A framework of spatial co-location mining on MapReduce

Spatial association rule mining is a useful tool for discovering interesting relationships among spatial objects. Co-locations, or sets of spatial events which are frequently observed together in close proximity, are particularly useful for discovering their spatial dependencies. The computation of co-location mining is prohibitively expensive with increase in data size and spatial neighborhood. In this work, we propose to parallelize spatial co-location mining on distributed machines. A framework of parallel co-location mining based on MapReduce is presented.