## 2 OpenStreetMap Quality Assessment History
### Study Area and Data Preparation  
A long history in geodata quality research has provided a variety of publications dealing with the definition of characteristics of geodata that can be used as quality parameters [39, 40]. In 2002 the International Organization of Standardization (ISO) set a standard that defines the quality attributes of geodata in ISO 19113:2002 (principles for describing the quality of geographic data) and ISO 19114:2003 (framework for procedures for determining and evaluating quality). The defined parameters for the quality of geodata according to ISO 19113:2002 are completeness, logical consistency, positional accuracy, temporal accuracy, and thematic accuracy.  
Within the scope of this article, we shall consider all these parameters with the exceptions of positional and thematic accuracy. The completeness of the street network is determined via a relative comparison between OSM and a commercial dataset provider. Furthermore, we will show the development in the urban and rural areas over a certain time period. The logical consistency will subsequently be evaluated with the help of an internal test, whereby topological and thematic consistency will be determined. The temporal accuracy will be verified in a simple form by means of an object's time stamp in the OSM dataset.  
The study area for this article relates to all of Germany; however, each of the studies conducted takes place on different scales with the smallest scale being the municipal level. A variety of OSM datasets with three-month intervals were used starting from January 2007 to June 2011. Overall, 19 datasets were prepared for Germany, representing the different dates. In addition to clipping the Germany dataset from the entire OSM database dump-file ([[http://planet.openstreetmap.org/](http://planet.openstreetmap.org/)]([http://planet.openstreetmap.org/](http://planet.openstreetmap.org/))) for each point in time, it proved to be a challenge to work with the different API (application programming interface) versions to extract the data. The first dataset included in the analysis (2007) was taken from OSM API Version 0.4; however; Version 0.6 is the latest version (2011) of the API. The data could be converted, but unfortunately it did not always feature all of the latest attributes. With the older version of the API ways would still consist of segments in the OSM database, while with API 0.5 ways were mapped by referencing to a node. Furthermore, since API 0.6 appeared in April 2009, anonymous edits in OSM are no longer permitted, and a user ID and user name have since been included with every object. This means that at certain points during our analysis it was not possible to retrieve information for the entire time frame up to 2007, but instead only to 2009. For the comparison analyses, the TomTom Multinet 2011 commercial dataset has been used. The respective data was imported into a PostgreSQL/PostGIS database with OSM's OSMOSIS program. In contrast to other available OSM programs that import the data into a database, the application used has the advantage of not filtering, preparing, or optimizing the data during the import procedure to the database. All analysis procedures in this paper are either based on PostgreSQL/PostGIS functions or specific tools developed in Java implementing the GeoTools open source toolkit.