On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies
AbstractVolunteer geographical information (VGI), either in the context of citizen science or the mining of social media, has proven to be useful in various domains including natural hazards, health status, disease epidemics, and biological monitoring. Nonetheless, the variable or unknown data quality due to crowdsourcing settings are still an obstacle for fully integrating these data sources in environmental studies and potentially in policy making. The data curation process, in which a quality assurance (QA) is needed, is often driven by the direct usability of the data collected within a data conflation process or data fusion (DCDF), combining the crowdsourced data into one view, using potentially other data sources as well. Looking at current practices in VGI data quality and using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF. It aims to help in deciding whether a disentanglement can be possible, whether beneficial or not, in understanding the data curation process with respect to its methodology for future usage of crowdsourced data. Analysing situations throughout the data curation process where and when entanglement between QA and DCDF occur, the paper explores the various facets of VGI data capture, as well as data quality assessment and purposes. Far from rejecting the usability ISO quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible while still integrating them within an approach analogous to a Bayesian paradigm. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Leibovici, D.G.; Rosser, J.F.; Hodges, C.; Evans, B.; Jackson, M.J.; Higgins, C.I. On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies. ISPRS Int. J. Geo-Inf. 2017, 6, 78.
Leibovici DG, Rosser JF, Hodges C, Evans B, Jackson MJ, Higgins CI. On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies. ISPRS International Journal of Geo-Information. 2017; 6(3):78.Chicago/Turabian Style
Leibovici, Didier G.; Rosser, Julian F.; Hodges, Crona; Evans, Barry; Jackson, Michael J.; Higgins, Chris I. 2017. "On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies." ISPRS Int. J. Geo-Inf. 6, no. 3: 78.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.