Discovering co-location patterns with aggregated spatial transactions and dependency rules

Mohomed Shazan Mohomed Jabbar, Colin Bellinger, Osmar R. Zaïane, Alvaro Osornio-Vargas
2017 International Journal of Data Science and Analytics  
Co-location pattern mining focuses on finding associations among spatial features. Existing co-location pattern mining techniques mainly rely on frequency based thresholds which discard the rare patterns and find the noisy patterns. This could be avoided by evaluating co-location patterns based on their statistical significance. Recent studies focused on association rule mining have successfully adopted statistical tests to find significant rules. By transforming spatial data to transaction
more » ... , the co-location pattern mining problem can be reduced to an association rule mining problem and such methods can be used to find co-location patterns robustly. A transactionization mechanism has been recently proposed to achieve this. However, this method ignores the effect of general instances, with nonoverlapping buffer regions, on the reference instances in their proximity. Addressing this, we propose a novel approach, AGT-Fisher, to robustly transform spatial data to transaction data and use statistically significant dependency rule searching methods to find co-location rules from them. Our work is motivated by an application in environmental health to investigate potential associations between air pollution and adverse birth outcomes in Canada. We used AGT-Fisher to B Mohomed Shazan Mohomed Jabbar mohomedj@ualberta.ca; find such associations from real datasets. The discovered colocation patterns were evaluated based on their statistical dependency and the empirical evidence, and results showed that our approach is more robust. Furthermore, we evaluated the resulting patterns to find spatial common and contrast sets, which are two special types of co-location patterns, to compare spatial regions and gain more insights.
doi:10.1007/s41060-017-0079-5 dblp:journals/ijdsa/JabbarBZO18 fatcat:dsqwtkvllzdt7po4jrs3h4kgra