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

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.

Automated summary

This study demonstrates the quantification of environmental implications of large-scale events and traffic in public spaces, utilizing social media platforms to extract pollution episodes and detect events. An innovative data fusion framework is developed to assess the impact of these events on pollution levels, providing potential benefits for city authorities in optimizing urban planning and traffic management.