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Considering the time period of this study (six years) it would be 
expected that low urban density areas would have more 
contributions. The line features which have been mapped 
mostly represent the main roads running through the area. These 
contributions have most likely been made not by the residents 
but by people passing through. 
What should however be taken into consideration is the fact that 
there is generally not many features to map in these areas, thus 
high data volumes cannot be expected. Although the residents 
could provide valuable feature information, the likelihood of 
this is slim due to the lack of resources. 
The contributions to point features in commercial areas appear 
to still be increasing while the volume of line feature data have 
had a much higher mapping rate but have stabilised since 2010. 
One of the main drivers of a mapping initiative like this is the 
availability of resources. There exists a digital divide between 
urban and rural areas. Williams (2001 as cited in Genovese & 
Roche 2009) describes this as the “gap between people with 
adequate access to digital information and technology versus 
those with very limited or no access at all”. 
User motivations play a big role in volunteered mapping. Some 
of these motivations include: “building professional networks", 
"strengthening social relationships" (Shekhar 2010) and 
benefiting others (Coleman et al. 2009). The motivation of an 
interest group will vary with geographic location and therefore 
the type of data contributed will vary for different areas. This is 
seen in the comparison of amenity contributions between 
commercial and residential areas. 
The influx of tourists into an area does have an influence on the 
number of contributions as can be seen in figure 6, where Cape 
Town had a surge of contributions leading up to and during the 
2010 FIFA Soccer World Cup period. The appreciation that 
tourists have for a location may have motivated South African 
citizens to contribute data. On the other hand the tourists 
themselves could be responsible for the increase in 
contributions made. 
6. CONCLUSION 
Haklay & Ellul (2010) conclude that the quality of VGI will 
vary with the different communities. This study has shown that 
the rate of mapping and the content of volunteer mapping also 
vary for different communities . The implication of this is that 
National Mapping Agencies cannot adopt one standard 
integration process across the country. VGI is a valuable data 
source for National Mapping Agencies, thus it would worth 
developing an integration model that is location specific. How 
National Mapping Agencies respond to this will have to be 
addressed in other investigations. 
Unlike other forms of information, presently VGI is unlikely to 
be of a quality as good as National Mapping Agency data. This 
is not to mean that it is not useful, but rather that our 
expectations of the data need to be shifted and pragmatic 
application sought. For example rather than trying to assimilate 
VGI it can instead be used as a tool to flag blunders in data 
produced by National Mapping Agencies. 
7. Acknowledgements 
The authors would like to acknowledge Grant Slater, Muki 
Haklay from the Department of Civil, Environmental Geomatic 
Engineering, University of London and Frederick Ramm, 
managing director of Geofabrik GmbH, Karlsruhe, Germany for 
providing the OSM test data and information regarding the 
OSM data. 
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