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

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.

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

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Correspondence to Suparna De.

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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).

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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 (2021). https://doi.org/10.1007/s00607-021-00944-8

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Keywords

  • Air pollution
  • Multimodal data fusion
  • Social event-pollution correlation
  • Social computing
  • Urban computing

Mathematics Subject Classification

  • 62J10
  • 62H20
  • 91B76