Analysing Environmental Impact of Large-scale Events in Public Spaces with Cross- domain Multimodal Data Fusion

Suparna De, Wei Wang, Yuchao Zhou, Charith Perera, Klaus Moessner, Mansour Naser Alraja

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalComputing (Vienna/New York)
DOIs
Publication statusPublished - 12 Apr 2021

Keywords

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

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