Abstract

A central component of the ‘smart’ city, sensors locate and measure a wide range of variables in order to characterise the urban environment. However, decisions about the placement of sensors in cities are complex, balancing structural inequalities with the differential needs of populations, local stakeholder priorities and the technical requirements of the sensors themselves.

Despite promising to provide new knowledge and data for cities, sensor networks can create new gaps in understanding about the urban populations for which data are not collected – sensor deserts. However, rapid developments in urban data collection and Geographic Data Science have the potential to support more equitable decision-making processes.

In this talk, Caitlin will first discuss and evidence some of the different types of injustice embedded in existing sensor networks. Spatial optimisation algorithms are then used to optimise sensor placement based on a range of demographic variables. Caitlin will show how even the most cutting-edge algorithms cannot deliver on complete and equitable sensor coverage, yet spatial optimisation is still a useful descriptive tool for illustrating the complex trade-offs that exist in the placement of sensors. 

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Watch the previous talks on the Spatial Analytics + Data YouTube channel.