While increasingly popular, small unmanned aerial vehicles, aka drones, are often flown illegally over outdoor public gatherings or public facilities like prisons, threatening the safety of those nearby. There is a clear need to address interloping drones in public spaces from a sociotechnical perspective, including understanding the tradespace of variables. Through surveys, interviews, technology and infrastructure design, and experimentation, we developed a tradespace model of those variables that managers and designers of high-risk settings like public spaces and prisons need to consider in their development or renovation. These include cost considerations, both capital and infrastructure, as well as technology design elements of range and false alarm rates potentially exacerbated by convolutional neural networks (aka, deep learning). Results also highlight that environmental design elements can provide a possible low-tech solution in the design of obstructions that either eliminate or complicate a drone pilot’s line of sight. This effort demonstrates that managers and designers of high-risk settings like public spaces and prisons will have to balance sometimes competing objectives to obtain the best possible outcomes for public safety.

虽然越来越受欢迎，但小型无人驾驶飞行器（又名无人机）经常非法飞越户外公共集会或监狱等公共设施，威胁附近人员的安全。从社会技术的角度来看，显然需要解决公共空间中的无人机相互干扰问题，包括了解变量的交易空间。通过调查、访谈、技术和基础设施设计以及实验，我们开发了一个交易空间模型，其中包含公共空间和监狱等高风险环境的管理者和设计师在开发或翻新时需要考虑的变量。其中包括资本和基础设施的成本考虑，以及卷积神经网络（又名深度学习）可能加剧的范围和误报率的技术设计元素。结果还强调，环境设计元素可以在障碍物设计中提供一种可能的低技术解决方案，消除或复杂化无人机飞行员的视线。这一努力表明，公共场所和监狱等高风险环境的管理者和设计者必须平衡有时相互竞争的目标，以获得公共安全的最佳可能结果。