where pattern Y is likely to occur when pattern X occurs. One of the most famous patterns, Diapers → Beer, is a typical association rule example. Spatial association analysis adds spatial features to association analysis, which makes it different from traditional association analysis. Previous research on spatial association analysis mainly focuses on collocation pattern mining. Collocation is the presence of more than two spatial objects at the same or close distances from each other. Spatial collocation patterns associate the co-existence of non-spatial features in a spatial neighborhood. It has many applications such as ecology, medical science, crime data analysis, et al. A typical example of spatial collocation patterns is bird and house, which tends to stay together. The figure below gives a graph example of collocation pattern.