Mining Co-locations from Continuously Distributed Uncertain Spatial Data

  • Bozhong LiuEmail author
  • Ling Chen
  • Chunyang Liu
  • Chengqi Zhang
  • Weidong Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)


A co-location pattern is a group of spatial features whose instances tend to locate together in geographic space. While traditional co-location mining focuses on discovering co-location patterns from deterministic spatial data sets, in this paper, we study the problem in the context of continuously distributed uncertain data. In particular, we aim to discover co-location patterns from uncertain spatial data where locations of spatial instances are represented as multivariate Gaussian distributions. We first formulate the problem of probabilistic co-location mining based on newly defined prevalence measures. When the locations of instances are represented as continuous variables, the major challenges of probabilistic co-location mining lie in the efficient computation of prevalence measures and the verification of the probabilistic neighborhood relationship between instances. We develop an effective probabilistic co-location mining framework integrated with optimization strategies to address the challenges. Our experiments on multiple datasets demonstrate the effectiveness of the proposed algorithm.


Discretization Method Uncertain Data Location Instance Probabilistic Participation Deterministic Data 
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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bozhong Liu
    • 1
    • 2
    Email author
  • Ling Chen
    • 2
  • Chunyang Liu
    • 2
  • Chengqi Zhang
    • 2
  • Weidong Qiu
    • 1
  1. 1.School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Centre for Quantum Computation and Intelligent SystemsUniversity of Technology SydneySydneyAustralia

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