[Research Article] Evaluating the quality of VGI and authoritative data in red-crowned crane conservation: a comparative study
image: Analytical framework and comparative metrics for data quality assessment.
Credit: Big Earth Data
A recent study in Big Earth Data evaluates the reliability of Volunteered Geographic Information (VGI) for ecological conservation, using red-crowned crane habitats in Hokkaido as a case study. Comparing VGI with authoritative datasets reveals uneven data quality across different formats. While eBird's vector observations unexpectedly outperformed the authoritative GBIF database in data completeness and remote spatial coverage, OpenStreetMap (OSM) raster land-use records exhibited significant omission errors compared to CAS Earth. Consequently, the research cautions against the indiscriminate use of crowdsourced data, advocating for tailored, automated quality control systems to safely integrate VGI into ecological modeling and spatial planning.
Citation
Gao, Y., Jin, J., & Wang, S. (2025). Evaluating the quality of VGI and authoritative data in red-crowned crane conservation: a comparative study. Big Earth Data, 1–24. https://doi.org/10.1080/20964471.2025.2601434
Abstract
Volunteered Geographic Information (VGI) has emerged as a valuable resource in citizen science, yet concerns about its data quality remain. While prior studies have focused on specific data types or platforms, few have systematically evaluated heterogeneous VGI datasets within a unified application context. Addressing this gap, this study evaluates the data quality of VGI by comparing vector data from eBird and raster data from OpenStreetMap against authoritative datasets from GBIF and CAS Earth, focusing on red-crowned crane habitats in Hokkaido, Japan. The evaluation focuses on thematic accuracy, coverage, and hotspot distribution, which are highly relevant to ecological conservation. For vector data in species distribution, VGI shows higher thematic accuracy and broader spatial coverage, while authoritative data spans a longer time range. Hotspot analysis reveals a more balanced distribution in VGI and stronger clustering in authoritative data. For raster data in land use, OSM performs well in forest classification but poorly in cropland and grassland, with notable coverage gaps. Additionally, hotspot analysis reveals differing spatial clustering patterns between VGI and authoritative datasets. These findings underscore the varying reliability of VGI across data types and sources, and call for tailored validation strategies to enhance its utility in ecological research.
Data quality; volunteered geographic information; red crowned crane; vector data; raster data
Big Earth Data is an interdisciplinary Open Access journal which aims to provide an efficient and high-quality platform for promoting the sharing, processing and analyses of Earth-related big data, thereby revolutionizing the cognition of the Earth’s systems. The journal publishes a wide range of content, including Research Articles, Review Articles, Data Notes, Technical Notes, and Perspectives. It is now included in ESCI (IF=3.8, Q1), Scopus (CiteScore=9.0, Q1), Ei Compendex, GEOBASE, and Inspec. Starting from 2023, Big Earth Data has announced a new award series for authors: Best and Outstanding Paper Awards.