The impact of crowdsourcing on spatial data quality indicators

Crowdsourced geospatial information has soared the last couple of years. For example the statistics of OpenStreetMap (OSM) show an accelerating growth (OSM 2010). At the same time, devices equipped with GPS became mainstream (e.g. iPhone), diminishing the threshold to participate in crowdsourced geospatial information projects. Together with the growth in volume, the usage of crowdsourced geospatial information grew extensively as well. For example OSM maps are used in different commercial projects as background maps. This increased usage makes it important to identify quality indicators for crowdsourced geospatial information (Haklay and Weber 2008, Goodchild 2007, Flanagin and Metzger 2008) in order to: 1. compare and integrate crowdsourced data with institutional data (from e.g. National Mapping and Cadastral Agencies) and commercial data (e.g. TeleAtlas and NAVTEQ); 2. determine fitness for the intended purpose; 3. predict the quality developments for certain areas. In this abstract, we introduce the concept ‘Crowd Quality’ (CQ) to describe and quantify the quality of crowdsourced geospatial information.