Introduction
Smart city technologies, particularly sensors, can potentially affect socio-economic and spatial inequalities. By integrating sensor data streams, fine-scale demographic data, and movement data, this project aims to identify who is affected by 'sensor deserts', ascertain coverage for vulnerable populations, and improve understanding of connections between urban mobility and sensor density and location. This work will contribute to a growing body of research that highlights the potential risk of smart cities increasing rather than reducing inequality and quality of life, providing a blueprint to assist cities in better adoption of smart city technologies.
Explaining the science
Sensors deployed across urban environments capture data on a variety of phenomena, including air quality, noise, and temperature. Sensor measurement uncertainty, coupled with spatial coverage gaps, results in relatively more information for some locations than others and, by extension, relatively more information for some sub-groups than others. This is because population density and composition also vary spatially, and individual movements within the city are selective: some areas are more heavily trafficked and spatial mobility will vary by sub-population.
This research overlays sensor coverage - using as a test case, air quality sensors from Newcastle University's Urban Observatory - with small-area demographic information, to be able to answer questions about who is served by the smart city and how coverage might be improved, given cost and other constraints.
Project aims
Smart city technologies promise to increase city efficiency, contain costs, and deliver services. Their allure is powerful. However, how should cities decide what to measure and where? How do they ensure that smart city technologies serve all inhabitants? And, importantly, how can cities maximise value for money with these technologies?
The primary goal of this project is to produce real-world oriented 'best practices' that assist city authorities to identify the purposes of adopting sensor technologies. These best practices will assist authorities to locate sensor coverage gaps and other possible challenges, using intuitive tools that facilitate negotiating trade-offs across costs, coverage, and policy-relevant data streams.
Applications
Interest in smart cities and the Internet of Things (IoT) is ubiquitous. This research will above all be useful to urban authorities and councils, local citizen groups and related stakeholders, and sensor producers and manufacturers.
