  Spatial co-location pattern mining refers to the task of discovering the
group of objects or events that co-occur at many places. Extracting these
patterns from spatial data is very difficult due to the complexity of spatial
data types, spatial relationships, and spatial auto-correlation. These patterns
have applications in domains including public safety, geo-marketing, crime
prediction and ecology. Prior work focused on using the spatial join. While
these approaches provide state-of-the-art results, they are very expensive to
compute due to the multiway spatial join and scaling them to real-world
datasets is an open problem. We address these limitations by formulating the
co-location pattern discovery as a clique enumeration problem over a
neighborhood graph (which is materialized using a distributed graph database).
We propose three new traversal based algorithms, namely $CliqueEnum_G$,
$CliqueEnum_K$ and $CliqueExtend$. We provide the empirical evidence for the
effectiveness of our proposed algorithms by evaluating them for a large
real-life dataset. The three algorithms allow for a trade-off between time and
memory requirements and support interactive data analysis without having to
recompute all the intermediate results. These attributes make our algorithms
applicable to a wide range of use cases for different data sizes.
