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 pattems,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 cost-effective 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.
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