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

会议：通过对 9-1-1 数据流建模建立大规模枪击早期警报网络

• 批准号: 2330460
• 负责人: Ilya Zaslavsky
• 金额: $ 6.5万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2330460&HistoricalAwards=false
• 关键词: Conference; Towards; mass; shooting; early

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.

大规模枪击事件在美国是一个日益严重的问题，尤其是在学校校园，由于警报和决策链效率低下或负担过重，导致大规模枪击事件的爆发，导致对这些事件的紧急响应经常被延迟。人工智能和时空数据挖掘的最新进展使得事件附近的 1-1 呼叫活动、此类呼叫模式的分析和建模为新型智能通知网络带来了巨大的希望，该网络能够在事件发生之前触发有意义的警报。信息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，共同探索机遇和关键研究，该研讨会将帮助澄清与实施基于 9-1-1 的预测分析工具和服务相关的数据需求、问题和限制。 1-1调用模式并打好基础用于原型早期警报系统，该系统可以部署在美国多个司法管辖区，以确保更有效、更及时的应急响应。基于下一代 911 (NG911) 标准和多渠道和多媒体通知协议，预测分析模型将使公共安全专业人员能够改造国家的 911 应急响应系统，从而能够针对不同的紧急情况触发和定制早期响应。研讨会的讨论将重点关注 (1) 改进的 9-1-1 数据模型。这将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) 9-1-1 和大量类似数据集的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) 用户需求以及在紧急行动中部署此类模型的技术、组织和道德挑战；强大的响应网络，实现这些目标，并概述一个全面的研究议程来研究有效应对大规模枪击事件的至关重要的多学科问题，需要融合尚未被同化的不同跨部门知识和专业知识。此前，该奖项反映了 NSF 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。