Mining co-location patterns with rare events from spatial data sets
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 co-location 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 co-location 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 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. © Springer Science + Business Media, LLC 2006.
Duke Scholars
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Geological & Geomatics Engineering
- 3704 Geoinformatics
- 0909 Geomatic Engineering
- 0499 Other Earth Sciences
- 0406 Physical Geography and Environmental Geoscience
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Geological & Geomatics Engineering
- 3704 Geoinformatics
- 0909 Geomatic Engineering
- 0499 Other Earth Sciences
- 0406 Physical Geography and Environmental Geoscience