research-article

On the accuracy of urban crowd-sourcing for maintaining large-scale geospatial databases

Authors:

Giovanni Quattrone,

Peter MooneyAuthors Info & Claims

WikiSym '12: Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration

Article No.: 15, Pages 1 - 10

https://doi.org/10.1145/2462932.2462952

Published: 27 August 2012 Publication History

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).

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On the accuracy of urban crowd-sourcing for maintaining large-scale geospatial databases

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cover image ACM Conferences

WikiSym '12: Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration

August 2012

295 pages

ISBN:9781450316057

DOI:10.1145/2462932

General Chair:
Cliff Lampe
University of Michigan, Ann Arbor, MI

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
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Published: 27 August 2012

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WikiSym '12: The 8th International Symposium on Wikis

August 27 - 29, 2012

Linz, Austria

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WikiSym '12 Paper Acceptance Rate 21 of 37 submissions, 57%;

Overall Acceptance Rate 69 of 145 submissions, 48%

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Jilani MBertolotto MCorcoran PAlghanim A(2019)Traditional vs. Machine-Learning Techniques for OSM Quality AssessmentGeospatial Intelligence10.4018/978-1-5225-8054-6.ch022(469-487)Online publication date: 2019
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Jilani MBertolotto MCorcoran PAlghanim A(2017)Traditional vs. Machine-Learning Techniques for OSM Quality AssessmentVolunteered Geographic Information and the Future of Geospatial Data10.4018/978-1-5225-2446-5.ch003(47-64)Online publication date: 2017
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