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 language||English (US)|
|Number of pages||25|
|Journal||International Journal of Geographical Information Science|
|State||Published - Nov 16 2020|
Bibliographical noteFunding Information:
This study was supported by the National Natural Science Foundation of China (NSFC) [41730105, 41471385]; National Key Research and Development Foundation of China [2016YFB0502303]; U.S. National Science Foundation (NSF) [1737633, 0940818, 1029711, 1541876, IIS-1218168, IIS-1320580]; Advanced Research Projects Agency - Energy, U.S. Department of Energy [DE-AR0000795]; U.S. Department of Defense [HM0210-13-1-0005, HM1582-08-1-0017]; U.S. Department of Agriculture [2017-51181-27222]; U.S. National Institute of Health [KL2 TR002492, TL1 TR002493, UL1 TR002494]; OVPR Infrastructure Investment Initiative, University of Minnesota; Minnesota Supercomputing Institute (MSI), University of Minnesota. The authors thank the editors, the reviewers, and the members of the spatial computing research group at the University of Minnesota for their helpful comments. We also thank Kim Koffolt for improving the readability of this paper.
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- Spatial data mining
- anomalous spatial co-locations
- multiple significance tests
- pattern reconstruction
- region detection