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，尤其是在校园。由于警报和决策链效率低下或负担过重，对这些事件的应急响应往往被推迟。由于大规模枪击等公共安全事件导致事件附近的911呼叫活动爆发，人工智能和时空数据挖掘的最新进展使此类呼叫模式的分析和建模成为一种新型智能通知网络的重要前景，甚至能够在信息通过传统响应通道传播之前触发有意义的警报。拟议的研讨会将汇集来自公共安全行业、911操作员、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，共同探讨利用911数据流早期发现大规模伤亡事件的机会和关键研究、技术和组织挑战以及战略。讲习班将有助于澄清与实施基于911呼叫模式的预测分析工具和服务有关的数据需求、关切和制约因素，并为原型预警系统奠定基础，该系统可在美国多个司法管辖区部署，以确保更有效和及时的应急反应。基于下一代911 （NG911）标准和协议的多渠道和多媒体通知，预测分析模型将使公共安全专业人员改变国家的911应急响应系统，使其能够触发和定制针对不同紧急情况的早期响应。讲习班讨论的重点将是：(1)改进9-1-1数据的数据模型，使其能够在实时公共安全紧急事件检测和通知中得到有效利用；(2)基于911和类似大规模数据集的时空模式检测的初步结果和挑战，特别是来自地理围栏区域（如学校周边）的数据集；(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；(4)在运行中的应急响应网络中以稳健、可互操作和可信赖的方式部署此类模型的用户需求、技术、组织和道德挑战。实现这些目标，并概述一个全面的研究议程，以研究对大规模枪击事件的有效反应这一至关重要的多学科问题，将需要汇集以前未被吸收的各种跨部门知识和专业知识。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.