Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective

TL;DR

This paper presents a Bayesian hierarchical model to correct misclassification errors in crowdsourced ecological data, improving the accuracy of ecological indicators derived from citizen science classifications.

Contribution

The paper introduces a Bayesian approach that accounts for participant abilities and spatial factors, enhancing the analysis of crowdsourced ecological data over traditional methods.

Findings

Model outperforms weighted-regression approaches

Produces more accurate regression coefficients

Better characterizes the latent ecological process

Abstract

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 characterization 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.