Rachel Franklin | Optimising for equity: sensor coverage, networks, and the responsive city

Decisions about where to place a sensor in a city are inherently complex: balancing the differential needs of populations and structural urban inequalities with the priorities of local stakeholders and the technical specificities of sensors themselves. Rapid developments in urban data collection and spatial data science have the potential to support these complex decision-making processes. Yet even the most cutting-edge algorithms cannot deliver on complete and equitable sensor coverage. Inevitable trade-offs occur, with uneven implications for urban populations – however these trade-offs are rarely the focus of detailed analysis. In this paper, we illustrate the equity challenge using spatial optimisation to demonstrate the decision-making processes involved in the equitable placement of sensor infrastructures in cities. Focusing on a case study of air-quality sensor networks in Newcastle-upon-Tyne, we assess the trade-offs that are imposed on decision-makers: number of sensors, placement, and coverage of demographic sub-groups. In doing so, we highlight the ways in which trade-offs and associated inequalities are implicit in algorithms that aim to support decision-making processes. Our results suggest that even with the best intentions, urban decision-makers are limited in their capacity to develop equitable solutions. Inequities in sensor networks needn’t be the outcome of unfair, unjust, or biased processes: even with "optimal" decision making, sensor networks are going to miss important groups.

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