Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Crowdsourcing Geographic Information Systems

  • Dieter Pfoser
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80607

Synonyms

User-generated geospatial content; Volunteered geographic information (VGI)

Definition

The crowdsourcing of geographic information addresses the collection of geospatial data contributed by non-expert users and the aggregation of these data into meaningful geospatial datasets. While crowdsourcing generally implies a coordinated bottom-up grassroots effort to contribute information, in the context of geospatial data, the term volunteered geographic information (VGI) specifically refers to a dedicated collection effort inviting non-expert users to contribute. A prominent example here is the OpenStreetMap effort focusing on map datasets. Crowdsourcing geospatial data is an evolving research area that covers efforts ranging from mining GPS tracking data to using social media content to profile population dynamics.

Historical Background

With the proliferation of the Internet as the primary medium for data publishing and information exchange, we have seen an explosion in the amount...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Adams B, McKenzie G. Inferring thematic places from spatially referenced natural language descriptions. In: Sui D, Elwood S, Goodchild M, Crowdsourcing Geographic Knowledge: Volunteered Geographic Information, editors, Crowdsourcing geographic knowledge: Volunteered Geographic Information (VGI) in theory and practice. Netherlands: Springer; 2013, p. 201--21.Google Scholar
  2. 2.
    Agarwal S, Snavely N, Simon I, Seitz SM, Szeliski R. Building rome in a day. In: Proceedings of the 12th IEEE Computer Vision Conference; 2009. p. 72–9.Google Scholar
  3. 3.
    Ahmed M, Karagiorgou S, Pfoser D, Wenk C. Map construction algorithms, Springer International Publishing, Switzerland, 2015.zbMATHCrossRefGoogle Scholar
  4. 4.
    Cope A. The shape of alpha. Available at http://code.flickr.net/2008/10/30/the-shape-of-alpha/, 2008.
  5. 5.
    Crooks AT, Pfoser D, Jenkins A, Croitoru A, Karagiorgou S, Efentakis A, Lamprianidis G, Smith D, Stefanidis A. Crowdsourcing urban form and function. Int J Geogr Inf Sci. 2015;29(5):720–41.CrossRefGoogle Scholar
  6. 6.
    Goodchild MF. Citizens as sensors: the world of volunteered geography. GeoJournal. 2007;69(4):211–21.CrossRefGoogle Scholar
  7. 7.
    Haklay M. How good is volunteered geographical information? A comparative study of OpenStreetMap and ordnance survey datasets. Environ Plan B. Plan & Design. 2010;37(4):682–703.CrossRefGoogle Scholar
  8. 8.
    Hecht B, Gergle D. On the localness of user-generated content. In: Proceedings of the ACM Computer Supported Cooperative Work Conference; 2010, p. 229–32.Google Scholar
  9. 9.
    Intagorn S, Plangprasopchok A, Lerman K. Harvesting geospatial knowledge from social metadata. In: Proceedings of the 7th ISCRAM Conference; 2010.Google Scholar
  10. 10.
    Kling F, Pozdnoukhov A. When a city tells a story: urban topic analysis. In: Proceedings of the ACM SIGSPATIAL GIS Conference; 2012. p. 482–85.Google Scholar
  11. 11.
    Lamprianidis G, Pfoser D. Collaborative geospatial feature search. In: Proceedings of the ACM SIGSPATIAL GIS Conference; 2012. p. 169–78.Google Scholar
  12. 12.
    Pfoser D, Brakatsoulas S, Brosch P, Umlauft M, Tsironis G, Tryfona N. Dynamic travel time provision for road networks. In: Proceedings of the ACM SIGSPATIAL GIS confernce; 2008. p. 475–78.Google Scholar
  13. 13.
    Pfoser D. On user-generated geocontent. Proceeding of 12th SSTD Symposium In: Proceedings of the 12th SSTD Symposium; 2011. p. 458–61.CrossRefGoogle Scholar
  14. 14.
    Skoumas G, Pfoser D, Kyrillidis A, Sellis T. Location estimation using crowdsourced spatial relations. ACM Trans Spat Algorithms Syst. 2016;2(2):1–23.CrossRefGoogle Scholar
  15. 15.
    Stefanidis T, Crooks AT, Radzikowski J. Harvesting ambient geospatial information from social media feeds. GeoJournal. 2013;78(2):319–38.CrossRefGoogle Scholar
  16. 16.
    Zhang Z, Zhang C, Du C, Zhu S. SVM-based extraction of spatial relations in text. In: Proceedings of the IEEE ICSDM Conferncence; 2011. p. 529–533.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Geography and Geoinformation ScienceGeorge Mason UniversityFairfaxUSA

Section editors and affiliations

  • Ralf Hartmut Güting
    • 1
  1. 1.Computer ScienceUniversity of HagenHagenGermany