Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.
PLoS One. 2012;7(5):e37027. doi: 10.1371/journal.pone.0037027. Epub 2012 May 29.
The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users. Surprisingly, while there are variations in human movement in different cities, our analysis shows that those are predominantly due to different distributions of places across different urban environments. Moreover, a universal law for human mobility is identified, which isolates as a key component the rank-distance, factoring in the number of places between origin and destination, rather than pure physical distance, as considered in some previous works. Building on our findings, we also show how a rank-based movement model accurately captures real human movements in different cities.
地理在线社交网络（如 Foursquare）的出现，用户自愿报告其当前位置，为人类移动性研究开辟了新的途径。特别是位置数据的精细粒度，GPS 精度达到 10 米，以及 Foursquare 在全球范围内的采用规模都是前所未有的。本文通过分析大量的 Foursquare 用户，研究了全球几个大都市的城市流动性模式。令人惊讶的是，尽管不同城市的人类移动性存在差异，但我们的分析表明，这些差异主要是由于不同城市环境中不同地点的分布不同造成的。此外，我们还确定了人类移动性的普适规律，该规律将排名距离作为一个关键组成部分，考虑了起点和终点之间的地点数量，而不是像之前的一些工作那样仅考虑物理距离。基于我们的发现，我们还展示了基于排名的移动模型如何在不同城市中准确地捕获真实的人类移动。
