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

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.

Graphical abstract

Keywords

Social media / Human mobility / Population bias / Sample reconstruction / Data integration

References

[1] Beckman R J, Baggerly K A, Mckay M D (1996). Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice, 30(6): 415–429

[2] Bojic I, Massaro E, Alexander B (2015). Choosing the right home location definition method for the given data set. In: International Conference on Social Informatics. Cham: Springer, 194–208

[3] Chen C, Ma J, Susilo Y, Liu Y, Wang M (2016). The promises of big data and small data for travel behavior (aka human mobility) analysis. Transportation Research Part C, Emerging Technologies, 68: 285–299

[4] Cho E, Myers S A, Leskovec J (2011). Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1082–1090

[5] Ghauche A(2010).Integrated transportation and energy activity-based model. Dissertation for the Master Degree. Cambridge: Massachusetts Institute of Technology

[6] Haase D, Lautenbach S, Seppelt R (2010). Modeling and simulating residential mobility in a shrinking city using an agent-based approach. Environmental Modelling & Software, 25(10): 1225–1240

[7] Hasan S, Ukkusuri S V (2014). Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44: 363–381

[8] Hasan S, Ukkusuri S V (2018). Reconstructing activity location sequences from incomplete check-in data: A semi-markov continuous-time Bayesian network model. IEEE Transactions on Intelligent Transportation Systems, 19(3): 687–698

[9] Hasan S, Zhan X, Ukkusuri S V (2013). Understanding urban human activity and mobility patterns using large-scale location-based data from online social media. In: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing. New York: ACM, 6

[10] Hendrix E M T, Ortigosa P M, García I (2002). On the efficiency and effectiveness of controlled random search. In: Stochastic and Global Optimization. Boston: Springer, 129–145

[11] Hermes K, Poulsen M (2012). A review of current methods to generate synthetic spatial microdata using reweighting and future directions. Computers, Environment and Urban Systems, 36(4): 281–290

[12] Hossain N, Hu T, White A M, Luo J, Kautz H (2016). Inferring fine-grained details on user activities and home location from social media: Detecting drinking-while-tweeting patterns in communities. arXiv preprint arXiv, 1603.03181

[13] Isaacman S, Becker R, Caceres R, Kobourov S, Martonosi M, Rowland J, Varshavsky A (2011). Identifying important places in people’s lives from cellular network data. In: International Conference on Pervasive Computing. Berlin: Springer, 133–151

[14] Kirk H (2013). Microsimulation Model User Guide (flexible modeling framework). Leeds: School of Geography, University of Leeds

[15] Lenormand M, Picornell M, Cantú-Ros O G, Tugores A, Louail T, Herranz R, Barthelemy M, Frías-Martínez E, Ramasco J J (2014). Cross-checking different sources of mobility information. PLoS One, 9(8): e105184

[16] Ma J, Heppenstall A, Harland K, Mitchell G (2014). Synthesising carbon emission for mega-cities: A static spatial microsimulation of transport CO₂ from urban travel in Beijing. Computers, Environment and Urban Systems, 45: 78–88

[17] Müller K, Axhausen K W (2010). Population synthesis for microsimulation: State of the art. Arbeitsberichte Verkehrs- und Raumplanung, 638doi: 10.3929/ethz-a-006127782

[18] Pontes T, Magno G, Vasconcelos M, Gupta A, Almeida J, Kumaraguru P, (2012). Beware of what you share: Inferring home location in social networks. In: 2012 IEEE 12th International Conference on Data Mining Workshops. Piscataway: IEEE, 571–578

[19] Rashidi T H, Abbasi A, Maghrebi M, Hasan S, Waller T S (2017). Exploring the capacity of social media data for modeling travel behavior: Opportunities and challenges. Transportation Research Part C: Emerging Technologies, 75: 197–211

[20] Rieser M, Dobler C, Dubernet T, Grether D, Horni A, Lammel G, Waraich, R, Zilske M, Axhausen K W, Nagel K(2014). MATSim User Guide.Zurich: MATSim

[21] Subbiah R, Lum K, Marathe A, Marathe M (2013). Activity based energy demand modeling for residential buildings. In: Innovative Smart Grid Technologies (ISGT), 2013 IEEE PES. Piscataway: IEEE, 1–6

[22] Toole J L, Colak S, Sturt B, Alexander L P, Evsukoff A, González M C (2015). The path most traveled: travel demand estimation using big data resources. Transportation Research Part C: Emerging Technologies, 58: 162–177

[23] Wang Y, Yuan N J, Lian D, Xu L, Xie X,Chen E, Rui Y (2015). Regularity and conformity: location prediction using heterogeneous mobility data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 1275–1284

[24] Wikipedia contributors (2017). Cosine similarity. Available online at accessed March 7, 2017)

[25] Wu L, Zhi Y, Sui Z, Liu Y (2014). Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS One, 9(5): e97010

[26] Yang F, Jin P J, Cheng Y, Zhang J, Ran B (2015). Origin-destination estimation for non-commuting trips using location-based social networking data. International Journal of Sustainable Transportation, 9(8): 551–564

[27] Yuan N J, Wang Y, Zhang F, Xie X, Sun G (2013). Reconstructing individual mobility from smart card transactions: a space alignment approach. In: ICDM. Piscataway: IEEE, 877–886

[28] Zhang F, Yuan N J, Xie X (2012). Data from ‘LifeSpec: Learning the spectrum of urban lifestyles’. Available online at accessed March 7, 2017)

[29] Zhang L, Yang W, Wang J, Rao Q (2013). Large-scale agent-based transport simulation in Shanghai, China. Transportation Research Record: Journal of the Transportation Research Board, 2399(1): 34–43

[30] Zhou J, Long Y (2014). Jobs-housing balance of bus commuters in Beijing. Transportation Research Record: Journal of the Transportation Research Board, 2418(1): 1–10

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