Decisions about sensor placement in cities are inherently complex, balancing social-technical, digital, and structural inequalities with the differential needs of populations, local stakeholder priorities, and the technical specificities of the sensors themselves. Rapid developments in urban data collection and geographic data science have the potential to support these decision-making processes. Focusing on a case study of air-quality sensors in Newcastle-upon-Tyne, UK, we employ spatial optimization algorithms as a descriptive tool to illustrate the complex trade-offs that produce sensor networks that miss important groups—even when the explicit coverage goal is one of equity. We show that the problem is not technical; rather, it is demographic, structural, and financial. Despite the considerable constraints that emerge from our analysis, we argue the data collected via sensor networks are of continued importance when evidencing core urban injustices (e.g., air pollution or climate-related heat). We therefore make the case for a clearer distinction to be made between sensors for monitoring and sensors for surveillance, arguing that a wider presumption of bad intent for all sensors potentially limits the visibility of positive types of sensing. For the purpose of monitoring, we also propose that basic spatial optimization tools can help to elucidate and remediate spatial injustices in sensor networks.

关于在城市中放置传感器的决策本质上是复杂的，需要平衡社会技术、数字和结构不平等与人口的不同需求、当地利益相关者的优先事项以及传感器本身的技术特性。城市数据收集和地理数据科学的快速发展有可能支持这些决策过程。专注于英国泰恩河畔纽卡斯尔的空气质量传感器案例研究，我们采用空间优化算法作为描述性工具来说明产生错过重要群体的传感器网络的复杂权​​衡 - 即使在明确的覆盖目标是股权之一。我们表明问题不是技术问题；相反，它是人口、结构和金融方面的。尽管我们的分析中出现了相当大的限制，我们认为通过传感器网络收集的数据在证明核心城市不公正（例如，空气污染或与气候相关的热量）时具有持续的重要性。因此，我们提出了在用于监控的传感器和用于监控的传感器之间进行更明确区分的理由，认为对所有传感器的恶意意图的更广泛假设可能会限制积极类型传感的可见性。出于监控的目的，我们还建议基本的空间优化工具可以帮助阐明和纠正传感器网络中的空间不公正。认为所有传感器的恶意意图的更广泛假设可能会限制积极类型传感的可见性。出于监控的目的，我们还建议基本的空间优化工具可以帮助阐明和纠正传感器网络中的空间不公正。认为所有传感器的恶意意图的更广泛假设可能会限制积极类型传感的可见性。出于监控的目的，我们还建议基本的空间优化工具可以帮助阐明和纠正传感器网络中的空间不公正。