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.
%0 Conference Paper
%1 hristova2012mapping
%A Hristova, Desislava
%A Mashhadi, Afra
%A Quattrone, Giovanni
%A Capra, Licia
%B Proc. When the City Meets the Citizen Workshop (WCMCW)
%D 2012
%K community computer crowdsourcing sensing social urban
%T Mapping Community Engagement with Urban Crowd-Sourcing
%U http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf
%X 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,
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. },
added-at = {2012-04-26T11:35:36.000+0200},
author = {Hristova, Desislava and Mashhadi, Afra and Quattrone, Giovanni and Capra, Licia},
biburl = {https://www.bibsonomy.org/bibtex/2f0a69ac56b94a471b470ebd56545fafd/jaeschke},
booktitle = {Proc. When the City Meets the Citizen Workshop (WCMCW)},
interhash = {373e02fe56d30b26261a33135e0b7a45},
intrahash = {f0a69ac56b94a471b470ebd56545fafd},
keywords = {community computer crowdsourcing sensing social urban},
month = jun,
timestamp = {2014-07-28T15:57:31.000+0200},
title = {Mapping Community Engagement with Urban Crowd-Sourcing},
url = {http://www.cs.ucl.ac.uk/staff/l.capra/publications/wcmcw12.pdf},
year = 2012
}