In our rapidly-changing world, information has never been more strategic to decision making. The new information landscape is associated with social media, high-speed networks and distributed information sharing from people all around the world. Smartphones with built-in GPS and survey capabilities enable anyone to create open source geospatial datasets, transforming the way in which information is produced.
Thousands of people already collect geospatial data, as in the case of the OpenStreetMap (OSM) initiative where volunteers contribute to the creation of a global map. This collective approach, defined as crowdsourcing, might raise concerns over the quality of the information collected.
To address such concerns, it is useful to define quality in terms of currency, precision and completeness. Conventional mapping is updated at periodic intervals while the OSM depends on users’ intervention. The latter can confer a significant advantage where, thanks to their local expertise, users are capable of detecting and recording change almost in real time.
The same advantage is also valid in the case of precision although, again, OSM can be more variable than conventional mapping depending on user input. Finally, in terms of completeness, OSM has contributed significantly in placing areas of the developing world finally on the map.
One can also note the crowdsourcing community’s rapid and effective response to specific emergency events such as the 2010 Haiti earthquake where Non-Governmental Organisations and volunteers collaborated in generating real-time crisis mapping. Such work continues to this day wherever disaster strikes. With regard to reliability, OSM ultimately benefits from a vast user community that not only contributes to the creation of the map but also uses it on a daily basis.
Authoritative data such as those produced by National Mapping Agencies (NMAs) are tried- and-tested and integrate well with applications and systems developed over the years by third parties. However, NMAs are under pressure to trim their budgets and find new cost-saving ways of delivering information to the same standard but with fewer resources. This ultimately impacts the collection of geospatial data and limits its currency and completeness.