Title: MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility,
Abstract: Collective urban mobility embodies the residents' local insights on the city.
Mobility practices of the residents are produced from their spatial choices,
which involve various considerations such as the atmosphere of destinations,
distance, past experiences, and preferences. The advances in mobile computing
and the rise of geo-social platforms have provided the means for capturing the
mobility practices; however, interpreting the residents' insights is
challenging due to the scale and complexity of an urban environment, and its
unique context. In this paper, we present MobInsight, a framework for making
localized interpretations of urban mobility that reflect various aspects of the
urbanism. MobInsight extracts a rich set of neighborhood features through
holistic semantic aggregation, and models the mobility between all-pairs of
neighborhoods. We evaluate MobInsight with the mobility data of Barcelona and
demonstrate diverse localized and semantically-rich interpretations.