Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 231 - Statistical Methods Under Preferential and Informative Sampling
Type: Topic Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #322195
Title: Preferential Sampling in Opportunistic Citizen Science Data
Author(s): Becky Tang* and Alan E. Gelfand
Companies: Duke University and Duke University
Keywords: Geostatistical model; intensity function; log Gaussian Cox process; preferential sampling; nonhomogeneous Poisson process
Abstract:

Citizen science databases are increasing in importance as sources of ecological information, but variability in effort across locations is inherent to such data. Spatially biased data—data not sampled uniformly across the study region—is expected. A further introduction of bias is variability in the level of sampling activity across locations. This motivates our work: with a spatial dataset of visited locations and sampling activity at those locations, we propose a model-based approach for assessing effort at these locations. Adjusting for potential spatial bias both in terms of sites visited and in terms of effort is crucial for developing reliable species distribution models. Using data from eBird, a global citizen science database dedicated to avifauna, and illustrative regions in Pennsylvania and Germany, we model spatial dependence in both the observation locations and observed activity. We employ point process models to explain the observed locations in space, fit a geostatistical model to explain observation effort at locations, and explore the potential existence of preferential sampling, i.e., dependence between the two processes.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program