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A hidden markov model for matching spatial networks

Benoit Costes, Julien Perret


Datasets of the same geographic space at different scales and temporalities become increasingly abundant, paving the way to new scientific researches. These require data integration, which implies to link homologous entities in a process called data matching that remains a challenging task despite a quite substantial literature, because of data imperfections and heterogeneities. In this paper, we present a novel approach for matching spatial networks based on a Hidden Markov Model (HMM) that takes fully benefit of the underlying topology of networks. The approach is assessed using 4 heterogeneous datasets (streets, roads, railway and hydrographic networks), showing that the HMM algorithm is
robust in regards with some data heterogeneities and imperfections geometric discrepancies and differences in level of details) and adapted to match any type of spatial networks. It also has the advantage to require no mandatory parameters, as proven by a sensitivity exploration, except a distance threshold that filters potential matching candidates in order to speedup the process. Finally, a comparison with a commonly cited approach highlights good matching accuracy and completeness.

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