Journal of Intelligent Information Systems

, Volume 42, Issue 3, pp 485–505 | Cite as

Mining regional co-location patterns with kNNG

  • Feng QianEmail author
  • Kevin Chiew
  • Qinming He
  • Hao Huang


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.


Regional co-location pattern mining kNNG Variation coefficient 



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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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Hangzhou R&D CenterNetEase Inc.HangzhouPeople’s Republic of China
  2. 2.School of EngineeringTan Tao UniversityDuc Hoa DistrictVietnam
  3. 3.College of Computer Science and TechnologyZhejiang UniversityHangzhouPeople’s Republic of China
  4. 4.School of ComputingNational University of SingaporeSingaporeSingapore

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