Encyclopedia of Big Data

Living Edition
| Editors: Laurie A. Schintler, Connie L. McNeely

Geography

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32001-4_102-1

Geography as a discipline is concerned with developing a greater understanding of processes that take place across the planet. While many geographers agree that big data presents opportunities to glean insights into our social and spatial world, and the processes that take place within it, many are also cautious about how it used and the impact it may have on how these worlds are analyzed and understood. Given that often big data are either explicitly or implicitly spatially or temporally referenced, this makes it particularly interesting for geographers. Geography, then, becomes part of the big data phenomenon.

As a term that has only relatively recently become commonly used, definitions of big data still vary. Rob Kitchin suggests there are in fact seven characteristics of big data, extending beyond the three Vs proffered by Doug Laney (volume, velocity, and variety) which are widely cited:
  1. 1.

    Volume: often terabytes and sometimes petabytes of information are being produced.

     
  2. 2.

    Velo...

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Further Readings

  1. Barnes, T. (2013). Big data, little history. Dialogues in Human Geography, 3(3), 297–302.CrossRefGoogle Scholar
  2. Batty, M. (2013). Big data, smart cities and city planning. Dialogues in Human Geography, 3(3), 274–278.CrossRefGoogle Scholar
  3. Gonzalez-Bailon, S. (2013). Big data and the fabric of human geography. Dialogues in Human Geography, 3(3), 292–296.CrossRefGoogle Scholar
  4. Goodchild, M. (2013). The quality of big (geo)data. Dialogues in Human Geography, 3(3), 280–284.CrossRefGoogle Scholar
  5. Kitchin, R. (2013). Big data and human geography: Opportunities, challenges and risks. Dialogues in Human Geography, 3(3), 262–267.CrossRefGoogle Scholar
  6. Kitchin, R. (2014). The data revolution: Big data open data, data infrastructures and their consequences. London: Sage.Google Scholar
  7. Li, L., Goodchild, M., & Xu, B. (2013). Spatial, temporal, and socioeconomic patterns in the use of twitter and Flickr. Cartography and Geographic Information Science, 40(2), 61–77.CrossRefGoogle Scholar
  8. Laney, D. (2001). 3D data management: controlling data volume, velocity, and variety. Available from: http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3DData-Management-Controlling-Data-Volume-Velocityand-Variety.pdf Accessed 18 Nov 2014.
  9. Shelton, T., Poorthuis, A., Graham, M., & Zook, M. (2014). Mapping the data shadows of hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’. Geoforum, 52(1), 167–179.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Business in SocietyCoventry UniversityCoventryUK