Discovering regions of anomalous spatial co-locations

Jiannan Cai, Min Deng, Yiwen Guo, Yiqun Xie, Shashi Shekhar

Research output: Contribution to journalArticlepeer-review

Abstract

Regions of anomalous spatial co-locations (ROASCs) are regions where co-locations between two different features are significantly stronger or weaker than expected. ROASC discovery can provide useful insights for studying unexpected spatial associations at regional scales. The main challenges are that the ROASCs are spatially arbitrary in geographic shape and the distributions of spatial features are unknown a priori. To avoid restrictive assumptions regarding the distribution of data, we propose a distribution-free method for discovering arbitrarily shaped ROASCs. First, we present a multidirectional optimization method to adaptively identify the candidate ROASCs, whose sizes and shapes are fully endogenized. Furthermore, the validity of the candidates is evaluated through significance tests under the null hypothesis that the expected spatial co-locations between two features occur consistently across space. To effectively model the null hypothesis, we develop a bivariate pattern reconstruction method by reconstructing the spatial auto- and cross-correlation structures observed in the data. Synthetic experiments and a case study conducted using Shanghai taxi datasets demonstrate the advantages of our method, in terms of effectiveness, over an available alternative method.

Original languageEnglish (US)
JournalInternational Journal of Geographical Information Science
DOIs
StateAccepted/In press - 2020

Keywords

  • Spatial data mining
  • anomalous spatial co-locations
  • multiple significance tests
  • pattern reconstruction
  • region detection

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