Abstract
Volunteered geographical information (VGI) and citizen science have become important sources data for much scientific research. In the domain of land cover, crowdsourcing can provide a high temporal resolution data to support different analyses of landscape processes. However, the scientists may have little control over what gets recorded by the crowd, providing a potential source of error and uncertainty. This study compared analyses of crowdsourced land cover data that were contributed by different groups, based on nationality (labelled Gondor and Non-Gondor) and on domain experience (labelled Expert and Non-Expert). The analyses used a geographically weighted model to generate maps of land cover and compared the maps generated by the different groups. The results highlight the differences between the maps how specific land cover classes were under- and over-estimated. As crowdsourced data and citizen science are increasingly used to replace data collected under the designed experiment, this paper highlights the importance of considering between group variations and their impacts on the results of analyses. Critically, differences in the way that landscape features are conceptualised by different groups of contributors need to be considered when using crowdsourced data in formal scientific analyses. The discussion considers the potential for variation in crowdsourced data, the relativist nature of land cover and suggests a number of areas for future research. The key finding is that the veracity of citizen science data is not the critical issue per se. Rather, it is important to consider the impacts of differences in the semantics, affordances and functions associated with landscape features held by different groups of crowdsourced data contributors.
Highlights
The scientific community in general is excited by the opportunities afforded by the related fields of crowdsourcing, volunteered geographical information and citizen science
The analyses show how data contributed by different groups of people result in different inferences and highlight the potential impacts of unintended variations in crowdsourced data
There are many citizen science and crowdsourced data generation activities, which in the realm of geographical information science and systems is frequently referred to as ‘Volunteered geographical information (VGI)’, a phrase coined by Goodchild [16], and recent developments are reviewed in [17]
Summary
The scientific community in general is excited by the opportunities afforded by the related fields of crowdsourcing, volunteered geographical information and citizen science. In the domain of land cover and land use, the European Commission has funded a number of projects to evaluate how volunteered or crowdsourced data may be used to help manage crises and emergencies [3], to develop Citizen Observatories for Land Cover and Land Use [4] and to monitor deforestation [5]. As a result a number of crowd-sourced land cover data collection systems have been initiated with perhaps the best known of these being the Geo-Wiki system developed at IIASA, Austria [7] others exist [8, 9, 10]. Concerns over the use of crowdsourced spatial data in formal scientific analyses remain because of data quality issues [13] Data quality in this context encompasses a number of considerations, which, in increasing complexity, relate to:. Do the data adequately capture the variation in the process under investigation, in extent as well as spatially and thematically? Is the density of data points of different classes sufficient to capture the spatial distribution of the land cover present on the ground?
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