MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility
This work addresses the problem of understanding complex urban mobility patterns for city planners and researchers, though it appears incremental as it builds on existing methods for feature extraction and modeling.
The authors tackled the challenge of interpreting residents' urban mobility insights by developing MobInsight, a framework that extracts semantic neighborhood features and models mobility between neighborhoods, demonstrating diverse localized interpretations using Barcelona mobility data.
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.