MobInsight

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 article, 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.