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Open AccessArticle

On Data Quality Assurance and Conflation Entanglement in Crowdsourcing for Environmental Studies

Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2RD, UK
Earth Observation Group, Aberystwyth University Penglais, Aberystwyth SY23 3FL, UK
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK
EDINA, University of Edinburgh, Edinburgh EH8 9YL, UK
Author to whom correspondence should be addressed.
Academic Editors: Alexander Zipf, Linda See, David Jonietz, Vyron Antoniou and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 78;
Received: 30 November 2016 / Revised: 6 March 2017 / Accepted: 8 March 2017 / Published: 11 March 2017
(This article belongs to the Special Issue Volunteered Geographic Information)
PDF [1397 KB, uploaded 11 March 2017]


Volunteer 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
Keywords: data curation; data quality; ISO standard; data fusion; data conflation; citizen science; crowdsourcing data curation; data quality; ISO standard; data fusion; data conflation; citizen science; crowdsourcing

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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.

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