The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007-2011

By Pascal Neis, Dennis Zielstra, Alexander Zipf. Future Internet, 2011

452 citations recorded by the source corpus.

The OpenStreetMap (OSM) project is a prime example in the field of Volunteered Geographic Information (VGI). Worldwide, several hundred thousand people are currently contributing information to the “free” geodatabase. However, the data contributions show a geographically heterogeneous pattern around the globe. Germany counts as one of the most active countries in OSM; thus, the German street network has undergone an extensive development in recent years. The question that remains is this: How does the street network perform in a relative comparison with a commercial dataset? By means of a variety of studies, we show that the difference between the OSM street network for car navigation in Germany and a comparable proprietary dataset was only 9% in June 2011. The results of our analysis regarding the entire street network showed that OSM even exceeds the information provided by the proprietary dataset by 27%. Further analyses show on what scale errors can be reckoned with in the topology of the street network, and the completeness of turn restrictions and street name information. In addition to the analyses conducted over the past few years, projections have additionally been made about the point in time by which the OSM dataset for Germany can be considered “complete” in relative comparison to a commercial dataset.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/1b4aefe425906a0181994979e16169811715f1b2

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Citations in the corpus

Beyond climate: disturbance niche shifts in invasive species (2015, methodology): “This dataset is highly accurate compared with corporate datasets (Neis et al., 2011) and we assume a similar error for all study areas.”
The world’s user-generated road map is more than 80% complete (2017, background): “European-focused studies have noted that dense areas appear to be better mapped in OSM [17, 19], presumably because there are more potential contributors with local knowledge.”
Crowdsourcing for climate and atmospheric sciences: current status and future potential (2015, unknown): “…Flanagin and Metzger, 2008; Snow et al. , 2008; Welinder et al. , 2010; Girres and Touya, 2010; Haklay et al. , 2010; Heipke, 2010; Goodchild and Glennon, 2010; Goodchild and Li, 2012; Basiouka and Potsiou, 2012; Neis et al. , 2012; Comber et al. , 2013; Foody et al. , 2013; See et al. , 2013).”
Geospatial Big Data Handling Theory and Methods: A Review and Research Challenges (2015, methodology): “This research focuses mostly on the completeness of OpenStreetMap (Haklay 2010, Mondzech & Sester 2011, Neis et al. 2012, Zielstra & Zipf 2010).”
Lanelet2: A high-definition map framework for the future of automated driving (2018, background): “In OSM as an open format, the quality of the data varies greatly [9], as these are created and maintained by volunteers.”
A review of volunteered geographic information quality assessment methods (2017, background): “The authors Schmitz et al. (2008), Neis et al. (2011), Barron et al. (2014), Siebritz (2014) have demonstrated that for each of these measures a separate topology integrity rule can be designed and applied.”