## 3 Current Developments
### Data Quality Analysis
#### 3.1.1 Road Network Evaluation
The researchers concluded that the integration of OSM into the official dataset caused several issues from the geometrical matching perspective. Major differences can be accredited to the varying data collection procedures in OSM. In their test areas, some of the data was remotely mapped by contributors from different countries with little to no local knowledge. [PERSON] and [PERSON] [90] presented an algorithm that allowed the mining of land-use patterns from the OSM street network. This was the first approach that actively enhanced the existing or generated a new dataset based on the collected VGI data. Additionally, [PERSON] _et al_. [91] presented a spatial statistical method to compute the positional accuracy of road junctions by extracting and comparing these particular features in OSM to a proprietary dataset. The temporal development of the OSM dataset in Germany was analyzed in a comprehensive study for the years 2007 to 2011 [21]. The results showed that the total difference between the OSM street network for motorized traffic and a comparable proprietary dataset, _i.e._, TomTom, was only 9%, indicating missing data in OSM. However, when considering the entire German OSM street network, including pedestrian paths and small trails, the VGI data source exceeded the proprietary information by 27%. The same study and [PERSON] and [PERSON] [92] revealed that other important information for navigation purposes, such as turn restrictions, included in most proprietary datasets, are oftentimes missing in the OSM dataset. In 2011, [PERSON] and [PERSON] [93] conducted one of the first OSM studies outside of Europe. Based on similar methods introduced in prior studies [19], they compared the OSM dataset with proprietary data from TomTom and Navteq for the entire state of Florida (USA). In contrast to prior findings in Europe, the results of this study showed an opposite trend with stronger coverage in OSM for rural areas in Florida, whereas urban areas showed better coverage in TomTom and Navteq. However, the researchers accredited this pattern to the U.S. Census TIGER/Line street data import for OSM in 2008/2009. Data imports are a highly discussed topic within the OSM community, with strong opinions for and against the import of license conform datasets. [PERSON] _et al_. [94] analyzed the import of the TIGER/Line dataset for the entire U.S. in more detail and summarized that the community is not focusing on improving the imported dataset. This statement could be made for rural, as well as urban areas. Instead, the OSM community rather focuses on adding more detailed information, such as pedestrian trails, after a data import of all major roads took place. More detailed analyses for the OSM US dataset were conducted with the focus on the road networks for motorized and non-motorized traffic [95, 96]. The analyses included computations of pedestrian routes for different data sources in selected cities in Europe and the U.S., as well as the assessment of pedestrian accessibility to transit stations. Based on the total length comparisons of the generated routes, errors of commission and omission were identified in the datasets. Due to the dense coverage of pedestrian data in German cities, better results were found for European cities in comparison to U.S. cities [95, 96]. In a different study for the U.S., the development of cycling-related features in OSM and Google were investigated and compared. Results revealed a high heterogeneity with regards to completeness between analyzed cities and showed that off-road trails were more completely mapped than on-street bicycle lanes [97].