Mining Co-locations from Continuously Distributed Uncertain Spatial Data

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

Abstract

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.

Keywords

Covariance Transportation 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Bozhong Liu
    • 1
    • 2
  • 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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