Deletions 
Deletions to commercial areas are generally low when 
compared to the amount of additions made between epochs. In 
Figure 7a Durban had significantly high deletions between late 
2009 and early 2010 (epoch 6-7). In figure 8a it can be seen that 
during this time period the 2008 data set was modified a lot, as 
if the entire data set had been shifting in position in 2009. This 
explains the increase in additions for this period in figure 6a. In 
Figure 7b deletions to Residential areas are lower than 
commercial areas, but this can be expected as the total 
contributions in residential areas are lower than commercial 
areas. The suburb Universitas also had high deletion values 
between early 2008 and late 2010 (epoch 6-8). In figure 8b it 
shows that as with Durban, many modifications were made 
during this period, accounting for the high deletion values. 
Brackenfell had high deletion values in 2011 (epoch 9). When 
examining the data sets, it was noted that an administrative 
boundary was removed from the early 2011 data set. 
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a) b) 
Figure 8 Difference in position of line data between two 
datasets: a) Durban — epoch 6-7 and b) Universitas — epoch 6-8 
a) b) 
Figure 9 Low urban density area: a) additions and b) deletions 
Figure 9 represents the additions and deletions for the low urban 
density category. The line colours represent the low urban 
density areas as follows: red-Gondeni, green-Makhwezini and 
purple-Stutterheim. Additions (figure 9a) for low urban density 
areas were very low when compared to commercial and 
residential areas. Gondeni had one small addition between late 
2010 and early 2011 (epoch 8-9). Makhwezini had two slightly 
larger additions between late 2009 and late 2010 (epoch 6-8). 
Stutterheim had one significant increase, when compared to 
Godeni and Makhwezini, in 2011. Because additions for low 
urban density areas are low, the deletions are expected to be 
even lower (figure 9b). Makhwezini and Stutterheim (as with 
Durban (figure 7a), Brackenfell and Universitas (figure 7b), 
experienced significant modifications between late 2010 and 
early 2011 (epoch 8-9) which accounts for relatively high 
deletion values. 
5. DISCUSSION 
5.1 Activity 
Most line features represented in the test data are either roads or 
railway lines, Road classes range from national routes to 
footways. Single point features are used to represent amenities, 
e.g. banks, shops, hospitals etc., 
Quantity 
OpenStreetMap is first a repository for road data, thus the 
quantity of line features far exceeds the point features. But it 
can be expected that with the passage time the acquisition of 
point features will outstrip that of linear features as these point 
features are the most likely to change with time. 
Quality 
In earlier years, there were much more modifications to line 
features than in later years. This indicates that the base data is 
becoming more stable over time. 
a) b) 
Figure 10 a)Two datasets from 2011 display minor differences. 
b) The same area compared in 2008 displays many more 
modifications. 
The structure of the OSM database allows for proper 
classification for both points and lines. This is however not 
enforced on the user and as a result most of the features are not 
classified, but only exists on a general field within the database. 
The result is variation in attribute information for the same 
features. 
5.2 Densification 
Rate of mapping 
From the results, it becomes clear that the rate of mapping is 
strongly correlated to the geographical location of an area. 
Highly populated urban and commercial areas experience 
greater contributions than towns. This is understandable as such 
places will contain more people with a culture of sharing 
information. 
Points 
The rate of mapping is very different for the three test arca 
categories, from a steady increase for commercial areas, to a 
much lower rate for residential areas, to no data for low urban 
areas. The contributions to point features in commercial areas 
appear to still be increasing. 
Lines 
In cities and high urban density areas, the quantity of data 
contributed to OpenStreetMap increased dramatically since 
2006. It does however appear that since 2010 very few 
contributions have been made in these areas. Low urban density 
areas continue to have a low contribution rate. 
5.3 Global vs Local variations 
Commercial areas had the highest mapping rate for the 2010- 
2011 time interval. Residential arcas have not had a steady 
increase in contributions, thus there is no common time period 
that can be said to have had the most mapping activity. 
Commercial areas have had the most additions of line features 
for the 2007-2009 time intervals. Residential and low urban 
density areas had the most contributions in 2010-2011. 
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