Designing Interactive Systems for Community Citizen Science

2018-10-11T18:59:54Z (GMT) by Yen-chia Hsu
Citizen science forges partnerships between experts and citizens through collaboration and has become a trend in public participation in scientific research over the<br>past decade. Besides this trend, public participation can also contribute to participatory democracy, which empowers citizens to advocate for their local problems. This<br>strategy supports citizens to form a community, increase environmental monitoring, gather evidence, and tell convincing stories. Researchers believe that this “community citizen science” strategy can contribute to the well-being of communities by giving them the power to influence the general public and decision makers.<br>Community citizen science requires collecting, curating, visualizing, analyzing, and interpreting multiple types of data over a large spacetime scale. This is highly dependent on community engagement (i.e., the involvement of citizens in<br>local neighborhoods). Such large-scale tasks require the assistance of innovative computational tools to give technology affordance to communities. However, existing<br>tools often focus on only one type of data, and thus researchers need to develop tools from scratch. Moreover, there is a lack of design patterns for researchers to reference when developing such tools. Furthermore, existing tools are typically treated as products rather than ongoing infrastructures that sustain community engagement.<br>This research studies the methodology of developing computational tools by using visualization, crowdsourcing, and artificial intelligence techniques to support the<br>entire community engagement lifecycle, from initiation, maintenance, to evaluation. This research will make methodological and empirical contributions to community<br>citizen science and sustainable human-computer interaction. Methodological contributions include detailed case studies with applied techniques from information<br>technology systems that are deployed in real-world contexts. Empirical contributions include generalizable empirical insights for developing interactive systems that<br>integrate multiple types of scientific data. In this dissertation, I first define “community citizen science” and explain corresponding design challenges. Then, I review existing computational tools and techniques<br>that are related to this research. Next, I present four interactive systems centered around the research scope: (1) a timelapse editor that supports building evidence-based narratives, (2) an air quality monitoring system that integrates heterogeneous data and computer vision to support the formation of scientific knowledge,<br>(3) a visualization tool that reveals the impact of oil and gas development, and (4) a mobile crowdsourced application for reporting and visualizing pollution odors. Finally, I synthesize findings from all four works into generalizable design<br>implications for future researchers and developers.