Towards Big Data-Driven Human Mobility Patterns and Models

大数据时代的到来使得基于个体粒度的海量时空轨迹获取人类移动模式成为可能.来自不同领域的学者基于手机通话数据、公交卡刷卡记录、社交网站签到数据、出租车轨迹、银行刷卡记录等进行了人类移动模式的研究,这些研究丰富了地理信息系统的时空分析方法,为从个体角度审视人与地理环境之间的交互关系提供了一个新视角,并可以应用于交通、公共卫生等领域.总结了基于大数据的人类移动模式研究流程,归纳了人类移动模式的基本度量方法,探讨了解释所观测移动模式的模型构建方法,指出了地理环境对于移动模型建立的影响.In the big data era, massive volumes of individual-level movements, extracted from various geospatial data, including mobile phone data, public transportation card records, social media check-in data, taxi trajectories, and bank card records, are available for scholars in different fields to study human mobility patterns. These studies enrich spatio-temporal analysis methods in GIS and provide a new perspective to human-environment interactions. Observed human mobility patterns and models can be applied to many applications such as transportation and public health.This paper presents a generic workflow for big-data-driven human mobility analyses and summaries major movement measures. By comparing a number of models used to interpret and reproduce the observed pattern, this paper emphasizes the geographical impact on human mobility patterns.EI中文核心期刊要目总览(PKU)中国科技核心期刊(ISTIC)中国科学引文数据库(CSCD)06660-6663