报告人简介：
Dr. Alexis Comber is the Professor of Spatial Data Analytics in the School of Geography, University of Leeds, UK. His research develops methods to integrate and analyse high volumes of spatial data in order to uncover hidden patterns and to provide spatial insight for social and environmental applications. The context for this work massive increase in spatially referenced data from GPS-enabled mobile devices as well as the increased availability of more traditional data through open data initiatives. He worked in many subject areas (accessibility, public health, service optimization, land cover, land use, remote sensing, etc) that are linked by a focus on techniques for spatial analysis and geo-computation. He has recently co-authored the first introductory text book for spatial analyses in R ('An Introduction to R for Spatial Analysis and Mapping'). He has long standing research interests in semantics in relation to data quality and uncertainty.
报告摘要：
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.
时间：9月2日上午9:30— 10：30
地点：遥感betway必威西汉姆联官网附3-202
