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Mining regional co-location patterns with kNNG

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Abstract

Spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework accounting for both variety of neighborhood distances and spatial heterogeneity. By adopting k-nearest neighbor graph (kNNG) instead of distance threshold, we propose “distance variation coefficient” as a new measure to drive the mining operations and determine an individual neighborhood relationship graph for each region. The proposed mining algorithm outputs a set of regions with each of them an individual set of regional co-location patterns. The experimental results on both synthetic and real world data sets show that our framework is effective to discover these regional co-location patterns.

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Notes

  1. As a direction of our future study we may consider the continuous type of data sets.

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Acknowledgements

This work is partly supported by National Key Technologies R&D Program of China under Grant No. 2011BAD21B02, in which Chiew’s work is partly supported by National Natural Science Foundation of China under Grant No. 61272303.

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Authors and Affiliations

  1. Hangzhou R&D Center, NetEase Inc., No.599, Wangshang Road, Bingjiang District, Hangzhou, Zhejiang Province, People’s Republic of China

    Feng Qian

  2. School of Engineering, Tan Tao University, Duc Hoa District, Long An Province, Vietnam

    Kevin Chiew

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, People’s Republic of China

    Qinming He

  4. School of Computing, National University of Singapore, Singapore, Singapore

    Hao Huang

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  1. Feng Qian
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  2. Kevin Chiew
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  3. Qinming He
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  4. Hao Huang
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Correspondence to Feng Qian.

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Qian, F., Chiew, K., He, Q. et al. Mining regional co-location patterns with kNNG. J Intell Inf Syst 42, 485–505 (2014). https://doi.org/10.1007/s10844-013-0280-5

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  • DOI: https://doi.org/10.1007/s10844-013-0280-5

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