Social-Urban Neighborhood Search Based on Crowd Footprints Network

Neighborhood is generally a geographically localized community often with face-to-face social interactions. However, modern cities and the widespread social networks have been drastically changing the concept of neighborhood, much beyond spatial constraint. Specifically, due to the complicated urban structures with entangled transportation network and the resulting spatio-temporally extended crowd activities, it is a non-trivial task to examine neighborhood areas from a location of interest. As a promising approach to investigate such a social-urban structure, we propose a social-urban neighborhood search which aims at identifying neighborhood areas from a specific location particularly considering social interactions between urban areas. We especially examine crowd movings through location-based social networks as an important indicator for measuring social interactions. We also introduce a data structure for aggregation of crowd movings as a simplified graph, with which we can easily analyze crowd movements in a large scale urban area. In the experiment, we will look into neighborhoods for several urban areas of our interests in terms of social interactions significantly focusing on how they are distorted from general localized vicinity.