Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project

The recent rise of neogeography and citizen sensing has increased the opportunities for the use of crowdsourcing as a means to acquire data to support geographical research. The value of the resulting volunteered geographic information is, however, often limited by concerns associated with its quality and the degree to which the contributing data sources may be trusted. Here, information on the quality of sources of volunteered geographic information was derived using a latent class analysis. The volunteered information was on land cover interpreted visually from satellite sensor images and the main focus was on the labeling of 299 sites by seven of the 65 volunteers who contributed to an Internet‐based collaborative project. Using the information on land cover acquired by the multiple volunteers it was shown that the relative, but not absolute, quality of the data from different volunteers could be characterized accurately. Additionally, class‐specific variations in the quality of the information provided by a single volunteer could be characterized by the analysis. The latent class analysis, therefore, was able to provide information on the quality of information provided on an inter‐ and intra‐volunteer basis.