  A spatial co-location pattern represents a subset of spatial features whose
instances are prevalently located together in a geographic space. Although many
algorithms of mining spatial co-location pattern have been proposed, there are
still some problems: 1) they miss some meaningful patterns (e.g.,
{Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase),
algaedead(decrease)}), and get the wrong conclusion that the instances of two
or more features increase/decrease (i.e., new/dead) in the same/approximate
proportion, which has no effect on prevalent patterns. 2) Since the number of
prevalent spatial co-location patterns is very large, the efficiency of
existing methods is very low to mine prevalent spatial co-location patterns.
Therefore, first, we propose the concept of dynamic spatial co-location pattern
that can reflect the dynamic relationships among spatial features. Second, we
mine small number of prevalent maximal dynamic spatial co-location patterns
which can derive all prevalent dynamic spatial co-location patterns, which can
improve the efficiency of obtaining all prevalent dynamic spatial co-location
patterns. Third, we propose an algorithm for mining prevalent maximal dynamic
spatial co-location patterns and two pruning strategies. Finally, the
effectiveness and efficiency of the method proposed as well as the pruning
strategies are verified by extensive experiments over real/synthetic datasets.

Databases