Abstract
AbstractThe discovery of spatial association rules is a core task in spatial data science projects and focuses on extracting useful and meaningful spatial patterns and relationships from spatial and geometric information. Many spatial phenomena have been modeled and represented by fuzzy spatial objects, which have blurred interiors, uncertain boundaries, and/or inexact locations. In this paper, we introduce a novel method for mining spatial association rules from fuzzy spatial data. By allowing users to represent spatial features of their applications as fuzzy spatial objects and by employing fuzzy topological relationships, our method discovers spatial association patterns between spatial objects of users’ interest (e.g., tourist attractions) and such fuzzy spatial features (e.g., sanitary conditions of restaurants, number of reviews and price of accommodations). Further, this paper presents a case study based on real datasets that shows the applicability of our method.KeywordsSpatial data scienceSpatial association ruleSpatial fuzzinessFuzzy spatial dataFuzzy topological relationship
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