Giovanni Quattrone

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Giovanni Quattrone is an author.

Publications

Only 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
Temporal analysis of activity patterns of editors in collaborative mapping project of openstreetmap Circadian pattern
Eigenbehaviour
Geo-wiki
Mass collaboration
Openstreetmap
Principal component analysis
Wikipedia
Proceedings of the 9th International Symposium on Open Collaboration, WikiSym + OpenSym 2013 English 2013 In the recent yearsWikis have become an attractive platform for social studies of the human behaviour. Containing mil- lions records of edits across the globe, collaborative systems such as Wikipedia have allowed researchers to gain a bet- Ter understanding of editors participation and their activity patterns. However, contributions made to Geo-wikis wiki- based collaborative mapping projects dier from systems such as Wikipedia in a fundamental way due to spatial di- mension of the content that limits the contributors to a set of those who posses local knowledge about a specic area and therefore cross-platform studies and comparisons are required to build a comprehensive image of online open col- laboration phenomena. In this work, we study the temporal behavioural pattern of OpenStreetMap editors, a successful example of geo-wiki, for two European capital cities. We categorise dierent type of temporal patterns and report on the historical trend within a period of 7 years of the project age. We also draw a comparison with the previously ob- served editing activity patterns of Wikipedia. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications Spatial Databases and GIS; H.5.3 [Group and Orga- nization Interfaces]: Collaborative computing, computer- supported cooperative work General Terms Human Factors, Measurement. Copyright 2010 ACM. 0 0
On the Accuracy of Urban Crowd-Sourcing for Maintaining Large-Scale Geospatial Databases Human Factors
Measurement
Reliability
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