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

Abstract

ABSTRACT:Volunteer geographical information (VGI) either in the context of citizen science, active crowdsourcing and even passive crowdsourcing has been proven useful in various societal domains such as natural hazards, health status, disease epidemic and biological monitoring. Nonetheless, the variable degrees or unknown quality due to the 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. Using two examples, namely land cover validation and inundation extent estimation, this paper discusses the close links between QA and DCDF in order to determine whether a disentanglement can be beneficial or not to a better understanding of the data curation process and to its methodology with respect to crowdsourcing data. Far from rejecting the usability quality criterion, the paper advocates for a decoupling of the QA process and the DCDF step as much as possible but still in integrating them within an approach analogous to a Bayesian paradigm.

Defining Fitness-for-Use for Crowdsourced Points of Interest (POI)

“…Apart from POI, there has been work focusing on matching co-referent geo-objects of linear (e.g., [35][36][37]) or polygonal (e.g., [15,38]) geometry types. In a combined evaluation of quality control measures and data conflation from different VGI sources, [7] state that in practice, the two steps are often entangled, which, according to the authors, limits the possibilities to evaluate the fitness-for-use of such data.…”

“…Compared to traditional spatial data, as provided by commercial vendors or the authorities, Volunteered Geographic Information (VGI) is in particular need of adequate methods for data quality assessment, a fact which is due to its contributors being untrained and heterogeneous, a lack of formal specifications and the potential effects of social factors [5][6][7][8]. Since in this context, quality assurance is challenging at best [6], it can be argued that the task of quality assessment has been somewhat shifted from the producer to the users of the data, who are required to evaluate its appropriateness with regards to their specific motive, or its fitness-for-use [9].…”

Citizen observations contributing to flood modelling: opportunities and challenges

“…Table 4 presents the articles considered for this review. Compared to previously discussed variables, the contribution of citizens to land cover map generation has already been proven as a concept (Albrecht et al, 2014;Fritz et al, 2012), and is currently being researched further for data quality (Salk et al, 2016) and fusion of maps (Lesiv et al, 2016).…”

Using digital technologies to improve the design and enforcement of public policies

“…Examples of participative data collection efforts are particularly common in the environmental sphere. Commonly referred to as "citizen science", recent initiatives include eBird, HotSpotter and Air Sensor Toolboxes (see Leibovici et al 2017, Clark 2014, and Glicksman et al 2016, Ziegler et al 2015. The technologies required in order for such programmes to be successful vary.…”