# Exploring Data Model Relations in OpenStreetMap
## 4 Experimental Analysis
### Relation Analysis
#### Edit Frequency of Relations  
The next aspect of relations we investigated was their edit timestamp. Using the timestamp attribute extracted for each relation, we can obtain an insight into how recently relations have been edited or changed. For each of our four cities, we calculated the Age of each relation by calculating the number of days between the timestamp of each relation and the date upon which we downloaded the OSM data from GeoFabrik (30 June 2017). Figure 4 provides the distribution of the Age of all relations in each city where the bin size is 180 days which is approximately 6 months. Both Zurich and Vienna show a large number of relations which have been edited in some way in the past 180 days.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**City** & **Multipolygon** & **Restriction** & **Route** & **Public Transport** & **Route Master** \\ \hline _Madrid 1_ & building & restriction & route & public\_transport & network \\ _Madrid 2_ & addr:street & except & network & operator & route\_master \\ _Rome 1_ & building & restriction & route & public\_transport & route\_master \\ _Rome 2_ & landuse & except & operator & operator & operator \\ _Vienna 1_ & building & restriction & route & public\_transport & route\_master \\ _Vienna 2_ & addr:street & except & network & operator & network \\ _Zurich 1_ & building & restriction & route & operator & operator \\ _Zurich 2_ & landuse & except & operator & public\_transport & network \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the most popular tag key combinations with the five most frequently occuring relation types in the four cities.  
Figure 3: Distribution of the number of tags assigned to every relation in all four cities.  
The distributions for Rome and Madrid are very different to Zurich and Vienna. The majority of relations in Madrid have been edited in some fashion over the previous four years. The distribution of Age in Rome appears to indicate that there are three distinct time periods for editing of relations: within the last 180 days, between 2 and 3 years ago, and also between 5 and 6 years. We conducted a review of the change sets for Rome during this time and found that relations with ages 540 days to 720 days (around 2 to 3 years ago) there was over 2000 relation objects edited with over 70% of these relations having type = multipolygon. A similar situation occurs during for relations with ages 720 days to 900 days with over 1100 type = multipolygon relations edited. Both situations could be the result of a bulk import of data to OSM or a large-scale organized mapping activity in the form of mapping parties[37; 38]. A more detailed investigation of the editing patterns of relations in Rome is required in order to understand the significance of these periods. In the case of Vienna, an investigation of change set activity revealed that in the age period 1620 days to 1800 there were 1570 relation objects edited, 90% of which are type = multipolygon. Two users are responsible for over 50% of these edits. This potential bulk import explains the spike in the Vienna distribution during this age period.  
Another aspect of the edit frequency of relations is contained in the edit version number attribute. Each time an object is edited and submitted to the OSM database, the version number attribute of that objected is incremented by one. Several authors [5; 8; 39; 40] have considered the version number of objects in OSM as an important attribute in relation to data quality, contributor pattern analysis and general understanding of OSM mapping. In Figure 5, we provide the distribution of edit version number of all relations within the four cities. We see a very similiar distribution with all cities containing a very large percentage of relations with between 1 and 10 versions.  
Figure 4: Distribution of calculated age, in days, of each relation in all four cities.