An Exploration of Future Patterns of the Contributions to OpenStreetMap and Development of a Contribution Index

Abstract

OpenStreetMap (OSM) represents one of the most well‐known examples of a collaborative mapping project. Major research efforts have so far dealt with data quality analysis but the modality of OSM's evolution across space and time has barely been noted. This study aims to analyze spatio‐temporal patterns of contributions in OSM by proposing a contribution index (CI) in order to investigate the dynamism of OSM. The CI is based on a per cell analysis of the node quantity, interactivity, semantics, and attractivity (the ability to attract contributors). Additionally this research explores whether OSM has been constantly attracting new users and contributions or if OSM has experienced a decline in its ability to attract continued contributions. Using the Stuttgart region of Germany as a case study the empirical findings of the CI over time confirm that since 2007, OSM has been constantly attracting new users, who create new features, edit the existing spatial objects, and enrich them with attributes. This rate has been dramatically growing since 2011. The utilization of a Cellular Automata‐Markov (CA‐Markov) model provides evidence that by the end of 2016 and 2020, the rise of CI will spread out over the study area and only a few cells without OSM features will remain.

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

“…Due to the advancement of Web 2.0 technologies, citizen observations have been well organized, stored, and maintained through collaborative mapping projects (CMPs: (Rouse et al 2007;), whereby these observations have been made available to the public for downloading, revising and for commenting upon. This approach has helped to collect citizen observations not only in a raw form, but also updated and corrected by other citizens, who wish to contribute to these collections (Jokar Arsanjani, Mooney, Helbich, et al 2015). These platforms provide citizens with user-friendly functionalities so that they can a) either collect information offline by GPS-enabled devices and then upload them to the platform, or b) allow them to map online based on visual interpretation of very high resolution satellite/aerial images (i.e.…”

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