Xuguang Bao

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Spatial co-locations represent the subsets of spatial features which are frequently located together in a geographic space. Discovering co-locations has many useful applications. For example, co-located plant species discovered from plant distribution datasets can contribute to the analysis of plant geography, phytosociology studies, and plant protection(More)
Spatial co-location patterns represent the subsets of Boolean spatial features, and the instances of the pattern are frequently located together in a geographic space. Most existing co-location pattern mining methods mainly focus on whether spatial feature instances are frequently located together. However, that the occurrence of neighbor relationships is(More)
Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It is difficult to discover co-location patterns because of the huge amount of space data. A common framework for mining spatial co-location patterns employs a level-wised search method to discover co-location patterns, and(More)
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