Skip to main content

Advertisement

SpringerLink
Log in

Analysing environmental impact of large-scale events in public spaces with cross-domain multimodal data fusion

  • Special Issue Article
  • Published:
Computing Aims and scope Submit manuscript

Abstract

In this study, we demonstrate how we can quantify environmental implications of large-scale events and traffic (e.g., human movement) in public spaces, and identify specific regions of a city that are impacted. We develop an innovative data fusion framework that synthesises the state-of-the-art techniques in extracting pollution episodes and detecting events from citizen-contributed, city-specific messages on social media platforms (Twitter). We further design a fusion pipeline for this cross-domain, multimodal data, which assesses the spatio-temporal impact of the extracted events on pollution levels within a city. Results of the analytics have great potential to benefit citizens and in particular, city authorities, who strive to optimise resources for better urban planning and traffic management.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://hub.docker.com/r/ikaas/anomaly-detection.

References

  1. World population prospects: the 2017 revision (2017). https://www.un.org/development/desa/publications/world-population-prospects-the-2017-revision.html

  2. World urbanization prospects: key facts (the 2018 revision) (2018). https://population.un.org/wup/Publications/Files/WUP2018-KeyFacts.pdf

  3. Alberti M (2017) Grand challenges in urban science. Front Built Environ 3:6

    Article  Google Scholar 

  4. Anantharam P, Barnaghi P, Thirunarayan K, Sheth A (2015) Extracting city traffic events from social streams. ACM Trans Intell Syst Technol 6(4):1–27

    Article  Google Scholar 

  5. Balduini M, Valle ED, Dell’Aglio D, Tsytsarau M, Palpanas T, Confalonieri C (2013) Social listening of city scale events using the streaming linked data framework. In: Advanced Information Systems Engineering, pp. 1–16. Springer Berlin Heidelberg

  6. Bardoutsos A, Filios G, Katsidimas I, Krousarlis T, Nikoletseas S, Tzamalis P (2020) A multidimensional human-centric framework for environmental intelligence: air pollution and noise in smart cities. In: 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), pp. 155–164

  7. Basu M, Shandilya A, Khosla P, Ghosh K, Ghosh S (2019) Extracting resource needs and availabilities from microblogs for aiding post-disaster relief operations. IEEE Trans Comput Soc Syst 6(3):604–618

    Article  Google Scholar 

  8. Bermudez-Edo M, Barnaghi P, Moessner K (2018) Analysing real world data streams with spatio-temporal correlations: Entropy vs. pearson correlation. Autom Constr 88:87–100

    Article  Google Scholar 

  9. Broadbent J (2017) Comparing online and blended learner’s self-regulated learning strategies and academic performance. Int High Educ 33:24–32

    Article  Google Scholar 

  10. Chen Q, Wang W, Huang K, De S, Coenen F (2020) Adversarial domain adaptation for crisis data classification on social media. In: 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)

  11. Chen Q, Wang W, Huang K, De S, Coenen F (2020) Multi-modal adversarial training for crisis-related data classification on social media. In: 2020 IEEE International Conference on Smart Computing (SMARTCOMP)

  12. De S, Christophe B, Moessner K (2014) Semantic enablers for dynamic digitalphysical object associations in a federated node architecture for the internet of things. Ad Hoc Netw 18:102–120

    Article  Google Scholar 

  13. De S, Jassat U, Wang W, Perera C and Moessner K (2021) Inferring latent patterns in air quality from urban big data. IEEE Internet Things Mag 4(1):20–27. https://doi.org/10.1109/IOTM.0011.20000

  14. Ding Y, Li Y, Deng K, Tan H, Yuan M, Ni LM (2017) Detecting and analyzing urban regions with high impact of weather change on transport. IEEE Trans Big Data 3(2):126–139

    Article  Google Scholar 

  15. Emmert-Streib F (2010) Statistic complexity: combining kolmogorov complexity with an ensemble approach. PLoS ONE 5(8):e12256

    Article  Google Scholar 

  16. Foundation, T.A.S.: Opennlp (2017). https://opennlp.apache.org

  17. Ge L, Zhou A, Li H, Liu J (2019) Deep spatial-temporal fusion network for fine-grained air quality prediction. In: 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 536–543

  18. Google: Google maps geocoding api (2020). https://developers.google.com/maps/documentation/geocoding/intro

  19. Hu K, Rahman A, Bhrugubanda H, Sivaraman V (2017) HazeEst: Machine learning based metropolitan air pollution estimation from fixed and mobile sensors. IEEE Sens J 17(11):3517–3525

    Article  Google Scholar 

  20. Hu K, Sivaraman V, Bhrugubanda H, Kang S, Rahman A (2016) SVR based dense air pollution estimation model using static and wireless sensor network. In: 2016 IEEE SENSORS. IEEE

  21. Hu T, Bigelow E, Luo J, Kautz H (2017) Tales of two cities: using social media to understand idiosyncratic lifestyles in distinctive metropolitan areas. IEEE Trans Big Data 3(1):55–66

    Article  Google Scholar 

  22. Jara AJ, Genoud D, Bocchi Y (2014) Big data for smart cities with KNIME a real experience in the SmartSantander testbed. Softw Pract Exp 45(8):1145–1160

    Article  Google Scholar 

  23. Jiang S, Ferreira J, Gonzalez MC (2017) Activity-based human mobility patterns inferred from mobile phone data: a case study of singapore. IEEE Trans Big Data 3(2):208–219

    Article  Google Scholar 

  24. Kim M (2012) Anomaly detection. http://uk.mathworks.com/matlabcentral/fileexchange/39593-anomaly-detection/content/kse_test_matlab/kse_test.m

  25. Komninos A, Stefanis V, Plessas A, Besharat J (2013) Capturing urban dynamics with scarce check-in data. IEEE Pervasive Comput 12(4):20–28

    Article  Google Scholar 

  26. LondonAir: London air quality network (laqn) (2020). https://www.londonair.org.uk/LondonAir/Default.aspx

  27. Lu X, Ota K, Dong M, Yu C, Jin H (2017) Predicting transportation carbon emission with urban big data. IEEE Trans Sustain Comput 2(4):333–344

    Article  Google Scholar 

  28. Luo X, Yuan Y, Li Z, Zhu M, Xu Y, Chang L, Sun X, Ding Z (2019) FBVA: a flow-based visual analytics approach for citywide crowd mobility. IEEE Trans Comput Soc Syst 6(2):277–288

    Article  Google Scholar 

  29. Machado KLS, Boukerche A, Cerqueira EC, Loureiro A (2019) A data-centric approach for social and spatiotemporal sensing in smart cities. IEEE Internet Comput 23(1):9–18

    Article  Google Scholar 

  30. Marakkalage SH, Sarica S, Lau BPL, Viswanath SK, Balasubramaniam T, Yuen C, Yuen B, Luo J, Nayak R (2019) Understanding the lifestyle of older population: mobile crowdsensing approach. IEEE Trans Comput Soc Syst 6(1):82–95

    Article  Google Scholar 

  31. Miles J, Shevlin M (2000) Applying Regression and Correlation. SAGE Publications Inc. https://www.ebook.de/de/product/3768816/jeremy_miles_mark_shevlin_applying_regression_and_correlation.html

  32. Myers JL, Well AD, Robert F, Lorch J (2010) Research Design and Statistical Analysis. Taylor & Francis Inc

  33. Noulas A, Mascolo C, Frias-Martinez E (2013) Exploiting foursquare and cellular data to infer user activity in urban environments. In: 2013 IEEE 14th International Conference on Mobile Data Management, vol. 1, pp. 167–176

  34. Pan B, Zheng Y, Wilkie D, Shahabi C (2013) Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL’13

  35. Ritter A, Mausam Etzioni O, Clark S (2012) Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD’12

  36. Wang Q, Dai HN, Wang H (2017) A smart MCDM framework to evaluate the impact of air pollution on city sustainability: a case study from china. Sustainability 9(6):911

    Article  Google Scholar 

  37. Whyte W (2001) The social life of small urban spaces. Project for Public Spaces, New York

    Google Scholar 

  38. Zhang X, He M, Shao B, Ren C (2016) Physical-social fusion to assist public services in the war against air pollution in china. In: 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), pp. 916–920

  39. Zhao WX, Jiang J, Weng J, He J, Lim EP, Yan H, Li X (2011) Comparing twitter and traditional media using topic models. In: Lecture Notes in Computer Science, pp. 338–349. Springer Berlin Heidelberg

  40. Zheng Y, Zhang H, Yu Y (2015) Detecting collective anomalies from multiple spatio-temporal datasets across different domains. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS’15

  41. Zhou Y, De S, Ewa G, Perera C, Moessner K (2018) Data-driven air quality characterization for urban environments: a case study. IEEE Access 6:77996–78006

    Article  Google Scholar 

  42. Zhou Y, De S, Moessner K (2016) Real world city event extraction from twitter data streams. Procedia Comput Sci 98:443–448

    Article  Google Scholar 

  43. Zhou Y, De S, Wang W, Moessner K (2014) Enabling query of frequently updated data from mobile sensing sources. In: 2014 IEEE 17th International Conference on Computational Science and Engineering

  44. Zhou Y, De S, Wang W, Wang R, Moessner K (2018) Missing data estimation in mobile sensing environments. IEEE Access 6:69869–69882

    Article  Google Scholar 

  45. Zhu JY, Sun C, Li VOK (2017) An extended spatio-temporal granger causality model for air quality estimation with heterogeneous urban big data. IEEE Trans Big Data 3(3):307–319

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Departments of Digital Technologies, University of Winchester, Winchester, UK

    Suparna De

  2. School of Advanced Technology, Xian Jiaotong Liverpool University, Suzhou, 215123, China

    Wei Wang

  3. Institute for Communication Systems (ICS), University of Surrey, Guildford, GU2 7XH, UK

    Yuchao Zhou

  4. School of Computer Science and Informatics, Cardiff University, Cardiff, UK

    Charith Perera

  5. Faculty of Electrical Engineering and Information Technology, Chemnitz University of Technology, Chemnitz, Germany

    Klaus Moessner

  6. Department of Management Information Systems, College of Commerce and Business Administration, Dhofar University, Salalah, Oman

    Mansour Naser Alraja

Authors
  1. Suparna De
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuchao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Charith Perera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Klaus Moessner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mansour Naser Alraja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Suparna De.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the European Commission, Horizon 2020 Programme, TagItSmart! Project, under Contract 688061. M. N. Alraja’s work was supported by The Research Council (TRC), Sultanate of Oman (Block Fund-Research Grant).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

De, S., Wang, W., Zhou, Y. et al. Analysing environmental impact of large-scale events in public spaces with cross-domain multimodal data fusion. Computing 103, 1959–1981 (2021). https://doi.org/10.1007/s00607-021-00944-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00607-021-00944-8

Keywords

Mathematics Subject Classification

Search

Navigation