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

doi:10.1007/s11783-018-1068-1

Front. Environ. Sci. Eng.

2018, Vol. 12

Issue (5) : 7 https://doi.org/10.1007/s11783-018-1068-1

RESEARCH ARTICLE

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

Yilan Cui¹, Xing Xie², Yi Liu¹(

)

¹. School of Environment, Tsinghua University, Beijing 100084, China
². Microsoft Research Asia, Microsoft Corporation, Beijing 100080, China

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Abstract

Check-in and survey data are explored to identify personal activity-specific places.

Ways for detecting and moderating sample bias of Weibo check-in data is proposed.

A graphic representation of urban activity intensity in Beijing, China is presented.

The potential application of Weibo check-in data for urban analysis is introduced.

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

Corresponding Author(s): Yi Liu

Issue Date: 25 September 2018

Cite this article:

Yilan Cui,Xing Xie,Yi Liu. Social media and mobility landscape: Uncovering spatial patterns of urban human mobility with multi source data[J]. Front. Environ. Sci. Eng., 2018, 12(5): 7.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-018-1068-1
https://academic.hep.com.cn/fese/EN/Y2018/V12/I5/7

Fig.1 User profiles. (a) Education background, (b) Age. Here, education background is classified into three categories; including primary (junior school and below), secondary (senior high school), and tertiary (college and above).

Fig.2 Temporal pattern of home-based (a and c) and work-based activity (b and d) from ground truth (a and b) and survey data (c and d).

Fig.3 Flowchart for identification of home location and work place.

Tab.1 Constraint configuration for commuters/non-commuters

Fig.4 Validation of activity-specific locations identified from check-ins with independent travel logistic surveys. The base map shows the spatial layout of Beijing’s ring roads. Kernel density estimation is conducted to obtain a smooth distribution. (a₁ and a₂). The identified-home and recorded-home density maps for major metropolitan districts in Beijing. (b₁ and b₂) The identified and recorded workplace density maps of Beijing. (c₁ and c₂) The identified and recorded entertainment density maps of Beijing.

Fig.5 Performance of sample reconstruction. (a) Distance between Centers of Gravity index before and after sample reconstruction for activity category of Home (H), Work (W), Entertainment (E) and Other (O). The transparent bars with un-bold data labels indicate the check-in data’s baseline accuracy, expressed in terms of Weibo microdata-survey similarity. (b) Centers of Gravity of activity-specific spatial distribution maps. We refer to the check-in data as “microdata”, and reconstructed data as “syn”. Movements of centers are denoted by arrows. Here, synthetic center coincides with survey center for home distributions.

Fig.6 Commuting distance distributions. The direct and cumulative distribution curves of commuting distance. Here, “Survey” refers to survey data; “Microdata” refers to Weibo data after location identification; “Synthesis” refers to generated population after sample reconstruction. After sample reconstruction, the frequency of zero-distance commuting has been largely reduced. The synthetic distribution curve is more consistent with survey data though it slightly overestimates the frequency of commuting distance longer than 3 km.

Fig. A1 Temporal features of main activity category. (a) Home; (b) Travel; (c) Work; (d) School; (e) Entertainment; (f) Eating out; (g) Other

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