Journal of Geographical Systems

, Volume 10, Issue 1, pp 71–88 | Cite as

Analysis of dependence of decision quality on data quality

Original Article

Abstract

GIS professionals seem to assume that better data lead to better decisions, but how does one decide when better data lead to a better decision? An analysis to determine the effects of data quality on the quality of decisions provides criteria whether to invest in data quality improvement. This article analyzes data quality and how it influences the quality of a decision. It uses an example of an environmental engineering decision to demonstrate a general method to assess the influence of data quality on the decision. It shows that the uncertainty in aspects, which are poorly known, e.g., the necessary security levels, dominate the uncertainty of many decisions. Efforts to collect more or better data to improve the data quality of those stored in a GIS would not reduce uncertainty in the decision significantly. This result seems to be consistent with results from other studies for this very large class of decisions. The article gives a general method to assess whether collecting better data improves a decision or not.

Keywords

Data quality 

References

  1. Achatschitz C (2006) Preference based retrieval of information elements. In: 12th International symposium on spatial data handling, Springer, ViennaGoogle Scholar
  2. Agumya A, Hunter GJ (2002) Responding to the consequences of uncertainty in geographical data. Int J Geogr Inf Syst 16(5):405–417CrossRefGoogle Scholar
  3. Attaran A (2006) Will negligence law Poison the well of foreign aid? A case comment on: Binod Sutradhar v. natural environment research council. Glob Jurist Adv 6(1, Article 3)Google Scholar
  4. Boin AT, Hunter GJ (2007) What communicates quality to the spatial data consumer? In: Proceedings of the 7th international symposium on spatial data quality (ISSDQ 2007), Enschede, The NetherlandsGoogle Scholar
  5. de Bruin S, Bregt A, van de Ven M (2001) Assessing fitness for use: the expected value of spatial data sets. Int J Geogr Inf Sci 15(5):457–471CrossRefGoogle Scholar
  6. ems-i (2006) Hydrologic models—basic equation. http://www.ems-i.com/wmshelp/Hydrologic_Models/Models/Rational/Equation/Basic_Equation.htm. Accessed 21 September 2006
  7. Feynman R (1998) The character of physical law. The MIT Press, Cambridge Google Scholar
  8. Fisher PF, Tate NJ (2006) Causes and consequences of error in digital elevation models. Prog Phys Geogr 30:467–489CrossRefGoogle Scholar
  9. Frank AU (2001) Tiers of ontology and consistency constraints in geographic information systems. Int J Geogr Inf Sci 75(5):667–678 (Special Issue on Ontology of Geographic Information)CrossRefGoogle Scholar
  10. Frank AU (2003) Ontology for spatio-temporal databases. In: Koubarakis M, Sellis T (eds) Spatiotemporal databases: the chorochronos approach, vol 2520. Springer, Berlin, pp 9–78Google Scholar
  11. Frank AU (2007a) Assessing the quality of data with a decision model. In: 5th International syposium on spatial data quality 2007, Enschede, NLGoogle Scholar
  12. Frank AU (2007b) Data quality ontology: an ontology for imperfect knowledge. In: Winter S, Duckham M, Kulik L, Kuipers B (eds) Spatial information thoery (COSIT 2007). Springer, BerlinGoogle Scholar
  13. Frank AU (2007c) Incompleteness, error, approximation, and uncertainty: an ontological approach to data quality. Geographic Uncertainty in Environmental Security. NATO Advanced Research Workshop, Kiev, Ukraine, SpringerGoogle Scholar
  14. Frank AU, Grum E (eds) (2004) Proceedings of the ISSDQ ‘04. Geoinfo series. Institute for Geoinformation, ViennaGoogle Scholar
  15. Frank AU, Mark DM (eds) (1991) Cognitive and linguistic aspects of geographic space. NATO ASI series D. Kluwer Academic Publishers, Dordrecht, The NetherlandsGoogle Scholar
  16. Frank AU, Palmer B, Robinson V (1986) Formal methods for accurate definition of some fundamental terms in physical geography. In: 2nd International symposium on spatial data handling, Seattle, WashGoogle Scholar
  17. Goodchild M (2006) Preface. In: ISTE 2006Google Scholar
  18. Goodchild M, Jeansoulin R (eds) (1998) Data quality in geographic information—from error to uncertainty. Hermes, ParisGoogle Scholar
  19. Goodchild MF, Gopal S (eds) (1989) The accuracy of spatial databases. Taylor & Francis, BasingstokeGoogle Scholar
  20. Haupt R (2000) Regionalisierung von Hochwasserkennwerten in Mecklenburg-Vorpommern. Universität Rostock, RostockGoogle Scholar
  21. Heuvelink GBM (1998a) Error propagation in environmental modelling with GIS. Taylor & Francis, LondonGoogle Scholar
  22. Heuvelink GBM (1998b) Geographic information technologies in society. 2007, from http://www.ncgia.ucsb.edu/giscc/units/u098/u098.html
  23. Heuvelink GBM, Burrough PA, Stein A (2006) Developments in analysis of spatial uncertainty since 1989. Classics from IJGIS: 20 years of the international journal of geographical information science and systems. P. F. Fisher. CRC, Boca Raton, pp 91–95Google Scholar
  24. Huggins DL (2006) Storm rainfall characterization. http://pasture.ecn.purdue.edu/~engelb/abe526/Rain/. Accessed 21 September 2006
  25. Karssenberg D, De Jong K (2005) Dynamic environmental modelling in GIS: 2. Modelling error propagation. Int J Geogr Inf Syst 19(6):623–637CrossRefGoogle Scholar
  26. Keller G, Sherar J (2007) Tools for hydraulic and road design. LOW-VOLUME ROADS ENGINEERING best management practices field guide. http://ntl.bts.gov/lib/24000/24600/24650/Chapters/H_Ch6_Tools_for_Hydraulic_Design.pdf. Accessed 04 November 2007
  27. Lehmann J, Breucker J, Brouwer B (2006) Causation in AI and law. ICAIL 2003 workshop on legal ontologies and web based legal information management, University of Amsterdam, Faculty of Law, p 34Google Scholar
  28. Lords Ho (2006) Binod Sutradhar v. Natural Environment Research Council. House of lords session 2005–2006; EWCA Civ 175; UKHL 33. London, UK, House of Lords, p 21Google Scholar
  29. McCuen RH (1989) Hydrologic analysis and design. Prentice-Hall, Englewood CliffsGoogle Scholar
  30. NCGIA (1989) The U.S. national center for geographic information and analysis: an overview of the agenda for research and education. IJGIS 2(3):117–136Google Scholar
  31. Openshaw S (1984a) Ecological fallacies and the analysis of areal census data. Environ Plan A 16(1):17–31CrossRefGoogle Scholar
  32. Openshaw S (1984b) The modifiable areal unit problem. Conc Tech Mod Geogr (CATMOG) 38:40Google Scholar
  33. Openshaw S, Charlton M, Carver S (1991) Error propagation: a Monte Carlo simulation. In: Masser I, Blakemore M (eds) Handling geographical information, vol 1. Longman Scientific & Technical, Essex, pp 78–101Google Scholar
  34. Physicslabs (2006) Uncer. and error propagation. http://physicslabs.phys.cwru.edu/MECH/Manual/Appendix_V_Error%20Prop.pdf. Accessed 21 September 2006
  35. Purvis JC, Tyler W, Sidlow S (1988) Maximum rainfall intensity in south Carolina by county. State Climatology Office, South CarolinaGoogle Scholar
  36. Schneider J (1999) Zur Dominanz der Lastannahmen im Sicherheitsnachweis. Festschrift zum 60. Geburtstag von Eduardo Anderheggen, Institut für Baustatik und Konstruktion der ETH ZürichGoogle Scholar
  37. Schneider J (2000) Safety—a matter of risk, cost, and consensus. Struct Eng Int 10(4):266–269CrossRefGoogle Scholar
  38. Shi W, Fisher PF, Goodchild MF (2002) Spatial data quality. Taylor & Francis, LondonGoogle Scholar
  39. Shi W, Goodchild MF, Fisher PF (eds) (2003) In: Proceedings of the 2nd international symposium on spatial data quality ‘03. Hong Kong Polytechnic University, Hong KongGoogle Scholar
  40. Todini E (1988) Rainfall-runoff modeling: past, present and future. J Hydrol JHYDA7 100(1/3):341–352CrossRefGoogle Scholar
  41. Topanga (2006) Description of hydrologic models. http://www.topangaonline.com/twc/water/APPM1.html. Accessed 21 September 2006
  42. USDAForestService (2006) Chapter 5—hydrology. FSH 7709.56b—transportation structures handbook. http://www.fs.fed.us/im/directives/fsh/7709.56b/7709.56b,5.txt. Accessed 21 September 2006
  43. Wikipedia (2007) Risk aversion. http://en.wikipedia.org/wiki/Risk_aversion
  44. Wu L, Shi W, Fang Y, Tong Q (eds) (2005) In: Proceedings of the 4th international symposium on spatial data quality (ISSDQ 05). Peking University, BeijingGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Institute for Geoinformation and CartographyTechnical University ViennaViennaAustria

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