Data Quality Comparison: VGI vs Authoritative Source

The most important value of VGI may lie in what it can tell about local activities in various geographic locations that go unnoticed by the world’s media, and about life at a local level.

In 2007, the concept of Volunteered Geographic Information (VGI) was first systematically introduced by Michael F. Goodchild in his seminal paper  Citizens as Sensors: The World of Volunteered Geography.  Goodchild emphasized that the emergence of VGI allows the general public to become part of a network of “dynamic sensors.

The application of VGI has not only changed the way geographic information is generated, but has also had a far-reaching impact in a number of fields.

From disaster response to urban planning, VGI is everywhere. However, with great data comes great responsibility—how reliable is it? What biases exist? As VGI shapes real-world decisions, data quality is more critical than ever.

Therefore, we decided to focus on the specific application of the VGI platform for animal conservation, exploring the opportunities and challenges presented by VGI data from the perspective of the endangered Red-Crowned Crane.

Next, we will focus on two key datasets essential for Red-Crowned Crane conservation: species observations and land use. Using Hokkaido, Japan—the region with the highest crane density—as our study area, we will compare the data quality of VGI and authoritative sources, evaluating their strengths and limitations in wildlife monitoring.

A surprising finding in this study is that cleaned VGI data for Red-Crowned Cranes  surpasses authoritative data in both volume and quality , challenging the common assumption that authoritative datasets are more complete. This may be due to eBird’s structured data entry, which ensures mandatory fields are filled, enhancing data integrity.

However, in land use analysis, OSM performs well in certain categories but exhibits significant  classification errors and coverage gaps . While eBird’s species observations extend into sparse regions, OSM’s land use data is largely dependent on overlapping areas with CASEarth, showing l imited independent coverage . This highlights varying spatial completeness across VGI data types.

In summary, VGI can effectively complement authoritative data, particularly in species distribution, but its reliability depends on data type, collection mechanisms, and spatial consistency. With improvements in data validation and collection strategies, VGI has the potential to be a valuable supplement for geospatial analysis and conservation efforts.

VGI’s broad coverage, real-time updates, and community-driven contributions make it a powerful resource for mapping, spatial analysis, and decision-making. However, challenges such as temporal gaps, spatial inconsistencies, and validation issues must be addressed for wider adoption.

To unlock VGI’s full potential, collaboration between citizen scientists, researchers, and data platforms is essential. Improving data validation, integration with authoritative sources, and long-term monitoring frameworks will enhance its credibility and usability across disciplines.

Geographic data is no longer just for experts, everyone can contribute.

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