Discovering Co-location Patterns from Spatial Domain using a Delaunay Approach

Abstract

Spatial co-location patterns represent the subsets of boolean spatial features whose instances are often located in close geographic proximity. Spatial statistics and data mining approaches are used to identify co-location patterns from spatial data sets. Spatial proximity is the important concept to determine the co-location patterns from massive data sets. A Delaunay diagram based co-location mining approach is developed to mine co-location patterns from spatial data by using the concept of spatial proximity. Delaunay diagram is used to model the spatial proximity between the objects. This approach eliminates the parameters from the user to define neighborhood of objects and avoids multiple test and trail repetitions in the process of mining. An algorithm to discover co-location patterns are designed which generates candidate locations and their table instances. Finally the co-location rules are generated to identify the patterns. The results of the experiments have been discussed