Skip to main content
SpringerLink
Log in

A Scalable Spatio-Temporal Analytics Framework for Urban Networks

  • Conference paper
  • First Online:
Networks in the Global World VI (NetGloW 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 663))

Included in the following conference series:

Abstract

Numerous real-world processes and events, especially in the urban domain, are represented with spatio-temporal transactional data (STTD): examples include human and vehicle mobility, communication, and economic transactions. Despite the overwhelming variability of such data sets they can be seen as having very much in common, including their structure as well as the analytic and visualization challenges they face. The present paper describes an idea of an analytic platform implementing an underlying general data model as well as the analytic and modeling tools for STTD. The platform is intended to provide increased scalability of the STTD-driven applications enabling broad reuse of the most common analytic and visualization solutions in multiple contexts of urban analytics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Apache Parquet. https://parquet.apache.org/. Accessed 31 May 2022

  2. Longitudinal employer-household dynamics. https://lehd.ces.census.gov/data/. Accessed 31 May 2022

  3. NYC taxi zones. https://data.cityofnewyork.us/Transportation/NYC-Taxi-Zones/d3c5-ddgc. Accessed 29 May 2022

  4. STTN implementation. https://github.com/yuribogomolov/sttn. Accessed 31 May 2022

  5. What is Apache Parquet. https://databricks.com/glossary/what-is-parquet. Accessed 31 May 2022

  6. Amini, A., Kung, K., Kang, C., Sobolevsky, S., Ratti, C.: The impact of social segregation on human mobility in developing and industrialized regions. EPJ Data Sci. 3(1), 1–20 (2014). https://doi.org/10.1140/epjds31

    Article  Google Scholar 

  7. Andrienko, N., Andrienko, G.: A visual analytics framework for spatio-temporal analysis and modelling. Data Mining Knowl. Disc. 27(1), 55–83 (2013)

    Article  MathSciNet  Google Scholar 

  8. Belyi, A., et al.: Global multi-layer network of human mobility. Int. J. Geographical Inf. Sci. 31(7), 1381–1402 (2017)

    Article  Google Scholar 

  9. Bogomolov, Y., He, M., Khulbe, D., Sobolevsky, S.: Impact of income on urban commute across major cities in US. Procedia Comput. Sci. 193, 325–332 (2021)

    Article  Google Scholar 

  10. Cao, G., Wang, S., Hwang, M., Padmanabhan, A., Zhang, Z., Soltani, K.: A scalable framework for spatiotemporal analysis of location-based social media data. Comput. Environ. Urban Syst. 51, 70–82 (2015)

    Article  Google Scholar 

  11. Compieta, P., Di Martino, S., Bertolotto, M., Ferrucci, F., Kechadi, T.: Exploratory spatio-temporal data mining and visualization. J. Vis. Languages Comput. 18(3), 255–279 (2007)

    Article  Google Scholar 

  12. Cressie, N., Wikle, C.K.: Statistics for Spatio-Temporal Data. Wiley, Hoboken (2015)

    MATH  Google Scholar 

  13. Diggle, P.J.: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns. CRC Press, New York (2013)

    Book  MATH  Google Scholar 

  14. Ferreira, N., Poco, J., Vo, H.T., Freire, J., Silva, C.T.: Visual exploration of big spatio-temporal urban data: a study of New York City taxi trips. IEEE Trans. Visual Comput. Graphics 19(12), 2149–2158 (2013)

    Article  Google Scholar 

  15. Flaming, D., et al.: Los Angeles rising: a city that works for everyone (2015)

    Google Scholar 

  16. Gao, S.: Spatio-temporal analytics for exploring human mobility patterns and urban dynamics in the mobile age. Spatial Cogn. Comput. 15(2), 86–114 (2015)

    Article  Google Scholar 

  17. Grauwin, S., et al.: Identifying and modeling the structural discontinuities of human interactions. Sci. Rep. 7(1), 1–11 (2017)

    Article  Google Scholar 

  18. Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., Ratti, C.: Geo-located Twitter as proxy for global mobility patterns. Cartogr. Geogr. Inf. Sci. 41(3), 260–271 (2014)

    Article  Google Scholar 

  19. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  20. Kung, K.S., Greco, K., Sobolevsky, S., Ratti, C.: Exploring universal patterns in human home-work commuting from mobile phone data. PLoS ONE 9(6), e96,180 (2014)

    Google Scholar 

  21. Kurkcu, A., Ozbay, K., Morgul, E.: Evaluating the usability of geo-located Twitter as a tool for human activity and mobility patterns: a case study for NYC. In: Transportation Research Board’s 95th Annual Meeting, pp. 1–20 (2016)

    Google Scholar 

  22. Paldino, S., Bojic, I., Sobolevsky, S., Ratti, C., González, M.C.: Urban magnetism through the lens of geo-tagged photography. EPJ Data Sci. 4(1), 1–17 (2015). https://doi.org/10.1140/epjds/s13688-015-0043-3

    Article  Google Scholar 

  23. Pei, T., Sobolevsky, S., Ratti, C., Shaw, S.L., Li, T., Zhou, C.: A new insight into land use classification based on aggregated mobile phone data. Int. J. Geogr. Inf. Sci. 28(9), 1988–2007 (2014)

    Article  Google Scholar 

  24. Qian, C., et al.: Geo-tagged social media data as a proxy for urban mobility. In: Hoffman, M. (ed.) AHFE 2017. AISC, vol. 610, pp. 29–40. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60747-4_4

    Chapter  Google Scholar 

  25. Ratti, C., Claudel, M.: Live Singapore! The urban data collider. Transfers 4(3), 117–121 (2014)

    Article  Google Scholar 

  26. Ratti, C., et al.: Redrawing the map of Great Britain from a network of human interactions. PLoS ONE 5(12), e14,248 (2010)

    Google Scholar 

  27. Roddick, J.F., Spiliopoulou, M.: A bibliography of temporal, spatial and spatio-temporal data mining research. ACM SIGKDD Explorations Newsl 1(1), 34–38 (1999)

    Article  MATH  Google Scholar 

  28. Santi, P., Resta, G., Szell, M., Sobolevsky, S., Strogatz, S.H., Ratti, C.: Quantifying the benefits of vehicle pooling with shareability networks. Proc. Natl. Acad. Sci. 111(37), 13290–13294 (2014)

    Article  Google Scholar 

  29. Senn, O., Khairul, M., Maitan, M., Pribadi, R., Shah, M., Sivaprakasam, R.: Datacollider: an interface for exploring large spatio-temporal data sets. In: SIGGRAPH Asia 2015 Visualization in High Performance Computing, pp. 1–4 (2015)

    Google Scholar 

  30. Sobolevsky, S.: Hierarchical graph neural networks. arXiv preprint arXiv:2105.03388 (2021)

  31. Sobolevsky, S., Sitko, I., Tachet des Combes, R., Hawelka, B., Murillo Arias, J., Ratti, C.: Cities through the prism of people’s spending behavior. PLoS ONE 11(2), e0146,291 (2016)

    Google Scholar 

  32. Sobolevsky, S., Szell, M., Campari, R., Couronné, T., Smoreda, Z., Ratti, C.: Delineating geographical regions with networks of human interactions in an extensive set of countries. PLoS ONE 8(12), e81,707 (2013)

    Google Scholar 

  33. Van de Weghe, N., De Roo, B., Qiang, Y., Versichele, M., Neutens, T., De Maeyer, P.: The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis. Int. J. Geogr. Inf. Sci. 28(5), 1047–1060 (2014)

    Article  Google Scholar 

  34. Yoshimura, Y., et al.: An analysis of visitors’ behavior in the Louvre museum: a study using Bluetooth data. Environ. Plann. B. Plann. Des. 41(6), 1113–1131 (2014)

    Article  Google Scholar 

  35. Zhu, E., Khan, M., Kats, P., Bamne, S.S., Sobolevsky, S.: Digital urban sensing: a multi-layered approach. arXiv preprint arXiv:1809.01280 (2018)

  36. Zipf, G.K.: The p 1 p 2/d hypothesis: on the intercity movement of persons. Am. Sociol. Rev. 11(6), 677–686 (1946)

    Article  Google Scholar 

Download references

Acknowledgements

This research was partially supported by the Masaryk University Award in Science and Humanities and the RSF grant 21-77-10098 “Spatial segregation of the largest post-Soviet cities in the Russian Federation: analysis of the geography of personal activity of residents based on big data”.

Author information

Authors and Affiliations

  1. Masaryk University, Brno, Czech Republic

    Yuri Bogomolov & Stanislav Sobolevsky

  2. New York University, New York, USA

    Stanislav Sobolevsky

  3. ITMO University, St. Petersburg, Russia

    Yuri Bogomolov & Stanislav Sobolevsky

Authors
  1. Yuri Bogomolov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stanislav Sobolevsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Stanislav Sobolevsky .

Editor information

Editors and Affiliations

  1. Centre for German and European Studies, Bielefeld University, Bielefeld, Germany

    Artem Antonyuk

  2. Department of Social Statistics, School of Social Sciences, University of Manchester, Manchester, UK

    Nikita Basov

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bogomolov, Y., Sobolevsky, S. (2023). A Scalable Spatio-Temporal Analytics Framework for Urban Networks. In: Antonyuk, A., Basov, N. (eds) Networks in the Global World VI. NetGloW 2022. Lecture Notes in Networks and Systems, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-29408-2_5

Download citation

Publish with us

Policies and ethics

Search

Navigation