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

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, propose ways of identifying personal activity-specific places commuting patterns in Beijing, China, based on Weibo (China’s Twitter) check-in records, well modifying sample data population synthesis technique. An independent citywide travel logistic survey is used the benchmark for validating results. Obvious differences are discerned from users’ respondents’ activity-mobility patterns, while there large variation representativeness between two sources. After similarity coefficient distance distributions observations increases substantially 23% 63%. Synthetic proves be satisfactory cost-effective alternative source information. The proposed framework can inform many applications related mobility, ranging transportation, through urban planning transport emission modeling.