Correcting inferences for volunteer-collected data with geospatial sampling bias

TL;DR

This paper presents a method to correct geospatial sampling bias in citizen science data by using geographical covariates and regression models, improving the accuracy of environmental inferences such as night sky brightness.

Contribution

It introduces a bias correction approach that enhances the scientific reliability of volunteer-collected geospatial data by integrating covariates and regression techniques.

Findings

Night sky brightness estimates significantly change after bias correction.

Corrected inferences align better with satellite-derived skyglow measures.

Geospatial bias correction can substantially improve citizen science data quality.

Abstract

Citizen science projects in which volunteers collect data are increasingly popular due to their ability to engage the public with scientific questions. The scientific value of these data are however hampered by several biases. In this paper, we deal with geospatial sampling bias by enriching the volunteer-collected data with geographical covariates, and then using regression-based models to correct for bias. We show that night sky brightness estimates change substantially after correction, and that the corrected inferences better represent an external satellite-derived measure of skyglow. We conclude that geospatial bias correction can greatly increase the scientific value of citizen science projects.