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Surveys and facilitated user-engagement were conducted among stakeholders during the development of CWDAT. We developed an operational citizen science tool called the Community Water Data Analysis Tool (CWDAT)-a R/Shiny-based web application designed for community-based water quality monitoring.
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Open-source tools for the analysis, visualization, and reporting of citizen science data hold promise for addressing the challenge of data quality, while providing other benefits such as technical capacity-building, increased user engagement, and reinforcing data sovereignty. One key barrier often cited in the citizen science literature is data quality. Despite this heterogeneity, there are several common barriers to the uptake and sustainability of citizen science projects and the information they generate. We discuss the results of a user evaluation of this app, and finally discuss our findings within the context of citizens’ engagement.Ĭitizen science initiatives span a wide range of topics, designs, and research needs. Based on this literature, we present a mobile-based flood monitoring citizen science application aimed at supporting data collection activities in a real-world CS project as part of an EU project. We outline a literature review of exploring and understanding various motivational and engagement factors that influence the participation of citizens in technology-driven CS activities. In this paper, we explore engagement factors, factors that help keeping engagement high, in technology-centric CS projects where technology is a key enabler to support CS activities.
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The participation and engagement of citizens are vital and yet often a real challenge in ensuring the long-term continuity of CS projects. Such activities are also designed with a feedback loop where the Citizens benefit from their participation by gaining a greater sense of awareness of their local issues while also influencing how the activities can align best with their local contexts. A popular complimentary science to traditional sensor-based data collection is to design Citizen Science (CS) activities in collaboration with citizens and local communities. In such situations, citizens can often play a crucial role in filling this data collection gap.
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In many cases, however, it is not feasible to install devices in remote and inaccessible areas, resulting in incomplete data coverage. Achieving this requires large volumes of sensors strategically deployed across urban areas. A key aspect of the development of Smart Cities involves the efficient and effective management of resources to improve liveability.