论文标题
纠正具有地理空间采样偏见的志愿者收集数据的推断
Correcting inferences for volunteer-collected data with geospatial sampling bias
论文作者
论文摘要
志愿者收集数据的公民科学项目由于能够吸引公众解决科学问题的能力而越来越受欢迎。但是，这些数据的科学价值受到了几种偏见的阻碍。在本文中，我们通过用地理协变量丰富了志愿者收集的数据，然后使用基于回归的模型来纠正偏见来处理地理空间采样偏差。我们表明，校正后的夜空亮度估计发生了很大的变化，并且校正后的推论更好地代表了Skyglow的外部卫星衍生措施。我们得出的结论是，地理空间偏见校正可以大大增加公民科学项目的科学价值。
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.
