Alessandretti Laura, Sapiezynski Piotr, Lehmann Sune, Baronchelli Andrea
City, University of London, London EC1V 0HB, United Kingdom.
Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.
PLoS One. 2017 Feb 15;12(2):e0171686. doi: 10.1371/journal.pone.0171686. eCollection 2017.
The recent availability of digital traces generated by phone calls and online logins has significantly increased the scientific understanding of human mobility. Until now, however, limited data resolution and coverage have hindered a coherent description of human displacements across different spatial and temporal scales. Here, we characterise mobility behaviour across several orders of magnitude by analysing ∼850 individuals' digital traces sampled every ∼16 seconds for 25 months with ∼10 meters spatial resolution. We show that the distributions of distances and waiting times between consecutive locations are best described by log-normal and gamma distributions, respectively, and that natural time-scales emerge from the regularity of human mobility. We point out that log-normal distributions also characterise the patterns of discovery of new places, implying that they are not a simple consequence of the routine of modern life.
近期，由通话记录和在线登录产生的数字踪迹数据的可得性显著增进了科学界对人类移动性的理解。然而，迄今为止，有限的数据分辨率和覆盖范围阻碍了对不同时空尺度下人类位移的连贯描述。在此，我们通过分析约850个人的数字踪迹来刻画跨越多个数量级的移动行为，这些踪迹以约10米的空间分辨率，每16秒采样一次，持续25个月。我们表明，连续位置之间的距离和等待时间分布分别最适合用对数正态分布和伽马分布来描述，并且自然时间尺度源自人类移动的规律性。我们指出，对数正态分布也刻画了新地点的发现模式，这意味着它们并非现代生活日常规律的简单结果。
