## 4 Results
### Temporal Dataset Quality  
The timeliness of the collected information plays a major role in the quality assessment of a geodataset such as OSM. Each object in the OSM database is related to a unique timestamp, which represents the time at which the object was edited the last time. Additionally each edited object has a version-number indicating how many times the object has been changed since its first creation. By utilizing this information and combining it with the OSM history dump file, it is possible to determine when an object has been created and when and how often it has been edited.  
Figure 6 shows the collected temporal accuracy information for all 12 analyzed areas based on the OSM history dump file dated 19 October 2012. The first three Nodes, Ways, and Relations bars of each diagram represent the distribution of the currency of the objects based on the timestamp information of the entire dataset. The larger the dark green area of the bar, the more up-to-date is the particular dataset. The first three bars are followed by a second set of Nodes v1, Ways v1 and Relations v1 bars for each tested area. The information provided in these bars shows when an object, _i.e.,_ the first version of the object, was created. Therefore, it is possible to determine for how long and how much data has been contributed to each area over the past few years. The comparison of the three upper and three lower bars provide detailed information if a dataset has been updated by the OSM community. If the bars show a similar pattern to each other e.g. Nodes and Nodes v1, it would indicate that the data has not been updated since its creation.  
Figure 5: Density of nodes, ways & relations per km\({}^{2}\) (Oct. 2012).  
The comparison of the diagrams for Berlin shows that the currency information is similar to the objects labeled as first version. Moscow, Paris and Buenos Aires show similar patterns. The aforementioned data imports for France are also represented in the diagrams for Paris, with a strong concentration of data contributions in 2010. For London, the results revealed that the majority of Relations were created before or in 2009, yet the timestamp of most Relations is dated within 2010. The strong increase in Relations in the dataset in 2009 can again be attributed to a data import. However, the diagram also shows that after the import the community has been updating the information in 2010, thus most Relations show a 2010 timestamp. Sydney proves to be one of the most up-to-date datasets in OSM. One possible reason for this pattern could be the OSM license change in 2012. Due to the license change, all data that was contributed by members that explicitly did not agree with the new license was deleted from the database, including all data that was imported from sources whose license was no longer compatible with the new OSM license. After the deletion of the data it was partially recollected by the local members that agreed to the new license. Cairo, Johannesburg, Seoul and Los Angeles show less up-to-date datasets in comparison to the other analyzed areas. The temporal OSM dataset quality is better in areas with a stronger community activity. The correlation between the number of contributors in an area and the number of newly created objects in 2012 proof to be high for Nodes (R\({}_{\rm S}\) 0.755), Ways (R\({}_{\rm S}\) 0.720) and Relations (R\({}_{\rm S}\) 0.832). With regards to the  
Figure 6: Distribution of currency and data versions per urban area (Oct. 2012).  
contributions by the individual mapper groups the numbers showed that on average 94.6% of all Nodes, 92.8% of all Ways and 84.8% of all Relations were collected by Senior Mappers.