ABSTRACT Natural disasters such as flooding, wildfires, and mudslides are rare events, but they affect citizens at unpredictable times and the impact on human life can be significant. Citizens located close to events can provide detailed, real-time data streams capturing their event response. Instead of visualizing individual updates, an integrated spatiotemporal map yields ‘big picture’ event information. We investigate the question of whether information from affected citizens is sufficient to generate a map of an unfolding natural disaster. We built the Citizen Disaster Reaction Multi-Agent Simulation (CDR-MAS), a multi-agent system that simulates the reaction of citizens to a natural disaster in an urban region. We proposed an rkNN classification algorithm to aggregate the update streams into a series of colored Voronoi event maps. We simulated the 2018 Montecito Creek mudslide and customized the CDR-MAS with the local environment to systematically generate stream data sets. Our experimental evaluation showed that event mapping based on citizen update streams is significantly influenced by the amount of citizen participation and movement. Compared with a baseline of 100% participation, with 40% citizen participation, the event region was predicted with 40% accuracy, showing that citizen update streams can provide timely information in a smart city.

中文翻译：

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使用公民更新流实时绘制自然灾害地图

摘要 洪水、野火和泥石流等自然灾害是罕见的事件，但它们在不可预测的时间影响公民，对人类生活的影响可能是巨大的。靠近事件的市民可以提供详细的实时数据流来捕获他们的事件响应。集成的时空地图不是可视化单个更新，而是生成“大图”事件信息。我们调查了来自受影响公民的信息是否足以生成正在发生的自然灾害的地图的问题。我们构建了市民灾难反应多代理模拟 (CDR-MAS)，这是一个多代理系统，可模拟市民对城市地区自然灾害的反应。我们提出了一种 rkNN 分类算法，将更新流聚合为一系列彩色 Voronoi 事件图。我们模拟了 2018 年 Montecito Creek 泥石流，并根据当地环境定制了 CDR-MAS，以系统地生成流数据集。我们的实验评估表明，基于公民更新流的事件映射受公民参与和移动量的显着影响。与 100% 参与的基线相比，40% 的公民参与，事件区域的预测准确度为 40%，表明公民更新流可以在智慧城市中提供及时的信息。