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

社区主导的犯罪和骚乱解决方案前景看好，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、更有同理心的理解，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如果要取得成功，就需要访问公共或自行生成的数据源。研究小组正在开发一种低成本的声学枪击探测网络，供洛杉矶的平民社区干预工作人员使用。该项目是“数据民主化”的一个例子。作为解决社区问题的一部分，社区在数据收集和传播方面发挥了带头作用。这个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，包括枪击发生的地点和时间。社区干预工作人员控制着探测器在社区中的放置，并使用研究团队开发的基于智能手机的警报系统来支持他们预防暴力的努力。目前的证据表明，如果社区干预工作人员能够在暴力发生时迅速做出反应，他们就可以阻止暴力报复。研究小组使用对照实验和因果推理方法来评估枪击探测网络对社区主导的暴力预防的影响。该项目建立了对数据的信任，并对将实验方法纳入社区主导的模型表示赞赏。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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.