Advertisement

Why Is Scale an Effective Descriptor for Data Quality? The Physical and Ontological Rationale for Imprecision and Level of Detail

  • Andrew U. Frank
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Observations and processing of data create data and their quality. Quantitative descriptors of data quality must be justified by the properties of the observation process. In this contribution two unavoidable sources of imperfections imperfection in the observation of physical properties are identified and their influences on data collections analyzed. These are, firstly, the random noise disturbing precise measurements; secondly, finiteness of observations—only a finite number of observations is possible and each of it averages properties over an extended area.

These two unavoidable imperfections of the data collection process determine data quality. Rational data quality measures must be derived from them: Precision is the effect of noise in the measurement. The finiteness of observations leads to a novel formalized and quantifiable approach to level of detail.

The customary description of a geographic data set by ‘scale’ seems to relate these two sources of imperfection in a single characteristic; the theory described here justifies this approach for static representation of geographic space and shows how to extend it for spatio-temporal data.

Keywords

Data Quality Physical Object Observation System Sampling Theorem Object Formation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

These ideas were developed systematically for a talk I presented at the University of Münster. I am grateful to Werner Kuhn for this opportunity.

References

  1. Abler R (1987) Review of the Federal Research Agenda. In: International Geographic Information Systems (IGIS) Symposium (IGIS'87), The Research Agenda, Arlington, VAGoogle Scholar
  2. Cardelli L (1997) Type Systems. In: Tucker AB (ed) Handbook of Computer Science and Engineering, CRC Press, pp 2208–2236Google Scholar
  3. Chrisman N (1987) Fundamental Principles of Geographic Information Systems. In: Auto-Carto 8, Baltimore, MA, ASPRS & ACSMGoogle Scholar
  4. Couclelis H (1992) People Manipulate Objects (but Cultivate Fields): Beyond the Raster-Vector Debate in GIS. In: Frank AU, Campari I, Formentini U (eds) Theories and Methods of Spatio-Temporal Reasoning in Geographic Space, Springer, Berlin Heidelberg New York, LNCS 639, pp 65–77Google Scholar
  5. Egenhofer MJ, Frank AU (1986) Connection between Local and Regional: Additional “Intelligence” Needed. In: FIG XVIII International Congress of Surveyors, Toronto, Canada (June 1-11, 1986)Google Scholar
  6. Frank AU (2006) Distinctions Produce a Taxonomic Lattice: Are These the Units of Mentalese? In: International Conference on Formal Ontology in Information Systems (FOIS), Baltimore, Maryland, IOS PressGoogle Scholar
  7. Frank AU (2001) Tiers of Ontology and Consistency Constraints in Geographic Information Systems. International Journal of Geographical Information Science (IJGIS) 75(5 (Special Issue on Ontology of Geographic Information)): 667–678Google Scholar
  8. Frank AU (2003) Ontology for Spatio-Temporal Databases. In: Koubarakis M, Sellis T, Frank AU, Grumbach S, Güting RH, Jensen CS, Lorentzos N, Manolopoulos Y, Nardelli E, Pernici B, Schek H-J, Scholl M, Theodoulidis B, Tryfona N (eds) Spatiotemporal Databases: The Chorochronos Approach, Springer, Berlin Heidelberg New York, pp 9–78Google Scholar
  9. Frank AU (2007) Data Quality Ontology: An Ontology for Imperfect Knowledge. In: Winter S, Duckham D, Kulik L, Kuipers B (eds) Spatial Information Theory, 8th International Conference, COSIT 2007, Melbourne, Australia, September 19-23, 2007, Proceedings, Lecture Notes in Computer Science 4736, Springer, Berlin Heidelberg New York, pp 406–420Google Scholar
  10. Frank AU (2008a) Analysis of Dependence of Decision Quality on Data Quality. Journal of Geographical Systems 10(1): 71–88CrossRefGoogle Scholar
  11. Frank AU (2008b) Data Quality - What Can an Ontological Analysis Contribute? In: Spatial Accuracy Assessment in Natural Resources and Environmental Sciences 2008, Shanghai, China, WorldAcademicPressGoogle Scholar
  12. Frank AU (draft 2005) Ontology for GIS. Vienna, Technical University Vienna, Institute for Geoinformation and CartographyGoogle Scholar
  13. Gabora L, Rosch E, Aerts E (2008) Toward an Ecological Theory of Concepts. Ecological Psychology 20(1): 84–116CrossRefGoogle Scholar
  14. Gibson JJ (1986) The Ecological Approach to Visual Perception, Hillsdale, NJ, Lawrence ErlbaumGoogle Scholar
  15. Goodchild MF, Egenhofer MJ, Kemp KK, Mark DM, Sheppard E (1999) Introduction to the Varenius Project. International Journal of Geographical Information Science (IJGIS) 13(8): 731–745CrossRefGoogle Scholar
  16. Goodchild MF, Proctor J (1997) Scale in a Digital Geographic World. Geographical & Environmental Modelling 1(1): 5–23Google Scholar
  17. Grenon P, Smith B, Goldberg L (2004) Biodynamic Ontology: Applying BFO in the Biomedical Domain. In: Pisanelli DM (ed) Ontologies in Medicine, IOS Press, Amsterdam, pp 20–38.Google Scholar
  18. Gruber, T. (2005). “TagOntology - a way to agree on the semantics of tagging data.” Retrieved October 29, 2005., from http://tomgruber.org/writing/tagontology-tagcapm-talk.pdf.
  19. Guarino, N. (1995). “Formal Ontology, Conceptual Analysis and Knowledge Representation.” International Journal of Human and Computer Studies. Special Issue on Formal Ontology, Conceptual Analysis and Knowledge Representation, edited by N. Guarino and R. Poli 43(5/6).Google Scholar
  20. Heidegger, M. (1927; reprint 1993). Sein und Zeit. Tübingen, Niemeyer.Google Scholar
  21. Horn, B. K. P. (1986). Robot Vision. Cambridge, Mass, MIT Press.Google Scholar
  22. Husserl (1900/01). Logische Untersuchungen. Halle, M. Niemeyer.Google Scholar
  23. Krantz DH, Luce RD, Suppes P, Tversky A (1971) Foundations of Measurement. New York, Academic PressGoogle Scholar
  24. Kuhn W (2007) An Image-Schematic Account of Spatial Categories. In: Winter S, Duckham D, Kulik L, Kuipers B (eds) Spatial Information Theory, 8th International Conference, COSIT 2007, Melbourne, Australia, September 19-23, 2007, Proceedings, Lecture Notes in Computer Science 4736, Springer, Berlin Heidelberg New YorkGoogle Scholar
  25. Lam N, Quattrochi DA (1992) On the issues of scale, resolution, and fractal analysis in the mapping sciences. The Professional Geographer (44): 88–98Google Scholar
  26. Marr D (1982) Vision. New York, N.Y., W.H. FreemanGoogle Scholar
  27. McCarthy J, Hayes PJ (1969) Some Philosophical Problems from the Standpoint of Artificial Intelligence. In: Meltzer B, Michie D (eds) Machine Intelligence 4. Edinburgh, Edinburgh University Press, pp 463–502Google Scholar
  28. NCGIA (1989a) The U.S. National Center for Geographic Information and Analysis: An Overview of the Agenda for Research and Education. International Journal of Geographical Information Science (IJGIS) 2(3): 117–136Google Scholar
  29. NCGIA (1989b) Use and Value of Geographic Information Initiative Four Specialist Meeting, Report and Proceedings, National Center for Geographic Information and Analysis; Department of Surveying Engineering, University of Maine; Department of Geography, SUNY at BuffaloGoogle Scholar
  30. Openshaw S, Charlton M, Wymer C, Craft A (1987) A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets. International Journal of Geographical Information Systems 1(4): 335–358CrossRefGoogle Scholar
  31. Orth B (1974) Einführung in die Theorie des Messens. Verlag W. Kohlhammer, Stuttgart, Berlin, Köln, MainzGoogle Scholar
  32. Raubal M (2002). Wayfinding in Built Environments: The Case of Airports. Münster, Solingen, Institut für Geoinformatik, Institut für Geoinformation.Google Scholar
  33. Reitsma F, Bittner T (2003) Process, Hierarchy, and Scale. In: Spatial Information Theory, Cognitive and Computational Foundations of Geographic Information Science, International Conference COSIT'03Google Scholar
  34. Riedl M (2009) Erstellung von Baulandbilanzen in Tirol. In: 15. Internationale Geodätische Woche Obergurgl, Ötztal Tirol, WichmannGoogle Scholar
  35. Robinson V, Frank AU (1987) Expert Systems Applied to Problems in Geographic Information Systems: Introduction, Review and Prospects. In: Auto-Carto 8, Baltimore, MA, ASPRS & ACSMGoogle Scholar
  36. Schneider M (1995) Spatial Data Types for Database Systems. Hagen, FernUniversitätGoogle Scholar
  37. Searle JR (1995) The Construction of Social Reality. New York, The Free PressGoogle Scholar
  38. Stefanidis A, Nittel S (2005) Geosensor Networks. Boca Raton, Florida: CRC PressGoogle Scholar
  39. Timpf S, Raubal M, Kuhn W (1996) Experiences with Metadata. In: 7th Int. Symposium on Spatial Data Handling, SDH'96, Delft, The Netherlands (August 12-16, 1996), Faculty of Geodectic Engineering, Delft University of TechnologyGoogle Scholar
  40. Tomlin CD (1983) A Map Algebra. Harvard Computer Graphics Conference, Cambridge, Mass.Google Scholar
  41. Zadeh LA (1974) Fuzzy Logic and Its Application to Approximate Reasoning. In: Information Processing, North-Holland Publishing CompanyGoogle Scholar
  42. Zadeh LA (2002) Some Reflections on Information Granulation and Its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language. In: Data Mining, Rough Sets and Granular Computing, Heidelberg, Germany, Physica-Verlag GmbHGoogle Scholar
  43. Zaibert L, Smith B (2004) Real Estate - Foundations of the Ontology of Property. In: Stuckenschmidt H, Stubkjaer E, Schlieder C (eds) The Ontology and Modelling of Real Estate Transactions: European Jurisdictions, Ashgate Pub Ltd, pp 35–51Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  1. 1.Department of Geoinformation and CartographyTechnical University ViennaViennaAustria

Personalised recommendations