Spatial co-location pattern discovery using multiple neighborhood relationship function

Abstract

Co-location pattern discovery is a process to find a subset of Boolean spatial feature that is frequently located in the same geographic area. There are some approaches have used for this process. Mostly co-location mining discovery has been done for point type and the feature has the same domain. But in reality spatial data has three types, which are point, line, and polygon. In this paper, we tried to discover spatial co-location pattern that involves three types of data spatial from a different domain. We propose multiple neighborhood relationship function to find neighborhood relation from the multiple types and multiples domains of data spatial and apply co-location mining with join less approach to find co-location pattern. The evaluation of our proposed method that using real data shows that multiple neighborhood relationship function is needed to extract the correct and complete spatial relationship to the data that have expansion of the data types and heterogeneous data source.