Potential of Citizen Science for Enhancing Infrastructure Monitoring Data and Decision‐Support Models for Local Communities

Abstract

Citizen science is a process by which volunteer members of the public, who commonly lack advanced training in science, engage in scientific activities (e.g., data collection) that might otherwise be beyond the reach of professional researchers or practitioners. The purpose of this paper is to discuss how citizen science projects coordinated by interdisciplinary teams of engineers and social scientists can potentially enhance infrastructure monitoring data and decision-support models for local communities. The paper provides an interdisciplinary definition of infrastructure data quality that extends beyond accuracy to include currency, timeliness, completeness, and equitability. We argue that with this expanded definition of data quality, citizen science can be a viable method for enhancing the quality of infrastructure monitoring data, and ultimately the credibility of risk analysis and decision support models that use these data. The paper concludes with a set of questions to aid in producing high-quality infrastructure monitoring data by volunteer citizen scientists.

What Methods Do Social Scientists Use to Study Disasters? An Analysis of the Social Science Extreme Events Research Network

“…The limited number of disaster research-focused methodological textbooks in the social and behavioral sciences (Norris et al, 2006; Phillips, 2014; Stallings, 2002) and public health (Institute of Medicine, 2015) are outstanding resources, but they do not consider the full range of approaches currently used by social scientists. This is, in part, because the social science methodological landscape has been rapidly transformed by the advent of big data, the introduction of new computational methodologies, and the increased participation of social scientists in interdisciplinary teams that are developing and using new methods and approaches (see, e.g., DeRouen & Smith, 2020; Gharaibeh et al, 2019; Nateghi et al, 2019; Reilly et al, 2018).…”

Post‐disaster damage classification based on deep multi‐view image fusion

“…These efforts broadly fall into two categories: crowdsourcing or participatory damage assessment and automated damage assessment using artificial intelligence (AI). Many recent studies have highlighted the use of citizen science and participatory approach to enhance data collection for infrastructure monitoring (Li et al., 2021) and disaster impact assessment (Gharaibeh et al., 2021; Khajwal & Noshadravan, 2021). On the other hand, the promise of AI has not only been encouraging in solving scientific problems in other domains (Rafiei & Adeli, 2017a), it is also emerging as a promising solution for automation and enhancing the efficiency of structural health monitoring and post‐disaster damage assessment (Xu et al., 2020).…”

Perspectives on Citizen Science Data Quality

“…The quality of CSD also can influence the analysis and interpretation of the data (Kelling et al, 2015;Clare et al, 2019). Quality information is important for scientific data, including CSD (Roman et al, 2017;Gharaibeh et al, 2019). Citizen science data contributes to many scientific endeavors that are important for environmental science and for the well-being of society, including sustainable development, humanitarian efforts, and disaster prevention and response (Hicks et al, 2019;Fraisl et al, 2020).…”