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31 mai 2017
- handle: 10670/1.j63erp
- halshs: halshs-02344592
- doi: 10.1007/978-3-319-57336-6_4
Ce document est lié à :
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-57336-6_4
Ce document est lié à :
info:eu-repo/grantAgreement//689812/EU/A Citizen Observatory and Innovation Marketplace for Land Use and Land Cover Monitoring/LANDSENSE
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Laurence Jolivet et al., « Crowd and Community Sourced Data Quality Assessment », HAL-SHS : géographie, ID : 10.1007/978-3-319-57336-6_4
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Résumé
Data quality assessment of different volunteered initiatives and platforms presents several challenges for data validation given the high amount of data collected. This paper focuses on two goals. The first consists in defining both a generic workflow and data quality indicators for validation of reports coming from crowd and community sourcing platforms. In the proposed workflow, a qualified report can be even described by each indicator separately or by a combination of them. Here, we focus mostly on analyzing the results obtained for each indicator separately. The second goal is to learn more information about contributors who has engaged in a platform proposed by a public body (i.e., the French National Mapping Agency): Who are they? How are they contributing? What are their motivations? More is known about contributors to OpenStreetMap than of any other VGI platform. Indeed, knowing the contributors is a crucial task for both motivation and data quality, especially now that public institutions are engaging with VGI.