Enhancing VGI application semantics by accounting for spatial bias

Abstract

Volunteered geographic information (VGI) is becoming an important source of geospatial big data that support many applications. The application semantics of VGI, i.e. how well VGI reflects the real-world geographic phenomena of interest to the application, is essential for any VGI applications. VGI observations often are spatially biased (e.g. spatially clustered). Spatial bias poses challenges on VGI application semantics because it may impede the quality of inferences made from VGI. Using species distribution modeling (SDM) as an example application, this article argues that spatial bias impedes VGI application semantics, as gauged by SDM model performance, and accounting for bias enhances application semantics. VGI observations from eBird were used in a case study for modeling the distribution of the American Robin (Turdus migratorius) in U.S. T. migratorius observations from the North American Breeding Bird Survey were used as independent validation data for model performance evaluation. A grid-based strategy was adopted to filter eBird species observations to reduce spatial bias. Evaluations show that spatial bias in species observations degrades SDM model performance and filtering species observations improves model performance. This study demonstrates that VGI application semantics can be enhanced by accounting for the spatial bias in VGI observations.