Optimizing for Equity: Sensor Coverage, Networks, and the Responsive City
Optimizing for Equity: Sensor Coverage, Networks, and the Responsive City
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DOI：
10.1080/24694452.2022.2077169
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发表时间：
2022-07
影响因子：
3.9
通讯作者：
Caitlin Robinson;Rachel S. Franklin;Jack Roberts
中科院分区：
文献类型：
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作者：
Caitlin Robinson;Rachel S. Franklin;Jack Roberts
关于传感器在城市中的位置的决定本身就很复杂，需要平衡社会技术，数字和结构不平等与人口的差异需求，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迅速发展有可能支持这些决策进程。专注于空气质量传感器在英国泰恩河畔纽卡斯尔的案例研究，我们采用空间优化算法作为一种描述性的工具来说明复杂的权衡，产生传感器网络，错过重要的群体，即使明确的覆盖目标是公平的。我们表明，问题不是技术性的，而是人口、结构和金融方面的。尽管从我们的分析中出现了相当大的限制，但我们认为，通过传感器网络收集的数据在证明核心城市不公正（例如，空气污染或气候相关的热量）。因此，我们提出了一个更明确的区分传感器监测和传感器监视的情况下，认为一个更广泛的假设所有传感器的不良意图可能会限制积极类型的感知的可见性。为了监测的目的，我们还建议，基本的空间优化工具可以帮助阐明和补救传感器网络中的空间不公正。
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.