Defining Local Experts: Geographical Expertise as a Basis for Geographic Information Quality

Authors Colin Robertson, Rob Feick



PDF
Thumbnail PDF

File

LIPIcs.COSIT.2017.22.pdf
  • Filesize: 10.4 MB
  • 14 pages

Document Identifiers

Author Details

Colin Robertson
Rob Feick

Cite AsGet BibTex

Colin Robertson and Rob Feick. Defining Local Experts: Geographical Expertise as a Basis for Geographic Information Quality. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 22:1-22:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.COSIT.2017.22

Abstract

As more data are produced by location sensors, mobile devices, and online participatory processes, the field of GIScience has grappled with issues of information quality, context, and appropriate analytical approaches for data with heterogeneous and/or unknown provenance. Data quality has often been viewed through a bifurcated lens of experts and amateurs, but consideration of what the nature of geographical expertise is reveals a much more more nuanced situation. We consider how adapting frameworks from the field of studies of experience and expertise may provide a conceptual basis and methodological framework for evaluating the quality of geographic information. For contributed geographic information, quality is typically derived from a data user’s trust in and/or perception of the reputation of the data producer. Trust and reputation of producers of geographic information has typically been derived from the presence or absence of professional qualifications and training. However this framework applies exclusively to ‘crisp’ notions of data quality, and has limited utility for more subjective contributions associated with volunteered geographic information which may provide a rich source of geographic information for many applications. We hypothesize that a conceptual framework for geographical expertise may be used as the basis for assessing information quality in both formal and informal sources of geospatial data. Two case studies are used to highlight the new concepts of geographical expertise introduced in the paper.
Keywords
  • data quality
  • expertise
  • geographic information
  • conceptual framework

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. M. Bishr and W. Kuhn. Trust and reputation models for quality assessment of human sensor observations. In International Conference on Spatial Information Theory, pages 53-73. Springer, 2013. Google Scholar
  2. Rick Bonney, Jennifer L. Shirk, Tina B. Phillips, Andrea Wiggins, Heidi L. Ballard, Abraham J. Miller-Rushing, and Julia K. Parrish. Next Steps for Citizen Science. Science, 343(6178):1436-1437, March 2014. URL: http://dx.doi.org/10.1126/science.1251554.
  3. Z. Cheng, J. Caverlee, H. Barthwal, and V. Bachani. Finding Local Experts on Twitter. In Proceedings of the 23rd International Conference on World Wide Web, WWW '14 Companion, pages 241-242, New York, NY, USA, 2014. ACM. Google Scholar
  4. Z. Cheng, J. Caverlee, H. Barthwal, and V. Bachani. Who is the Barbecue King of Texas?: A Geo-spatial Approach to Finding Local Experts on Twitter. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, SIGIR '14, pages 335-344, New York, NY, USA, 2014. ACM. Google Scholar
  5. N. Chrisman. Part 2: Issues and Problems Relating to Cartographic Data Use, Exchange and Transfer: The Role Of Quality Information In The Long-Term Functioning Of A Geographic Information System. Cartographica: The International Journal for Geographic Information and Geovisualization, 21(2-3):79-88, July 1984. Google Scholar
  6. N. Chrisman. Development in the treatment of spatial data quality. Fundamentals of Spatial Data Quality, pages 21-30, 2006. Google Scholar
  7. J. Cinnamon. Deconstructing the binaries of spatial data production: Towards hybridity. The Canadian Geographer / Le Géographe canadien, 59(1):35-51, March 2015. Google Scholar
  8. D. Coleman, Y. Gerogiadou, and J. Labonte. Volunteered Geographic Information: the nature and motivation of produsers. International Journal of Spatial Data Infrastructures Research, 4:332-358, 2009. Google Scholar
  9. H. Collins. Three dimensions of expertise. Phenomenology and the Cognitive Sciences, 12(2):253-273, April 2011. Google Scholar
  10. H. Collins, R. Evans, M. Weinel, J. Lyttleton-Smith, A. Bartlett, and M. Hall. The Imitation Game and the Nature of Mixed Methods. Journal of Mixed Methods Research, page 1558689815619824, December 2015. Google Scholar
  11. H. M. Collins and R. Evans. The Third Wave of Science Studies: Studies of Expertise and Experience. Social Studies of Science, 32(2):235-296, April 2002. Google Scholar
  12. R. Devillers and R. Jeansoulin, editors. Fundamentals of spatial data quality. ISTE, 2006. Google Scholar
  13. H. L. Dreyfus and S. E. Dreyfus. From Socrates to Expert Systems: The Limits of Calculative Rationality. In C. Mitcham and A. Huning, editors, Philosophy and Technology II, number 90 in Boston Studies in the Philosophy of Science, pages 111-130. Springer Netherlands, 1986. Google Scholar
  14. M. J. Egenhofer and D. M. Mark. Naive Geography. In Spatial Information Theory A Theoretical Basis for GIS, pages 1-15. Springer, Berlin, Heidelberg, September 1995. Google Scholar
  15. Hongchao Fan, Alexander Zipf, Qing Fu, and Pascal Neis. Quality assessment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science, 28(4):700-719, 2014. Google Scholar
  16. S. Gao, K. Janowicz, and G. McKenzie. Towards Platial Joins and Buffers in Place-Based GIS. In Proceedings of The First ACM SIGSPATIAL International Workshop on Computational Models of Place, COMP '13, pages 42:42-42:49, New York, NY, USA, 2013. ACM. Google Scholar
  17. F. Girardin, F. Calabrese, F. D. Fiore, C. Ratti, and J. Blat. Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4):36-43, October 2008. Google Scholar
  18. R. G. Golledge. The Nature of Geographic Knowledge. Annals of the Association of American Geographers, 92(1):1-14, March 2002. Google Scholar
  19. M. Goodchild. NeoGeography and the Nature of Geographic Expertise. J. Locat. Based Serv., 3(2):82-96, June 2009. Google Scholar
  20. M. F. Goodchild and L. Li. Assuring the quality of volunteered geographic information. Spatial statistics, 1:110-120, 2012. Google Scholar
  21. M. Haklay. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environment and Planning B: Planning and Design, 37(4):682-703, 2010. Google Scholar
  22. M. Haklay. Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation. In Daniel Sui, Sarah Elwood, and Michael Goodchild, editors, Crowdsourcing Geographic Knowledge, pages 105-122. Springer Netherlands, January 2013. Google Scholar
  23. Carsten Keßler and René Theodore Anton De Groot. Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap. In Geographic information science at the heart of Europe, pages 21-37. Springer, 2013. Google Scholar
  24. M. Lewicka. Place attachment: How far have we come in the last 40 years? Journal of Environmental Psychology, 31(3):207-230, September 2011. Google Scholar
  25. S. Maderson and S. Wynne-Jones. Beekeepers' knowledges and participation in pollinator conservation policy. Journal of Rural Studies, 45:88-98, June 2016. Google Scholar
  26. S. Maguire and M. Tomko. Ripe for the picking? Dataset maturity assessment based on temporal dynamics of feature definitions. International Journal of Geographical Information Science, 0(0):1-25, February 2017. Google Scholar
  27. I. Poças, J. Gonçalves, B. Marcos, J. Alonso, P. Castro, and J. P. Honrado. Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring. International Journal of Geographical Information Science, 28(11):2356-2371, 2014. Google Scholar
  28. J. Preece. Citizen Science: New Research Challenges for Human-Computer Interaction. International Journal of Human-Computer Interaction, 32(8):585-612, 2016. Google Scholar
  29. T. Quesnot and S. Roche. Platial or Locational Data? Toward the Characterization of Social Location Sharing. In 2015 48th Hawaii International Conference on System Sciences, pages 1973-1982, January 2015. Google Scholar
  30. C. Robertson, R. Feick, M. Sykora, K. Shankardass, and K. Shaughnessy. Personal Activity Centres and Geosocial Data Analysis: Combining big data with small data. In AGILE 2017, Wageningen, Netherlands, May 2017. Springer-Verlag. Google Scholar
  31. R. A. Rundstrom. GIS, Indigenous Peoples, and Epistemological Diversity. Cartography and Geographic Information Systems, 22(1):45-57, January 1995. URL: http://dx.doi.org/10.1559/152304095782540564.
  32. H. Senaratne, A. Mobasheri, A. L. Ali, C. Capineri, and M. Haklay. A review of volunteered geographic information quality assessment methods. International Journal of Geographical Information Science, 31(1):139-167, 2017. Google Scholar
  33. M. D. Sykora, C. Robertson, K. Shankardass, R. Feick, K. Shaughnessy, B. Coates, H. Lawrence, and T. Jackson. Stresscapes: validating linkages between place and stress expression on social media. In Proceedings of the 2nd International Workshop on Mining Urban Data, pages 80-84, Lille, France, 2015. Google Scholar
  34. Y.-F. Tuan. Place: An Experiential Perspective. Geographical Review, 65(2):151-165, April 1975. ArticleType: research-article / Full publication date: Apr., 1975 / Copyright 1975 American Geographical Society. Google Scholar
  35. M. Van Exel, E. Dias, and S. Fruijtier. The impact of crowdsourcing on spatial data quality indicators. Proceedings of GiScience 2011, 2010. Google Scholar
  36. D. Watson, L. A. Clark, and A. Tellegen. Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54(6):1063-1070, 1988. Google Scholar
  37. B. Wynne. Sheepfarming after Chernobyl: A Case Study in Communicating Scientific Information. Environment: Science and Policy for Sustainable Development, 31(2):10-39, March 1989. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail