Social media and mobility landscape:Uncovering spatial patterns of urban human mobility with multi source data

查看全文 作　　者：Yilan [1]Cui;Xing [2]Xie;Yi [1]Liu

机构地区：[1]School of Environment,Tsinghua University,Beijing 100084,China;[2]Microsoft Research Asia,Microsoft Corporation,Beijing 100080,China

出　　处：《Frontiers of Environmental Science & Engineering》索引2018年第12卷第5期,共14页

摘　　要：In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media data.More specifically,we propose ways of identifying personal activity-specific places and commuting patterns in Beijing,China,based on Weibo(China’s Twitter)check-in records,as well as modifying sample bias of check-in data with population synthesis technique.An independent citywide travel logistic survey is used as the benchmark for validating the results.Obvious differences are discerned from Weibo users’and survey respondents’activity-mobility patterns,while there is a large variation of population representativeness between data from the two sources.After bias modification,the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%.Synthetic data proves to be a satisfactory costeffective alternative source of mobility information.The proposed framework can inform many applications related to human mobility,ranging from transportation,through urban planning to transport emission modeling. 关键词：Social media Human mobility Population bias Sample reconstruction Data integration

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Social media and mobility landscape:Uncovering spatial patterns of urban human mobility with multi source data

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