Algorithm of regional co-location patterns based on rough set

doi:10.13474/j.cnki.11-2246.2022.0298

Abstract

Abstract: Through co-location pattern mining, we can find the set of events frequently occurring in nearby locations, which provides important decision support for revealing the symbiosis law between geographical phenomena.Due to the spatial heterogeneity of co-location patterns, the existing methods can not detect the distribution of co-location patterns.Therefore, in this paper, we detect the distribution regions of isotopic patterns from the direction of proximity of geographical attributes, and propose a local co-location pattern mining method based on rough sets.Firstly, infrequent co-location patterns are extracted from the global perspective as candidate local co-location patterns. Then, the locations of the candidate co-location patterns are processed, and the attributes of hot spots are used as rough data sets to detect the natural distribution regions of local co-location patterns. Finally, the frequency of these local regions is measured and all frequent local co-location patterns are generated. Through experiments and applications, it is found that this method can not only detect the similarity region of local spatial distribution of the same location pattern, but also reflect the geographical attribute information of the same location pattern distribution region.

Key words: rough set, regional co-location patterns, geographical attributes, urban facilities, POI

CLC Number:

P208

WU Jing, FU Youjie, CHENG Penggen. Algorithm of regional co-location patterns based on rough set[J]. Bulletin of Surveying and Mapping, 2022, 0(10): 80-85,104.

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