Abstract
Abstract. Crowd sourcing geographic data is an opensource geographic data which is contributed by lots of non-professionals and provided to the public. The typical crowd sourcing geographic data contains GPS track data like OpenStreetMap, collaborative map data like Wikimapia, social websites like Twitter and Facebook, POI signed by Jiepang user and so on. These data will provide canonical geographic information for pubic after treatment. As compared with conventional geographic data collection and update method, the crowd sourcing geographic data from the non-professional has characteristics or advantages of large data volume, high currency, abundance information and low cost and becomes a research hotspot of international geographic information science in the recent years. Large volume crowd sourcing geographic data with high currency provides a new solution for geospatial database updating while it need to solve the quality problem of crowd sourcing geographic data obtained from the non-professionals. In this paper, a quality analysis model for OpenStreetMap crowd sourcing geographic data is proposed. Firstly, a quality analysis framework is designed based on data characteristic analysis of OSM data. Secondly, a quality assessment model for OSM data by three different quality elements: completeness, thematic accuracy and positional accuracy is presented. Finally, take the OSM data of Wuhan for instance, the paper analyses and assesses the quality of OSM data with 2011 version of navigation map for reference. The result shows that the high-level roads and urban traffic network of OSM data has a high positional accuracy and completeness so that these OSM data can be used for updating of urban road network database.
Highlights
Crowd sourcing geographic data is an opensource geographic data which is contributed by lots of non-professionals and provided to the public (Giles, 2006; Heipke, 2010; Howe, 2008)
The simple and valid method is to compare OSM with reference data to analyse and assess the quality of OSM based on quality assessment model built with proper quality elements
The OSM data derives from the OpenStreetMap website and the geographic datum used in OpenStreetMap is the WGS-84/long datum while the reference data is the 2011 version of navigation map with position accuracy of 4 meters from NavInfo
Summary
Crowd sourcing geographic data is an opensource geographic data which is contributed by lots of non-professionals and provided to the public (Giles, 2006; Heipke, 2010; Howe, 2008). While discussing the processing and application method, the primary problem is to analyse the quality of crowd sourcing geographic data (Goodchild, 2007). As there have been lots of problems such as information redundancy, devoid of information in corwd sourcing geographic data, it is necessary to built the quality analysis model, assessment method or system of corwd sourcing geographic data before applying them. To solve the quality problem of crowd sourcing geographic data obtained from the non-professionals, a quality analysis model for OpenStreetMap crowd sourcing geographic data is proposed in this paper. Take the OSM data of Wuhan for instance, the paper analyses and assesses the quality of OSM data with 2011 version of navigation map for reference
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