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

  • Yilan Cui
  • Xing Xie
  • Yi LiuEmail author
Research Article
  • 58 Downloads

Abstract

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 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.

Keywords

Social media Human mobility Population bias Sample reconstruction Data integration 

Notes

Acknowledgements

We would like to thank Fuzheng Zhang, Jianxun Lian, and Danyang Liu from Microsoft Research Asia for helping us crawl and manage Weibo check-in data.

Supplementary material

11783_2018_1068_MOESM1_ESM.pdf (576 kb)
Supporting materials

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Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of EnvironmentTsinghua UniversityBeijingChina
  2. 2.Microsoft Research AsiaMicrosoft CorporationBeijingChina

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