a8c862b5_WikiSym___WikiPapers__Abstract
[Title] On the Accuracy of Urban Crowd-Sourcing for Maintaining Large-Scale Geospatial Databases [Author(s)] Afra Mashhadi Giovanni Quattrone Licia Capra Peter Mooney [Keyword(s)] Human Factors Measurement Reliability [Language] English August 2012 [R] 17 [C] 1 [Abstract] 
The world is in the midst of an immense population shift from rural areas to cities. Urban elements, such as businesses, Points-of-Interest (POIs), transportation, and housing are continuously changing, and collecting and maintaining accurate information about these elements within spatial databases has become an incredibly onerous task. A solution made possible by the uptake of social media is crowd-sourcing, where user-generated content can be cultivated into meaningful and informative collections, as exemplified by sites like Wikipedia. This form of user-contributed content is no longer confined to the Web: equipped with powerful mobile devices, citizens have become cartographers too, volunteering geographic information (e.g., POIs) as exemplified by sites like OpenStreetMap. In this paper, we investigate the extent to which crowd-sourcing can be relied upon to build and maintain an accurate map of the changing world, by means of a thorough analysis and comparison between traditional web-based crowd-sourcing (as in Wikipedia) and urban crowd-sourcing (as in OpenStreetMap).
WikiSym - WikiPapers
Abstract