Here you will find the materials to reproduce descriptive statistics and figures from The sensor desert quandary: What does it mean (not) to count in the smart city? recently published in Transactions of the Institute of British Geographers. The full paper is available here.
Abstract: As a central component of the smart city, sensor infrastructures locate and measure a wide range of variables in order to characterise the urban environment. Perhaps the most visible expression of the smart city, sensor deployment is a key equity concern. As new sensor technologies and resultant data interact with social processes, they have the potential to reproduce well-documented spatial injustices. Contrary to promises of providing new knowledge for cities, they can also create new gaps in understanding about specific urban populations that fall into the interstices of data collection—what we term sensor deserts. Building upon emerging data justice debates, specifically considering distributional, recognition and procedural forms of injustice, we conceptualise and analyse sensor deserts through two case studies, Newcastle’s Urban Observatory (UK) and Chicago’s Array of Things (US). Open sensor locations are integrated with small-area socio-economic data to evidence the demographic configuration and spatialities of sensor deserts across each city. We illustrate how the structural processes via which inequality is reinforced by smart agendas manifest as uneven social and spatial outcomes. In doing so, the paper opens up a new conceptual space in which to consider what it means (not) to count in the smart city, bringing a demographic perspective to critical debates about smart urbanisms.
Keywords: Sensor desert, smart city, spatial inequality, data justice, recognition.
Acknowledgements: This project is funded by the Alan Turing Insitute project #R-NEW-001. We are also grateful to the Newcastle Urban Observatory.
There are multiple ways of conceptualising sensor deserts (see table). In this analysis, we take a material and utilitarian approach to measurement of sensor coverage. We therefore assume that if a small area contains a sensor then it has coverage to some degree, limiting our understanding in several ways. This binary approach underestimates the complexity of sensor technologies and the data generated, given that different types of sensors vary in their scales of measurement. We cannot account for diversity within each small area, or its size, and therefore the extent to which a single sensor is representative of that setting. The approach also does not account for areas that contain more than one sensor. These assumptions are necessary owing to the constraints imposed by the boundaries for which administrative datasets are available.
