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