Abstract A co-location pattern is a group of spatial features/events that are frequently co-located in the same region. For example, human cases of West Nile Virus often occur in regions with poor mosquito control and the presence of birds. For colocation pattern mining, previous studies often emphasize the equal participation of every spatial feature. As a result, interesting patterns involving events with substantially different frequency cannot be captured. In this paper, we address the problem of mining co-location patterns with rare spatial features. Specifically, we first propose a new measure called the maximal participation ratio (maxPR) and show that a co-location pattern with a relatively high maxPR value corresponds to a colocation pattern containing rare spatial events. Furthermore, we identify a weak monotonicity property of the maxPR measure. This property can help to develop an efficient algorithm to mine patterns with high maxPR values. As demonstrated A preliminary version of the paper appeared as [13] . by our experiments, our approach is effective in identifying co-location patterns with rare events, and is efficient and scalable for large-scale data sets.
Besides the above representative co-location mining algorithms, Huang, Pei and Xiong address the problem of mining co-location patterns with rare spatial events REF .
Mining co-location patterns with rare events from spatial data sets
Geoinformatica
Geography Computer Science