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- W2783168339 abstract "This paper describes a framework combining graph theoretical tools and metrics with machine learning to analyze big geospatial data. By combining multiple methods targeted to different levels of resolution, this approach detects subtle normalcy patterns that would otherwise remain hidden to any single approach. Initial feasibility testing shows the applicability of the proposed methods to data from trip records of New York City taxis." @default.
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- W2783168339 date "2017-12-01" @default.
- W2783168339 modified "2023-09-23" @default.
- W2783168339 title "Multiscale graph theoretical tools reveal subtle patterns in big geospatial data" @default.
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- W2783168339 doi "https://doi.org/10.1109/bigdata.2017.8258328" @default.
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