SCC-IRG Track 2: Independent Real-Time Sensing Data to Support Community Well-Being

SCC-IRG 第 2 轨：独立实时传感数据支持社区福祉

• 批准号: 2125319
• 负责人: P Jeffrey Brantingham
• 金额: $ 142.25万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125319&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Independent; Real

Community-led solutions to crime and disorder hold considerable promise, but also come with challenges. Community leaders and residents are likely to have a much richer, empathetic understanding of local problems, but are also likely to lack objective data. The infrastructure to collect and use data to support crime prevention resides almost exclusively with law enforcement agencies. Community-led solutions therefore require access to public or self-generated sources of data if they are to be successful. The research team is developing a low-cost acoustic gunshot detection network for use by civilian community intervention workers in Los Angeles. The project is an example of “data democratization.” Communities take the lead in data collection and dissemination as part of solving community problems.This research network uses off-the-shelf hardware and custom deep learning algorithms to recognize gunshots in real-time, including where and when they occur. Community intervention workers control the placement of detectors in the community and use a smartphone-based alert system, developed by the research team, to support their violence prevention efforts. Current evidence suggests that community intervention workers can stop violent retaliations if they are able to respond quickly when violence occurs. The research team uses controlled experiments and causal inference methods to assess the impact of the gunshot detection network on community-led violence prevention. The project builds trust in data and an appreciation for the inclusion of experimental methods in community-led models. Demonstrating successful data-driven solutions is essential to the development of smart and connected communities.This 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.

社区主导的解决犯罪和混乱的办法有很大的希望，但也带来了挑战。社区领导人和居民可能对当地问题有更丰富、更有同情心的理解，但也可能缺乏客观数据。收集和使用数据以支持预防犯罪的基础设施几乎完全属于执法机构。因此，社区主导的解决方案要想取得成功，就需要获得公共或自行生成的数据来源。该研究小组正在开发一种低成本的声学枪声探测网络，供洛杉矶的民间社区干预工作者使用。该项目是“数据民主化”的一个例子。社区在数据收集和传播方面处于领先地位，这是解决社区问题的一部分。该研究网络使用现成的硬件和定制的深度学习算法来实时识别枪击事件，包括枪击事件发生的地点和时间。社区干预工作人员控制探测器在社区的放置，并使用研究小组开发的基于智能手机的警报系统来支持他们的暴力预防工作。目前的证据表明，社区干预工作者如果能够在暴力发生时迅速作出反应，就可以制止暴力报复。研究小组使用对照实验和因果推理方法来评估枪击检测网络对社区领导的暴力预防的影响。该项目建立了对数据的信任，并赞赏将实验方法纳入社区主导的模型。展示成功的数据驱动解决方案对于智能和互联社区的发展至关重要。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Low-Cost Gunshot Detection System with Localization for Community Based Violence Interruption

• DOI: 10.1109/dsaa60987.2023.10302469
• 发表时间: 2023-10
• 期刊: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
• 作者: Isaac Manring;James H. Hill;George O. Mohler;P. Brantingham;Thomas Williams;Bruce White