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Changing Pattern of Human Movements in Istanbul During Covid-19

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

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Abstract

Human trajectories provide spatial information on how citizens interact with the city. This information can explain the daily routine, economic and cultural cycle of citizens. In the last year, the pattern of human trajectories is expected to change over cities due to Covid-19. This paper investigates the change in human trajectories based on the social media data (SMD) before and after the Covid-19. This study aims to find out the differences between the years 2018, 2020, and 2021. Firstly, all accounts for each year are classified based on movement behaviors. Secondly, spatial distributions of the tweets in terms of classified accounts are visualized after the hierarchical clustering applied to each dataset. Lastly, the average step lengths (ASL) are calculated for each account and classified in terms of step length levels as no movement, neighborhood, district, inter-districts, inter periphery, and center, outbound. The number of tweets and distinct accounts decreased by 90% and 84% from 2018 to 2021. The decrease in the number of single tweeting accounts is 84%, it is 60% in stationary accounts, and 94% in moving ac-counts. The size of the spatial clusters also decreased for all types of accounts maps, however, some of the previously visited spatial points are disappeared while new ones appeared on maps of single tweeting and moving accounts. The ASL of moving accounts also confirms the human movement decrease. According to that, the max, mean, and median ASL decreased 22%, 13%, and 35%. Results point out outcomes vary in terms of accounts’ movement behaviors. This study is expected to contribute the measuring the pandemic impacts on human movement with SMD.

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References

  1. Ahas, R., et al.: Everyday space–time geographies: using mobile phone-based sensor data to monitor urban activity in Harbin, Paris, and Tallinn. Int. J. Geogr. Inf. Sci. 29(11), 2017–2039 (2015)

    Google Scholar 

  2. Huang, Q., Wong, D.W.: Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? Int. J. Geogr. Inf. Sci. 30(9), 1873–1898 (2016)

    Article  Google Scholar 

  3. Noulas, A., Scellato, S., Lambiotte, R., Pontil, M., Mascolo, C.: A tale of many cities: universal patterns in human urban mobility. PloS ONE 7(5), e37027 (2012)

    Google Scholar 

  4. Buchin, K., Löffler, M., Popov, A., Roeloffzen, M.: Fréchet distance between uncertain trajectories: computing expected value and upper bound. In: 36th European Workshop on Computational Geometry (EuroCG 2020). Würzburg, Germany (2020)

    Google Scholar 

  5. Dodge, S., Gao, S., Tomko, M., Weibel, R.: Progress in computational movement analysis–towards movement data science (2020)

    Google Scholar 

  6. Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 1–41 (2015)

    Article  Google Scholar 

  7. Buchin, K.: Trajectory Similarity. Winter School on Computational Geometry AUT, 11th Winter School on Computational Geometry. https://www.youtube.com/watch?v=GqOIzUEZgsA&list=LL&index=17. Accessed 03 May 2021

  8. Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., Newth, D.: Understanding human mobility from Twitter. PloS ONE 10(7), e0131469 (2015)

    Google Scholar 

  9. Demšar, U., Long, J.A., Siła-Nowicka, K.: Integrated science of movement. J. Spat. Inf. Sci. 2020(21), 25–31 (2020)

    Google Scholar 

  10. Minghini, M., Coetzee, S., Grinberger, A.Y., Yeboah, G., Juhász, L., Mooney, P.: OpenStreetMap research in the COVID-19 era. Editors 1 (2020)

    Google Scholar 

  11. Ma, D., Osaragi, T., Oki, T., Jiang, B.: Exploring the heterogeneity of human urban movements using geo-tagged tweets. Int. J. Geogr. Inf. Sci. 34(12), 2475–2496 (2020)

    Article  Google Scholar 

  12. Xin, Y., MacEachren, A.M.: Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. Int. J. Geogr. Inf. Sci. 34(12), 2497–2516 (2020)

    Article  Google Scholar 

  13. Gabrielli, L., Rinzivillo, S., Ronzano, F., Villatoro, D.: From tweets to semantic trajectories: mining anomalous urban mobility patterns. In: Nin, J., Villatoro, D. (eds.) CitiSens 2013. LNCS, vol. 8313, pp. 26–35. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-04178-0_3

    Chapter  Google Scholar 

  14. McKenzie, G., Janowicz, K., Gao, S., Gong, L.: How where is when? On the regional variability and resolution of geosocial temporal signatures for points of interest. Comput. Environ. Urban Syst. 54, 336–346 (2015)

    Article  Google Scholar 

  15. Kumari, A., Behera, R.K., Shukla, A.S., Sahoo, S.P., Misra, S., Rath, S.K.: Quantifying influential communities in granular social networks using fuzzy theory. In: Gervasi, O., et al. (eds.) ICCSA 2020. LNCS, vol. 12252, pp. 906–917. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58811-3_64

    Chapter  Google Scholar 

  16. Behera, R.K., Jena, M., Rath, S.K., Misra, S.: Co-LSTM: convolutional LSTM model for sentiment analysis in social big data. Inf. Process. Manag. 58(1), (2021)

    Google Scholar 

  17. Slös Wogu, I.A.P., Njie, S.N.N., Katende, J.O., Ukagba, G.U., Edogiawerie, M.O., Misra, S.: The social media, politics of disinformation in established hegemonies, and the role of technological innovations in 21st century elections: the road map to us 2020 presidential elections. Int. J. Electron. Gov. Res. (IJEGR) 16(3), 65–84 (2020)

    Article  Google Scholar 

  18. Hijmans, R.J.: Introduction to the “geosphere” package (Version 1.5-10) (2019)

    Google Scholar 

  19. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2020)

    Google Scholar 

  20. Bivand, V.R., Rundel, C.: rgeos: Interface to Geometry Engine - Open Source (‘GEOS’). R package version (2020)

    Google Scholar 

  21. Tennekes, M.: tmap: Thematic maps in R. J. Stat. Softw. 84(6), 1–39 (2018)

    Article  Google Scholar 

  22. Michelot, T., Langrock, R., Patterson, T.A.: moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 7(11), 1308–1315 (2016)

    Article  Google Scholar 

  23. Snow, J.: On the Mode of Communication of Cholera. John Churchill (1855)

    Google Scholar 

  24. Gulnerman, A.G., Karaman, H., Pekaslan, D., Bilgi, S.: Citizens’ spatial footprint on Twitter—anomaly, trend and bias investigation in Istanbul. ISPRS Int. J. Geo Inf. 9(4), 222 (2020)

    Article  Google Scholar 

  25. Gulnerman, A.G., Karaman, H.: Spatial reliability assessment of social media mining techniques with regard to disaster domain-based filtering. ISPRS Int. J. Geo Inf. 9(4), 245 (2020)

    Article  Google Scholar 

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Correspondence to Ayse Giz Gulnerman .

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Gulnerman, A.G. (2021). Changing Pattern of Human Movements in Istanbul During Covid-19. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12957. Springer, Cham. https://doi.org/10.1007/978-3-030-87013-3_17

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  • DOI: https://doi.org/10.1007/978-3-030-87013-3_17

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