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

Abstract

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.

Earth Observation for Citizen Science Validation, or Citizen Science for Earth Observation Validation? The Role of Quality Assurance of Volunteered Observations

“…These annotations, only used when communicating graphically between stakeholders, are conformant to the BPMN standard. The metadata on data quality belongs to three different quality models (see Appendix B for a full description): the producer model, generating the spatial data quality from the ISO19157, the consumer model following the principle of the user feedback model (Geospatial User Feedback Standard Working Group www.opengeospatial.org/projects/groups/gufswg) and the stakeholder quality model [26,36]. The ISO 19157 establishes the principles for describing the quality of geographic data Qualifying each citizen volunteer, the stakeholder quality model produces quality elements that are updated at each new participation in a survey (from running the associated QA) but current values are kept associated with the observation when it is made by this citizen volunteer.…”

“…A screenshot and video of the interface can be viewed in the GitHub repository (https://github.com/cobweb-eu/workflow-at) and shows composition of a QA workflow, including choosing a QC from a list of QCs (classified as one of the 7 pillars), populating the necessary input parameters and input data, then continuing to instantiate the workflow. [26,36].…”

“…In the context of CS, the quality controls (QCs) can be related to both the design of data capture tools a priori to a QA procedure, and to (geo)computational operations that output quality values according to particular measures, either during data capture or a posteriori to the data capture (a posteriori QA). Selection of the QCs, which feed into the QA procedure, is often decided with future data usage in mind (fitness for purpose) [26].…”

“…This paper goes on to comprehensively describe the purpose of the COBWEB e-infrastructure, in specific relation to QA, expanding upon previous work [26,36] (Section 2). Then, applicability of QA is analyzed using an INNS JKW case study based in the Snowdonia National Park, in Wales, UK (Section 3).…”

Assessing Crowdsourced POI Quality: Combining Methods Based on Reference Data, History, and Spatial Relations

“…Despite the aforementioned and ongoing research around VGI quality, there is no holistic and comprehensive framework for assessing the overall quality of such data (research in this direction can be found, for example, in [13,14]) and the existing methods (e.g., ISO-based quality evaluation) are not inclusive or flexible enough to accommodate this new type of data.…”