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

TL;DR

This study develops a three-step framework to analyze urban human mobility using social media data, specifically Weibo check-ins, and validates the approach with a citywide survey in Beijing.

Contribution

It introduces a novel bias modification method and validation process for social media-based mobility analysis, enhancing data reliability for urban planning applications.

Findings

Bias modification significantly improves data similarity to survey results.

Weibo data can serve as a cost-effective alternative for mobility analysis.

Distinct activity patterns are observed between social media users and survey respondents.

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 modelling.