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Many research domains use data elicited from "citizen scientists" when adirect measure of a process is expensive or infeasible. However, participantsmay report incorrect estimates or classifications due to their lack of skill.We demonstrate how Bayesian hierarchical models can be used to learn aboutlatent variables of interest, while accounting for the participants' abilities.The model is described in the context of an ecological application thatinvolves crowdsourced classifications of georeferenced coral-reef images fromthe Great Barrier Reef, Australia. The latent variable of interest is theproportion of coral cover, which is a common indicator of coral reef health.The participants' abilities are expressed in terms of sensitivity andspecificity of a correctly classified set of points on the images. The modelalso incorporates a spatial component, which allows prediction of the latentvariable in locations that have not been surveyed. We show that the modeloutperforms traditional weighted-regression approaches used to account foruncertainty in citizen science data. Our approach produces more accurateregression coefficients and provides a better characterization of the latentprocess of interest. This new method is implemented in the probabilisticprogramming language Stan and can be applied to a wide number of problems thatrely on uncertain citizen science data.

Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective