MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility

TL;DR

MobInsight is a framework that leverages semantic neighborhood features to provide localized, interpretable insights into urban mobility patterns, addressing the complexity of urban environments.

Contribution

It introduces a novel semantic aggregation approach for neighborhood features and models mobility between all neighborhood pairs for urban analysis.

Findings

Demonstrates diverse localized interpretations of mobility in Barcelona.

Shows the effectiveness of semantic features in understanding urban mobility.

Provides a scalable method for urban mobility analysis.

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.