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Abstract
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.