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.

在这项研究中，我们演示了如何量化公共场所中大规模事件和交通（例如，人类活动）对环境的影响，并确定受影响城市的特定区域。我们开发了一个创新的数据融合框架，该框架融合了最新技术，可从社交媒体平台（Twitter）上提取污染事件并从公民提供的特定于城市的消息中检测事件，并检测事件。我们进一步为此跨域，多模式数据设计了一条融合管道，用于评估提取事件对城市内污染水平的时空影响。分析的结果具有极大的潜力，可以使公民，特别是城市当局受益，他们努力优化资源以更好地进行城市规划和交通管理。