Mining causal rules hidden in spatial co-locations based on dynamic spatial databases

Spatial co-locations represent the subsets of spatial features which are frequently located together in a geographic space. Spatial co-location mining has been a research hot in recent years. But the research on causal rule discovery hidden in spatial co-locations has not been reported. Maybe the features in a co-location accidentally share the similar environment, and maybe they are competitively living in the same environment, they themselves have no causal relationships. So mining causal rules in amount of prevalent co-locations is more interesting. This paper proposes a novel algorithm to mine causal rules from prevalent co-locations based on dynamic spatial databases. Because of large collections of prevalent co-locations and amount of rules in one co-location, the computational cost for the discovery is high, thus the pruning strategies are presented to solve the problem in an acceptable period of time. The extensive experiments evaluate the proposed algorithms with “real + synthetic” data sets and the results show that causal rules are just about 60% of co-location rules, and which are more powerful.