Convergence of Experts’ Opinions on the Territory: The Spatial Delphi and the Spatial Shang

  • Simone Di Zio


The judgments of a panel of experts are of extreme usefulness when, in front of a decision-making problem, quantitativ data are insufficient or completely absent. Experts’ opinions are helpful in forecasting contexts, for the detection of innovative solutions or for the verification and refinement of consensus on objectives or alternative scenarios.

The way the views are collected is crucial, and without a rigorous methodology any consultation process may become vain. In literature, there are many methods, but some are used for the ease of application rather than for their scientific properties. Methods such as focus group, face-to-face interview, or online questionnaire, are very popular but have quite important drawbacks.

Many of those disadvantages are overcome by the methods of the “Delphi family”, whose prototype is the Delphi method, which involves the repeated administration of questionnaires, narrowing the range of assessment uncertainty without generating errors that result from face-to-face interactions. To date, the Delphi technique has a very high number of applications and its success has produced a wide range of methods that are its variants.

This chapter describes two recent variants, called Spatial Delphi and Spatial Shang, applicable when consultations and consequent decisions concern matters of spatial location. The judgments of the experts are collected by means of points placed on a map, and the process of the convergence of opinions is built up through the use of simple geometric shapes (circles or rectangles). During the subsequent iterations of the procedure, the shapes become smaller and smaller, until to circumscribe a very small portion of territory that is the final solution to the research/decision problem.

After the discussion of the methods and the presentation of some practical applications, some possible evolutions are discussed that most likely will produce a future increase in the application of these techniques.


  1. Bailey, T. C., & Gatrell, A. C. (1995). Interactive Spatial Data Analysis. New York: J. Wiley.Google Scholar
  2. Brockhaus, W. L., & Mickelsen, J. F. (1975). An Analysis of Prior Delphi Applications. Technological Forecasting and Social Change, 10(1), 103–110.CrossRefGoogle Scholar
  3. Chatterjee, S. (1975). Reaching a Consensus: Some Limit Theorems. Bulletin of the International Statistical Institute, 46(3), 156–160.Google Scholar
  4. Chung, K. H., & Ferris, M. J. (1971). An Inquiry of the Nominal Group Process. Academy of Management Journal, 14(4), 520–524.CrossRefGoogle Scholar
  5. Dajani, J. S., Sincoff, M. Z., & Talley, W. K. (1979). Stability and Agreement Criteria for the Termination of Delphi Studies. Technological Forecasting and Social Change, 13(1), 83–90.CrossRefGoogle Scholar
  6. Dalkey, N. C., & Helmer, O. (1963). An Experimental Application of Delphi Method to the Use of Experts. Management Science, 9(3), 458–467.CrossRefGoogle Scholar
  7. de Ayala, R. J. (2009). The Theory and Practice of Item Response Theory. New York: Guilford Press.Google Scholar
  8. De Groot, M. H. (1974). Reaching a Consensus. Journal American Statistical Association, 69(345), 118–121.CrossRefGoogle Scholar
  9. De Mars, C. (2010). Item Response Theory. Understanding Statistics Measurement. Oxford: Oxford University Press.Google Scholar
  10. Di Zio, S., & Fontanella, L. (2012). Public Geomarketing: Georeferencing IRT Models to Support Public Decision. Statistica Applicata—Italian Journal of Applied Statistics, 24(3), 301–320.Google Scholar
  11. Di Zio, S., & Pacinelli, A. (2011). Opinion Convergence in Location: A Spatial Version of the Delphi Method. Technological Forecasting and Social Change, 78(9), 1565–1578.CrossRefGoogle Scholar
  12. Di Zio, S., & Staniscia, B. (2014a). A Spatial Version of the Shang Method. Technological Forecasting and Social Change, 86, 207–215.CrossRefGoogle Scholar
  13. Di Zio, S., & Staniscia, B. (2014b). Citizen Participation and Awareness Raising in Coastal Protected Areas. A Case Study from Italy. In A. Montanari (Ed.), Mitigating Conflicts in Coastal Areas Through Science Dissemination: Fostering Dialogue Between Researchers and Stakeholders (pp. 155–197). Rome: Sapienza Università Editrice.Google Scholar
  14. Di Zio, S., Castillo Rosas, J., & Lamelza, L. (2016). Real Time Spatial Delphi: Fast Convergence of Experts’ Opinions on the Territory. Technological Forecasting and Social Change, on line doi:10.1016/j.techfore.2016.09.029.Google Scholar
  15. Dragicevic, S., & Balram, S. (2004). A Web GIS Collaborative Framework to Structure and Manage Distributed Planning Processes. Journal of Geographical Systems, 6(2), 133–153.CrossRefGoogle Scholar
  16. Ford, D. A. (1975). Shang Inquiry as an Alternative to Delphi: Some Experimental Findings. Technological Forecasting and Social Change, 7(2), 139–164.CrossRefGoogle Scholar
  17. Glenn, J. C. (2009). Participatory Methods. In J. C. Glenn & T. J. Gordon (Eds.), Futures Research Methodology, CD-ROM Version 3.0. Washington: The Millennium Project, American Council for the United Nations University.Google Scholar
  18. Gordon, T. J. (2009a). The Delphi Method. In J. C. Glenn & T. J. Gordon (Eds.), Futures Research Methodology, CD-ROM Version 3.0. Washington: The Millennium Project, American Council for the United Nations University.Google Scholar
  19. Gordon, T. J. (2009b). The Real-Time Delphi Method. In J. C. Glenn & T. J. Gordon (Eds.), Futures Research Methodology, CD-ROM Version 3.0. Washington: The Millennium Project, American Council for the United Nations University.Google Scholar
  20. Gordon, T. J., & Helmer, O. (1964). Report on a Long-Range Forecasting Study. R-2982. Santa Monica: The Rand Corporation.Google Scholar
  21. Gordon, T. J., & Pease, A. (2006). RT Delphi: An Efficient, “Round-Less” Almost Real Time Delphi Method. Technological Forecasting and Social Change, 73(4), 321–333.CrossRefGoogle Scholar
  22. Grime, M. M., & Wright, G. (2016). Delphi Method. New York: Wiley StatsRef: Statistics Reference Online: John Wiley & Sons.CrossRefGoogle Scholar
  23. Gustafson, D. H., Shukla, R. K., Delbecq, A. L., & Walster, W. G. (1973). A Comparative Study of Differences in Subjective Likelihood Estimates Made by Individuals, Interacting Groups, Delphi Groups, and Nominal Groups. Organizational Behavior and Human Performance, 9(2), 280–291.CrossRefGoogle Scholar
  24. Hassan, G. (2013). Groupthink Principles and Fundamentals in Organizations. Interdisciplinary Journal of Contemporary Research in Business, 5(8), 225–240.Google Scholar
  25. Hastings, H. M., & Sugihara, G. (1993). Fractals: A User’s Guide for the Natural Sciences. Oxford, UK: Oxford University Press.Google Scholar
  26. Hoffman, L. R. (1965). Group Problem Solving. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology. New York: Academic Press.Google Scholar
  27. Jankowski, P., Swobodzinski, M., & Ligmann-Zielinska, A. (2008). Choice Modeler: A Web-Based Spatial Multiple Criteria Evaluation Tool. Transactions in GIS, 12(4), 541–561.CrossRefGoogle Scholar
  28. Neill, S. A. (2009). The Alternate Channel: How Social Media Is Challenging the Spiral of Silence Theory in GLBT Communities of Color. Washington, DC: American University.Google Scholar
  29. Riggs, W. E. (1983). The Delphi Technique, an Experimental Evaluation. Technological Forecasting and Social Change, 23(1), 89–94.CrossRefGoogle Scholar
  30. Ripley, B. D. (1976). The Second-Order Analysis of Stationary Point Processes. Journal of Applied Probability, 13(2), 255–266.CrossRefGoogle Scholar
  31. Rowe, G., & Wright, G. (2001). Expert Opinions in Forecasting: The Role of the Delphi Technique. In J. Scott Armstrong (Ed.), Principles of Forecasting (pp. 125–144). Boston: Kluwer Academic.CrossRefGoogle Scholar
  32. Sarra, A., Di Zio, S., & Cappucci, M. (2015). A Quantitative Valuation of Tourist Experience in Lisbon. Annals of Tourism Research, 53, 1–16.CrossRefGoogle Scholar
  33. Scheibe, M., Skutsch, M., & Schofer, J. L. (1975). Experiments in Delphi Methodology. In H. A. Linstone & M. Turoff (Eds.), The Delphi Method: Techniques and Applications (pp. 262–287). Reading: Addison-Wesley.Google Scholar
  34. Torrance, P. E. (1957). Group Decision Making and Disagreement. Social Forces, 35(4), 314–318.CrossRefGoogle Scholar
  35. Turoff, M. (1970). The Design of a Policy Delphi. Technological Forecasting and Social Change, 2(2), 149–171.CrossRefGoogle Scholar
  36. Van de Ven, A. H. (1974). Group Decision Making and Effectiveness: An Experimental Study. Kent: Kent State University Press.Google Scholar
  37. Van de Ven, A. H., & Delbecq, A. L. (1974). The Effectiveness of Nominal, Delphi, and Interacting Group Decision Making Processes. The Academy of Management Journal, 17(4), 605–621.CrossRefGoogle Scholar
  38. von der Gracht, H. A. (2012). Consensus Measurement in Delphi Studies. Review and Implications for Future Quality Assurance. Technological Forecasting and Social Change, 79(8), 1525–1536.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2018

Authors and Affiliations

  • Simone Di Zio
    • 1
  1. 1.Department of Legal and Social SciencesUniversity “G. d’Annunzio”, Chieti-PescaraChietiItaly

Personalised recommendations