By Richmond Addo (Ghana)
Voluntary Geographic Information (VGI) has become prominent source of spatial information delivery. Through web 2.0 platforms, citizens with no or limited expertise with geodata can share their current location, create their own maps, publish georeferenced pictures and contribute to geographical databases. VGI data from a large pool of contributors with different background and varying level of spatial knowledge implies varying quality in VGI datasets.
Unlike official data which adheres to official standards and therefore maintains a certain level of quality, VGI data usually follows no such standards. Therefore, VGI data is plagued with issues of reliability, quality and overall value which seems to always impede its adoption into mainstream geographic information (Flanagin & Metzger, 2008).
This research sought to define a framework of related quality measures needed to evaluate VGI quality. To achieve this, a framework of relevant quality measures was defined and applied on selected VGI datasets.
Based on literature, it was found that VGI quality measures can be grouped under mainly two components; the Intrinsic and Extrinsic quality (G Bordogna, Carrara, Criscuolo, Pepe, & Rampini, 2015). Intrinsic quality measures relating to the ISO data quality elements while the Extrinsic quality relates to the validity of the data source and contributors. A set of quality measures were presented to a group of 44 experts and rated based on their relevance for VGI quality. The rated quality measures from the experts were then assessed against the SMART criteria to conclude on the final framework of measures.
From the study, Homogeneity, Trust, Reputation, Reliability and Credibility were identified as relevant extrinsic quality measures in the final framework. While Completeness, Temporal Accuracy, Thematic Accuracy and Positional Accuracy were the relevant intrinsic quality measures identified.
Keywords: VGI; Voluntary Geographic Information; Data quality; Data Experts