Methodology for Mining, Discovering and Analyzing Semantic Human Mobility Behaviors
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Several institutes produce large semantic data sets about daily activities and human mobility. The analysis and the understanding of these data are crucial for urban planning, socio-psychology and political sciences or epidemiology. Currently, none of usual data mining process is customized for a complete analysis of semantic mobility sequences from data to understandable behaviors. Based on an extended review of the literature, we define in this article a new methodological pipeline, SIMBA (Semantic Indicators for Mobility and Behavior Analysis), for mine and analyze semantic mobility sequences in order to discover coherent information and human behaviors. A framework for semantic sequence mobility analysis and clustering explicability integrating different complementary statistical indicators and visual tools is proposed. To validate this methodology, we use a large set of real daily mobility sequences obtained from one household-travel survey. Complementary knowledge are automatically discovered help to this method.
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