Advertisement

Empirical Study on Location Indeterminacy of Localities

  • Sungsoon Hwang
  • Jean-Claude Thill
Conference paper

Abstract

Humans perceive the boundary of locality vaguely. This paper presents how the indeterminate boundaries of localities can be represented in GIS. For this task, indeterminate boundaries of localities are modeled by a fuzzy set membership function in which generic rules on geospatial objects are incorporated. Georeferenced traffic crash data reveal that police officers identify localities precisely at best 88% of the time. An empirical analysis indicates that people are 6% more confident in identifying urban localities than rural localities. As a conclusion, fuzzy set theory seems to provide a reasonable mechanics to represent vague concept of geospatial objects. The comparison of urban versus rural localities with respect to location indeterminacy suggests that neighborhood types may affect the way humans acquire spatial knowledge and forge mental representations of it.

Keywords

GIS uncertainty fuzzy set theory mental maps 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Allen JF (1983) Maintaining knowledge about temporal intervals. Communications of the ACM 26(11):832–843CrossRefGoogle Scholar
  2. Bailey TC, Gatrell AC (1995) Interactive Spatial Data Analysis. Longman Scientific & Technical, Essex, pp.303–308Google Scholar
  3. Cohn A, Gotts N (1996) The ‘egg-yolk’ representation of regions with indeterminate boundaries. In: Burrough P, Frank AU (ed) Geographic Objects with Indeterminate Boundaries, Taylor & Francis, London, pp 171–187Google Scholar
  4. Egenhofer M, Franzosa R (1991) Point-set topological spatial relations. INT J of Geographical Information Systems 5(2):161–174Google Scholar
  5. Erwig M, Schneider M (1997) Vague regions. In: 5th international symposium on advances in spatial databases (SSD’97), LNCS Vol 1262, Springer, pp 298–320Google Scholar
  6. Gahegan M (1995) Proximity operators for qualitative spatial reasoning, In: Frank AU, Kuhn W (ed) Spatial Information Theory: A Theoretical Basis for GIS. LNCS Vol 988, Springer, Berlin, Germany, pp 31–44Google Scholar
  7. Golledge RG, Dougherty V, Bell S (1995) Acquiring spatial knowledge: survey versus route-based knowledge in unfamiliar environments. Annals of AAG 85(1):134–158Google Scholar
  8. Goodchild MF (2001) A geographer looks at spatial information theory, In: Montello DR (ed) COSIT 2001, Springer-Verlag, London, pp.1–13Google Scholar
  9. Gould P, White R (1986) Mental Maps. Harmondsworth, PenguinGoogle Scholar
  10. Guarino N, Welty C (2000) Ontological analysis of taxonomic relationships. In: Laender A, Storey V (ed) Proceedings of ER-2000: The International Conference on Conceptual Modeling, Springer-VerlagGoogle Scholar
  11. Hwang S, Thill JC (2003) Georeferencing Historical FARS Accident Data: A Preliminary Report, Unpublished document, Department of Geography and NCGIA, State University of New York at BuffaloGoogle Scholar
  12. Kennedy, S (1989) The small number problem and the accuracy of spatial data-bases. In: Goodchild M, Gopal S (ed) Accuracy of Spatial Databases, Taylor & Francis, London, pp 187–196Google Scholar
  13. Montello DR, Goodchild MR, Gottsegen J, and Fohl P (2003) Where’s down-towns?: behavioral methods for determining referents of vague spatial queries. Spatial Cognition and Computation, 3(2&3):185–204Google Scholar
  14. NHTSA (1995) FARS 1996 Coding and Validation Manual. National Center for Statistics and Analysis, National Highway Traffic Safety Administration, Department of Transportation, Washington, D.C.Google Scholar
  15. Robinson VB (1988) Some implications of fuzzy set theory applied to geographic databases. Computers, Environment, and Urban Systems 12:89–97CrossRefGoogle Scholar
  16. Schneider M (1999) Uncertainty management for spatial data in databases: fuzzy spatial data types. In: Guting RH, Papadias D, Lochovsky F (ed) SSD’99, LNCS Vol 1651, Springer-Verlag, Heidelberg, pp 330–351Google Scholar
  17. Smith B (1995) On drawing lines on a map. In: Frank AU, Kuhn W (ed) Spatial information theory: A theoretical basis for GIS. LNCS Vol 988, Springer, Berlin, Germany, pp 475–484Google Scholar
  18. Stefanakis E, Vazirgiannis M, Sellis T (1999) Incorporating fuzzy set methodologies in a DBMS repository for the application domain of GIS. INT J Geographical Information Science 13(7):657–675Google Scholar
  19. Thill JC, Sui DZ (1993) Mental maps and fuzzy preferences. Professional Geographer 45: 264–276CrossRefGoogle Scholar
  20. Ullman EL (1956) The Role of Transportation and the Bases for Interaction. In: William TJ (ed) Man’s Role in Changing the Face of the Earth, University of Chicago Press, pp 862–80.Google Scholar
  21. Wang F, Hall GB (1996) Fuzzy representation of geographic boundaries in GIS. INT J of Geographical Information Systems 10(5):573–590Google Scholar
  22. Worboys M (2001) Nearness relations in environmental space. INT J Geographical Information Science 15(7):633–651Google Scholar
  23. Yao X, Thill JC (2004) How far is too far? A statistical approach to context-contingent proximity modeling. Transactions in GIS, forthcomingGoogle Scholar
  24. Zadeh LA (1965) Fuzzy sets. Information and Control 8:338–353CrossRefGoogle Scholar
  25. Zelinsky W (1980) North America’s vernacular regions. Annals of AAG 70(1):1–16Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sungsoon Hwang
    • 1
  • Jean-Claude Thill
    • 1
  1. 1.Department of GeographyState University of New York at BuffaloBuffaloUSA

Personalised recommendations