Cities worldwide are vulnerable to unpredictable extreme events such as disasters and public health crises. Urban big data and data-driven technologies have played an increasingly important role in building smart and resilient cities that can respond rapidly to these perturbations. However, many existing approaches had limited capabilities for processing big data, which has led to time-consuming and costly decision-making. Thus, we develop a real-time data-driven analytical and geo-visual system to enable smart and rapid responses to urban extreme events. The system is built on ArcGIS’s GeoEvent Server and Apache Spark and processes streaming data from social media with high speed, massive volume, and multiple modalities. The system employs online topic modeling and domain-adaptive sentiment analysis to track small-scale, undefined events, visualizes their spatial and semantic dynamics, and provides early alerts for crises and emergencies via an interactive online GIS platform. The proposed system has been applied during a large-scale hurricane and demonstrated effectiveness and agility in tracking and reporting emerging small-scale crises. The developed system can be applied in various urban scenarios to enable timely situation awareness and rapid response. This research contributes to the smart city safety and building rapidity of resilient cities.

世界各地的城市都容易遭受灾难和公共卫生危机等不可预测的极端事件的影响。城市大数据和数据驱动技术在建设能够快速响应这些扰动的智慧和弹性城市中发挥着越来越重要的作用。但是，许多现有方法处理大数据的能力有限，这导致了耗时且昂贵的决策。因此，我们开发了一个实时数据驱动的分析和地理视觉系统，以对城市极端事件做出明智而快速的反应。该系统基于ArcGIS的GeoEvent Server和Apache Spark构建，并以高速，大量和多种方式处理来自社交媒体的流数据。该系统采用在线主题建模和领域自适应情感分析功能来跟踪小规模的，未定义的事件，可视化其空间和语义动态，并通过交互式在线GIS平台为危机和紧急情况提供早期警报。拟议的系统已在大规模飓风中应用，在跟踪和报告新出现的小规模危机方面显示出了有效性和敏捷性。该开发的系统可以应用于各种城市场景，以实现及时的态势感知和快速响应。这项研究有助于智慧城市的安全和恢复型城市的快速建设。该开发的系统可以应用于各种城市场景，以实现及时的态势感知和快速响应。这项研究有助于智慧城市的安全和恢复型城市的快速建设。该开发的系统可以应用于各种城市场景，以实现及时的态势感知和快速响应。这项研究有助于智慧城市的安全和恢复型城市的快速建设。