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.
This work was supported, in part, by the Australia Research Council (ARC) Discovery Project under Grant No. DP140100545, program of Shanghai Technology Research Leader under Grant No. 16XD1424400, program of New Century Excellent Talents in University under Grant No. NCET-12-0358, and public interest research of Institute of Forensic Science, Ministry of Justice, PRC under Grant No. GY2016G-6.
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References
Mining Co-locations from Continuously Distributed Uncertain Spatial Data (2016). https://goo.gl/Q3OYns
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Liu, B., Chen, L., Liu, C., Zhang, C., Qiu, W. (2016). Mining Co-locations from Continuously Distributed Uncertain Spatial Data. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_6
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DOI: https://doi.org/10.1007/978-3-319-45814-4_6
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