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Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective / Edgar Santos-Fernandez in Journal of the Royal Statistical Society: Series C Applied Statistics, vol 70 n° 1 (January 2021)  

Public [article]  inJournal of the Royal Statistical Society: Series C Applied Statistics > vol 70 n° 1 (January 2021) Titre : Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective Type de document : Article/Communication Auteurs : Edgar Santos-Fernandez, Auteur ; Erin E. Peterson, Auteur ; Julie Vercelloni, Auteur ; Em Rushworth, Auteur ; Kerrie Mengersen, Auteur Année de publication : 2021 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification bayesienne [Termes IGN] données écologiques [Termes IGN] estimation bayesienne [Termes IGN] modèle d'incertitude [Termes IGN] récif corallien [Termes IGN] science citoyenne Résumé : (auteur) Many research domains use data elicited from ‘citizen scientists’ when a direct measure of a process is expensive or infeasible. However, participants may report incorrect estimates or classifications due to their lack of skill. We demonstrate how Bayesian hierarchical models can be used to learn about latent variables of interest, while accounting for the participants’ abilities. The model is described in the context of an ecological application that involves crowdsourced classifications of georeferenced coral-reef images from the Great Barrier Reef, Australia. The latent variable of interest is the proportion of coral cover, which is a common indicator of coral reef health. The participants’ abilities are expressed in terms of sensitivity and specificity of a correctly classified set of points on the images. The model also incorporates a spatial component, which allows prediction of the latent variable in locations that have not been surveyed. We show that the model outperforms traditional weighted-regression approaches used to account for uncertainty in citizen science data. Our approach produces more accurate regression coefficients and provides a better characterisation of the latent process of interest. This new method is implemented in the probabilistic programming language Stan and can be applied to a wide number of problems that rely on uncertain citizen science data. Numéro de notice : A2021-509 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/IMAGERIE/MATHEMATIQUE Nature : Article DOI : 10.1111/rssc.12453 Date de publication en ligne : 11/11/2020 En ligne : https://doi.org/10.1111/rssc.12453 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102439 [article]