Validation of Citizen Science Data for Decision-Making during Disasters

Restricted (Penn State Only)
Author:
Hultquist, Carolynne
Graduate Program:
Geography
Degree:
Doctor of Philosophy
Document Type:
Dissertation
Date of Defense:
May 03, 2019
Committee Members:
  • Guido Cervone, Dissertation Advisor/Co-Advisor
  • Guido Cervone, Committee Chair/Co-Chair
  • Anthony C Robinson, Committee Member
  • Todd Smith Bacastow, Committee Member
  • Andrea Tapia, Outside Member
Keywords:
  • crowdsource
  • citizen science
  • data integration
  • environmental hazards
Abstract:
Varying standards for crowdsourced data can cause potentially valuable sources of data to be overlooked for scientific analysis and decision-making during disasters. Citizen science environmental monitoring data are intentionally collected, and when integrated with other sources, these observational networks have the potential to contribute to an improved understanding of human activities and the environment. However, evaluation of the validity, resolution, and usefulness of citizen science data for scientific analysis and operational response to hazards are critical aspects that need to be addressed as the data could be used by decision-makers or the public when risks are involved. Evaluation is complicated by the nature of citizen science data as it is collected at different spatio-temporal resolutions than traditional sources of data. Methods are developed to integrate heterogeneous spatio-temporal citizen science data with traditional data sources for two case studies. In the first case study, it is demonstrated that citizen science data provide reliable quantitative estimations of the spatial distribution of high concentrations of radiation around Fukushima after the March 2011 radioactive releases. In the second, it is shown that citizen science data can be leveraged to improve our understanding of the spatial distribution of flooding during Hurricane Florence in September 2018 with implications for maximum flood level mapping and disaster response. The case studies conclude that intentional citizen science data trends are consistent with government observations and models. However, contributed data are generally not uniformly spatially sampled, and tend to be collected in larger numbers in high population density areas and along built infrastructure such as roads. Therefore, analysis methods must take into account the over and under sampling of the regions. As a result of the tendency to provide data where people are, citizen science projects can also be a unique source of data to address environmental concerns in light of human impacts. The evaluation of citizen science data improves our understanding of spatio-temporal patterns of phenomena and their representation through spatial data sources. In addition to environmental conditions, social data brings insights about human impacts. Social data sources can provide essential information in real-time for decision-making during disasters.