Ripe for the picking? Dataset maturity assessment based on temporal dynamics of feature definitions

Abstract

ABSTRACTMap 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.