Our paper that has been recently accepted for publication in the International Journal of Geographical Information Science investigates a thus-far unexplored aspect of spatial data of particular relevance to VGI usability (OSM as a case) – the differences between (geometric) feature definitions within a feature class. I am particularly pleased by this paper as it is the outcome of a short, but intense and very satisfying collaboration with Stephen Maguire, a Masters of IT (Spatial) student here at the University of Melbourne. Congratulations Stephen!


From the abstract:

Map databases traditionally capture snapshot representations of the world following strict data collection and representation guidelines. The content of these map databases is often assessed using data quality metrics focusing on accuracy, completeness and consistency. The success of volunteered geographic information, supporting evolving representations of the world based on fluid guidelines, has rendered these measures insufficient. In this paper, we address the need to capture the variability in quality of a map database. We propose a new spatial data quality measure — dataset maturity — enabling assessment of the database based on temporal trends in feature definitions, specifically geometry type definitions. The proposed measure can be (1) efficiently used to identify feature definition patterns reflecting community consensus that could be formalised in community guidelines; and (2) deployed to identify regions that would benefit from increased editorial activity to achieve greater map homogeneity. We demonstrate the measure based on the content of the OpenStreetMap database in four regions of the world, and show how the proposed dataset maturity measure captures a distinct quality of the datasets, distinct to data completeness and consistency.

Read the full thing in pre-print, supplementary materials here.

Maguire, S., & Tomko, M. (accepted 2017). Ripe for the Picking? Dataset Maturity Assessment based on Temporal Dynamics of Feature Definitions. International Journal of Geographical Information Science. doi:10.1080/13658816.2017.1287370