2 research outputs found
Spatial Co-location Pattern Mining - A new perspective using Graph Database
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 ,
and . 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
Event Centric Modeling Approach in Colocation Pattern Snalysis from Spatial Data
Spatial co-location patterns are the subsets of Boolean spatial features
whose instances are often located in close geographic proximity. Co-location
rules can be identified by spatial statistics or data mining approaches. In
data mining method, Association rule-based approaches can be used which are
further divided into transaction-based approaches and distance-based
approaches. Transaction-based approaches focus on defining transactions over
space so that an Apriori algorithm can be used. The natural notion of
transactions is absent in spatial data sets which are embedded in continuous
geographic space. A new distance -based approach is developed to mine
co-location patterns from spatial data by using the concept of proximity
neighborhood. A new interest measure, a participation index, is used for
spatial co-location patterns as it possesses an anti-monotone property. 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.Comment: 9 page