Empowering Citizens through Perceptual Sensing of Urban Environmental and Health Data Following a Participative Citizen Science Approach

Abstract

The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and development of a novel participatory citizen science-based application and data ecosystem model. These developments are imperative and scientifically designed to gather and process perceptual sensing of urban, environmental, and health data. This data acquisition approach allows citizens to gather and generate environment- and health-related data through mobile devices. The sum of all citizens’ data will continuously enrich and increase the volumes of data coming from the city sensors and sources across geographical locations. These scientifically generated data, coupled with data from the city sensors and sources, will enable specialized predictive analytic solutions to empower citizens with urban, environmental, and health recommendations, while enabling new data-driven policies. Although it is difficult for citizens to relate their personal behaviour to large-scale problems such as climate change, pollution, or public health, the developed ecosystem provides the necessary tools to enable a greener and healthier lifestyle, improve quality of life, and contribute towards a more sustainable local environment.

Combining the Digital, Social and Physical Layer to Create Age-Friendly Cities and Communities

“…Older adults, for instance, can use ICT to gather and share information about themselves and the environment that surrounds them. Projects like the European funded PULSE (Participatory Urban Living for Sustainable Environments) is developing a public health observatory, with the participation of intergenerational citizen scientists using wearable devices [ 50 ] across seven cities: Paris, Singapore, Birmingham, Barcelona, New York, Pavia, and Keelung.…”

Addressing new challenges in smart urban planning using Information and Communication Technologies

“…A wide range of studies has been developed to emphasize the great potential of information on the preferences and use that citizens make of urban public space, leading to the definition of what has been called a new citizen science (Mueller et al, 2018). Various data ecosystem models and participatory applications based on citizen science have been developed allowing collaborative mapping actions to help make cities safer (Jelokhani‐Niaraki et al, 2019a), healthier (Ottaviano et al, 2019), more accessible (Jelokhani‐Niaraki et al, 2019b; Mora et al, 2017), more creative (Mueller et al, 2020), more smart living friendly (Cristie & Berger, 2017) and where citizens can identify (Buš et al, 2017) thanks to this citizen design science from the bottom up.…”

Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions

“…Finally, T2D predictive models can be integrated in mobile health (mHealth) apps as tools to provide the users with a feedback about their health status (eg, a diabetes risk indicator) and recommendations on how to improve it, also resorting to gamification strategies to promote healthy behaviors. 34 Although the many potential users, T2D predictive models are scarsely adopted in real-world applications.…”