Direct Passive Participation: Aiming for Accuracy and Citizen Safety in the Era of Big Data and the Smart City

Abstract

The public services in our smart cities should enable our citizens to live sustainable, safe and healthy lifestyles and they should be designed inclusively. This article examines emerging data-driven methods of citizen engagement that promise to deliver effortless engagement and discusses their suitability for the task at hand. Passive participation views citizens as sensors and data mining is used to elicit meaning from the vast amounts of data generated in a city. Direct passive participation has a clear link between the creation and the use of the data whereas indirect passive participation does not require a link between creation and use. The Helsinki city bike share scheme has been selected as a case study to further explore the concept of direct passive participation. The case study shows that passive user generated data is a strong indicator of optimum city bike station sizing relative to the existing methods that are already in use. Indirect passive participation is an important area of development; however, it still needs to be developed further. In the meantime, direct passive participation can be one of the tools used to design inclusive services in a way that is safe and an accurate representation of the citizens’ needs.