Hristova, D.; Mashhadi, A.; Quattrone, G. & Capra, L.: Mapping Community Engagement with Urban Crowd-Sourcing. Proc. When the City Meets the Citizen Workshop (WCMCW). 2012
[Volltext]
Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort.
@inproceedings{hristova2012mapping,
author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
title = {Mapping Community Engagement with Urban Crowd-Sourcing},
booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
year = {2012},
url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
keywords = {community, computer, crowdsourcing, sensing, social, urban},
abstract = {Communities of people are better mappers if they are spatially clustered, as revealed in an interesting new paper by Hristova, Mashhadi, Quattrone and Capra from UCL. "This preliminary analysis inspires further inquiry because it shows a clear correlation between spatial affiliation, the internal community structure and the community’s engagement in terms of coverage", according to the authors. They have studied the similarity patterns among eight hundred contributors to OpenStreetMap, the well-known crowdmapping project and detected the hidden community structure. It is a very promising field of research, coupling a social network analysis of crowdsourced data. Participants to such projects are rarely independent individuals: in most cases, they involve communities more than single participants and it would be crucial to uncover how the underlying social structure reflects on the quantity and the quality of the collected data. It has the greatest relevance for citizen science projects, as data quality is often the key issue determining the success or the failure of the collective effort. }
}