| Licia Capra|
(Alternative names for this author)
|Co-authors||Afra Mashhadi, Giovanni Quattrone, Hristova D., Peter Mooney|
|Authorship||Publications (2), datasets (0), tools (0)|
|Citations||Total (1), average (0.5), median (0.5), max (1), min (0)|
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Licia Capra is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|On the Accuracy of Urban Crowd-Sourcing for Maintaining Large-Scale Geospatial Databases||Human Factors
|WikiSym||English||August 2012||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).||17||1|
|Mapping community engagement with urban crowd-sourcing||AAAI Workshop - Technical Report||English||2012||Cities are highly dynamic entities, with urban elements such as businesses, cultural and social Points-of-Interests (POIs), housing, transportation and the like, continuously changing. In order to maintain accurate spatial information in these settings, crowd-sourcing models of data collection, such as in OpenStreetMap (OSM), have come under investigation. Like many crowd-sourcing platforms (e.g., Wikipedia), these geowikis exhibit tailing-off activity, bringing into question their long-term viability. In this paper, we begin an investigation into the sustainability of urban crowd-sourcing, by studying the network structure and geographical mapping of implicit communities of contributors in OSM. We observe that spatially clustered crowd-sourcing communities produce higher coverage than those with looser geographic affinity. We discuss the positive implications that this has on the future of urban crowd-sourcing. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.||0||0|