Ensemble-spotting: Ranking urban vibrancy via POI embedding with multi-view spatial graphs

Abstract

Vibrant residential communities are defined as places with permeability, vitality, variety, accessibility, identity and legibility. Developing vibrant communities can help boost commercial activities, enhance public security, foster social interaction, and thus yield livable, sustain-able, and viable environments. However, it is challeng-ing to understand the underlying drivers of vibrant com-munities to make them traceable and predictable. To-ward this goal, we study the problem of ranking vibrant communities using human mobility data and point-of-interests (POIs) data. We analyze large-scale urban and mobile data related to residential communities and find that in order to effectively identify vibrant communities, we should not just consider community "contents" such as buildings, facilities, and transportation, but also take into account the spatial structure. The spatial structure of a community refers to how the geographical items (POIs, road networks, public transits, etc.) of a com-munity are spatially arranged and interact with one an-other. Along this line, we first develop a geographical learning method to find proper representations of com-munities. In addition, we propose a novel geographic ensemble ranking strategy, which aggregates a variety of weak rankers to effectively spot vibrant communi-ties. Finally, we conduct a comprehensive evaluation with real-world residential community data. The ex-perimental results demonstrate the effectiveness of the proposed method.