From the abstract - &quot;Spatial association rule mining is a useful tool for discovering
correlations and 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. Although a number
of spatial co-location mining algorithms have been developed,
the computation of co-location pattern discovery remains prohibitively
expensive with large data size and dense neighborhoods.
We propose to leverage the power of parallel processing, in
particular, the MapReduce framework to achieve higher spatial
mining processing efficiency. MapReduce-like systems have been
proven to be an efficient framework for large-scale data processing
on clusters of commodity machines, and for big data analysis
for many applications. The proposed parallel co-location mining
algorithm was developed on MapReduce. The experimental result
of the developed algorithm shows scalability in computational
performance.&quot;