Conference: Towards a mass shooting early alert network by modeling 9-1-1 data streams

大规模枪击事件在美国是一个日益严重的问题，特别是在校园里。由于警报和决策链效率低下或负担过重，对这些事件的应急反应往往被拖延。由于诸如大规模枪击事件的公共安全事件导致事件附近的9-1-1呼叫活动的突发，因此通过人工智能和时空数据挖掘的最新进展实现的对这种呼叫的模式的分析和建模对于新颖的智能通知网络具有重要的希望，即使在信息通过传统响应信道传播之前，也能够触发有意义的警报。拟议的研讨会将汇集来自公共安全行业，9-1-1运营商，监管机构，计算机科学和人工智能研究人员，教育工作者和政策制定者的专家，共同探讨机会和关键研究，技术和组织挑战，以及使用9-1-1数据流早期检测大规模伤亡事件的策略。该研讨会将有助于澄清与实施基于9-1-1呼叫模式的预测分析工具和服务相关的数据需求、问题和限制，并为原型早期预警系统奠定基础，它可以部署在美国的多个司法管辖区，以确保更有效和及时的应急响应。基于下一代911（NG 911）预测分析模型将帮助公共安全专业人员改变国家的911紧急响应系统，使其能够触发和定制对不同紧急情况的早期响应。研讨会的讨论重点将是：（1）改进的9-1-1数据数据模型，使其能够有效地用于实时公共安全紧急事件检测和通知;（2）在9-1-1和类似的大规模数据集中进行时空模式检测的初步结果和挑战，特别是来自学校周边等地理围栏区域的数据;（3）使用最先进的人工智能和机器学习工具开发大规模枪击事件预测模型的初步结果和机会;以及（4）用户需求，以及在一个健壮的，可互操作的，可靠的方式。实现这些目标，并概述一个全面的研究议程，以研究至关重要的多学科问题，有效地应对大规模枪击事件，将需要不同的跨部门知识和专业知识的融合，这些知识和专业知识以前没有被吸收这个奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

Mass shootings are a growing problem in the United States, especially on school campuses. Emergency response to these events is often delayed due to inefficient or overburdened alert and decision-making chains. As public safety events such as mass shootings result in bursts of 9-1-1 call activity in the vicinity of the events, analysis, and modeling of patterns of such calls enabled by recent advances in artificial intelligence and spatiotemporal data mining holds significant promise for a novel intelligent notification network, capable of triggering meaningful alerts even before the information propagates through the legacy response channels. The proposed workshop will bring together experts from the public safety industry, 9-1-1 operators, regulatory bodies, computer science and AI researchers, educators, and policymakers, to jointly explore the opportunities and critical research, technical and organizational challenges, and strategies of using 9-1-1 data streams for early detection of mass casualty events. The workshop will help clarify data needs, concerns, and constraints related to the implementation of predictive analytics tools and services based on 9-1-1 call patterns and lay the groundwork for a prototype early alert system, which could be deployed in multiple jurisdictions across the United States to ensure a more efficient and timely emergency response.Building on the Next Generation 911 (NG911) standards and protocols for multi-channel and multimedia notifications, the predictive analytics models would let public safety professionals transform the nation’s 911 emergency response system, making it possible to trigger and tailor an early response to different emergencies. The workshop discussions will focus on (1) an improved data model for 9-1-1 data that would enable its efficient use in real-time public safety emergency event detection and notification; (2) initial results and challenges of spatiotemporal pattern detection in 9-1-1 and similar massive datasets, particularly from geo-fenced areas such as school perimeters; (3) preliminary results and opportunities for developing predictive models of mass shooting events using state-of-the-art AI and machine learning tools; and (4) user requirements, and technical, organizational and ethical challenges of deploying such models in operational emergency response networks in a robust, interoperable, and trustworthy manner. Achieving these goals, and outlining a comprehensive research agenda to study the critically-important multi-disciplinary problem of efficient response to mass shootings, will require a convergence of diverse cross-sectoral knowledge and expertise that has not been assimilated previouslyThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.