Modelling human mobility patterns using photographic data shared online.
Modelling human mobility patterns using photographic data shared online.
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DOI：
10.1098/rsos.150046
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发表时间：
2015-08
影响因子：
3.5
通讯作者：
Moat HS
中科院分区：
文献类型：
--
作者：
Barchiesi D;Preis T;Bishop S;Moat HS
人类天生就是移动的生物。我们在环境中移动的方式会对一系列问题产生影响，包括高效交通系统的设计和城市地区的规划。在这里，我们收集了大约16000个人的空间和时间位置的数据，这些人从英国境内的地点上传了带有地理标记的图像到Flickr照片共享网站。受Lévy flights理论的启发，该理论以前曾用于描述人类移动的统计特性，我们设计了一种机器学习算法来推断在地理位置中找到人的概率以及位置对之间移动的概率。我们的研究结果与英国的官方数据以及主要城市之间的旅行流量基本一致，这表明在线数据源可用于量化和模拟大规模的人类流动模式。
Humans are inherently mobile creatures. The way we move around our environment has consequences for a wide range of problems, including the design of efficient transportation systems and the planning of urban areas. Here, we gather data about the position in space and time of about 16 000 individuals who uploaded geo-tagged images from locations within the UK to the Flickr photo-sharing website. Inspired by the theory of Lévy flights, which has previously been used to describe the statistical properties of human mobility, we design a machine learning algorithm to infer the probability of finding people in geographical locations and the probability of movement between pairs of locations. Our findings are in general agreement with official figures in the UK and on travel flows between pairs of major cities, suggesting that online data sources may be used to quantify and model large-scale human mobility patterns.