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Identification Of Multifunctional Urban Activity Centers In Tokyo

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Identification of urban activity centers is among the most important components of the urban structure study, it is necessary for reasonable planning, regulation of traffic flows and other practical measures. The purpose of this paper is to design a complex method to identify urban activity centers based on different but universal data types. In this study, we used social media data (Twitter) since it guarantees regular updates and does not rely on administrative borders and points of interest database that was considered a 'hard' representation of multifunctional urban activities. A large amount of geotagged tweets was processed by means of statistical modelling (spatial autoregression) and combined with the distribution analysis of points of interest. This allowed to identify the local centers of urban activity within 23 special wards of Tokyo more objectively and precisely than when only based on the social media data. Thereafter, delimitated centers were classified in order to define and describe their main functional and spatial characteristics. As a result of the study, railway transport was identified as the main attraction factor of the urban activity; the modern urban structure of Tokyo was identified and mapped; a new comprehensive method for identification of urban activity centers was developed and five classes of urban activity centers were defined and described.

About the Authors

Vadim I. Boratinskii
Lomonosov Moscow State University
Russian Federation

Faculty of Geography

Leninskie gory, Moscow, GSP-1, 119991

Irina S. Tikhotskaya
Lomonosov Moscow State University
Russian Federation

Faculty of Geography

Leninskie gory, Moscow, GSP-1, 119991


1. Ahas R. et al. (2009). Modelling home and work locations of populations using passive mobile positioning data. In: Gartner and Rehrl (eds.), Location-Based Services and Tele-cartography II, Springer, Berlin, 301-315.

2. Anselin L. (1995). Local indicators of spatial association-LISA. Geogr. Anal. 27(2), 93-115.

3. Batty M. (2013). The New Science of Cities, MIT Press, Cambridge, MA.

4. Bingham-Hall J. & Tidey J. (2016). Visualizing social medias impact on local communities. Visual Communication, 15(3), 317-328, DOI: 10.1177/1470357216645710.

5. Birch C. et al. (2007). Rectangular and hexagonal grids used for observation, experiment, and simulation in ecology. Ecological Modelling, 206, 3-4, 347-359.

6. Cai J., Huang B., Song Y. (2016). Using multi-source geospatial big data to identify the structure of polycentric cities. Remote Sensing of Environment, 202, 210-221, DOI: 10.1016/j.rse.2017.06.039.

7. Campagna M. (2014) The Geographic Turn in Social Media: Opportunities for Spatial Planning and Geodesign. In: Murgante B. et al. (eds) Computational Science and Its Applications - ICCSA 2014. ICCSA. Lecture Notes in Computer Science, 8580. Springer, Cham/

8. Campagna M., Floris R., Massa P, Girsheva A., Ivanov K. (2015). The role of social media geographic information (SMGI) in spatial planning. In S. Geertman, J. Ferreira, Jr., R. Goodspeed et al. (Eds.), Planning support systems and smart cities: Lecture notes in geoinformation and cartography (41-60). Basel: Springer International Publishing Switzerland, DOI: 10.1007/978-3-319-18368-8_3.

9. Ciuccarelli P, Lupi G., Simeone L. (2014). Visualizing the data city: Social media as a source of knowledge for urban planning and management. Heidelberg: Springer, DOI: 10.1007/978-3-319-02195-9/

10. De Goei, B., Burger, M.J., Van Oort, F.G., Kitson, M. (2010). Functional Polycentrism and Urban Network Development in the Greater South East, United Kingdom: Evidence from Commuting Patterns, 1981-2001. Regional Studies, 44(9), 1149-1170, DOI: 10.1080/00343400903365102.

11. Demographia World Urban Areas (2018). 14th Annual Edition (PDF), archived from the original (PDF) on 3 May 2018. [Accessed 14 march 2019].

12. Evans-Cowley J.S., & Griffin G.P. (2011). Micro-participation: The role of microblogging in planning. Social Science Research Network, DOI: 10.2139/ssrn.1760522/

13. Frias-Martinez V, Soto V., Hohwald H., & Frias-Martinez E. (2012). Characterizing Urban Landscapes Using Geolocated Tweets. 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

14. Fujita M., Ogawa H. (1982). Multiple equilibria and structural transition of non-monocentric urban configurations. Regional Science and Urban Economics, 12(2), 161-196, DOI: 10.1016/0166-0462(82)90031-X.

15. Healey P. (1999). Institutionalist analysis, communicative planning, and shaping places. Journal of Planning Education and Research, 19, 111-121.

16. Heckman J. J. (1979). Sample selection bias as a specification error. The Econometric Society Stable, 47(1), 153-161.

17. JR Higashi Nihon Kaisha Yoran ( ) (2017)/ (JR East Company Directory for 2016-2017). URL: (24.03.2019) (in Japanese).

18. Kaplan A.M., Haenlein M. (2010). Users of the world, unite! The challenges and opportunities of social media, Bus. Horiz.

19. Kelejian H.H., & Prucha I.R. (1998). The Journal of Real Estate Finance and Economics, 17(1), 99-121.

20. Kotov E., et al. (2016). Moscow: a course for polycentricity: Summary of proceedings for the Moscow, Urban Forum Graduate School of Urbanism, Moscow (In Russian).

21. McMillen D.P. (2001). Nonparametric employment subcenter identification. Journal of Urban Economics, 50(3), 448-473.

22. McMillen D.P. (2004). Employment densities, spatial autocorrelation, and subcenters in large metropolitan areas. Journal of Regional Science, 44(2), 225-244, DOI: 10.1111/j.0022-4146.2004.00335.x.

23. McMillen D.P., McDonald J.F. (1997). A nonparametric analysis of employment density in a polycentric city. Journal of Regional Science, 37(4), 591-612, DOI: 10.1111/0022-4146.00071.

24. Openshaw S. (1984). The Modifiable Areal Unit Problem, GeoBooks, Norwich, U.K.

25. Openshaw S., Taylor PJ. (1981). The modifiable areal unit problem in Quantitative Geography. In N. Wringley and R. J. Bennett, (eds.), Routledge, London, U.K., 60-70.

26. OpenStreetMap (2017). Retrieved December 24, 2017. URL:

27. Pomorov S.B., Zhukovsky R.S. (2015). Retrospective of the development of urban polycentrism and theoretical ideas about it. Teoriya arhitektury (Architecture Theory), 52 (In Russian).

28. Riguelle F., Thomas I., Verhetsel A. (2007). Measuring urban polycentrism: a European case study and its implications. Journal of Economic Geography, 7(2), 193-215, DOI: 10.1093/jeg/lbl025.

29. Rosenbaum MS. (2006). Exploring the social supportive role of third places in consumers lives. Journal of Service Research, 9(1), 59-72, DOI: 10.1177/1094670506289530.

30. Schneider C.M., Belik V, Couronne T., Smoreda Z., and Gonzalez M.C. (2013). Unravelling daily human mobility motifs. Journal of the Royal Society Interface, 10(84), DOI: 10.1098/rsif.2013.0246.

31. Steiger E., Westerholt R., Resch B., & Zipf A. (2015). Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data. Computers, Environment and Urban Systems, 54, 255-265.

32. TechCrunch (2018). Twitter data reported in Dec 2018. Retrieved from.

33. Tokyo Metro official website (2019). URL: (23.03.2019).

34. (2017). Tokyo Railway Performance Report 2017. URL: (24.03.2019).

35. Vysokovsky A.A. (2005). Land use and development rules: design guide. The experience of introducing legal zoning in Kyrgyzstan, Ega-Basma, Bishkek (In Russian).


For citations:

Boratinskii V.I., Tikhotskaya I.S. Identification Of Multifunctional Urban Activity Centers In Tokyo. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2021;14(2):83-91.

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ISSN 2071-9388 (Print)
ISSN 2542-1565 (Online)