Skip to main content
SpringerLink
Log in

Fuzzy Sets for Representing the Spatial and Temporal Dimensions in GIS Databases

  • Chapter
Spatio-Temporal Databases

Abstract

Geographic phenomena are dynamic and any effort to study, analyze, and predict them must consider both the space and time dimension. Space can be defined based on an absolute or a relative framework. According to the absolute view, space is a container in which objects are located; its structure is best described by the familiar and common three-dimensional Euclidean geometry. The relative view focuses on objects as the subject matter, and space is measured as relationships between objects. While the absolute view involves measurement referenced to a constant base with non-judgmental observations, the relative view implies interpretation of patterns and processes within specific contexts [47, 50]. Time can be described as the absolute linear line on which events occur and on which intervals between events can be measured. Space is three-dimensional and physically perceptible while time is understood as a unidirectional continuous flow. Both geography and cartography deal with space and time, and their relationships with various geographic phenomena. If there is no understanding of time and its impacts, geographers could not analyze and understand spatial changes nor cartographers able to represent these changes on maps [61].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Abraham T., Roddick J. (1999) Survey of spatio-temporal databases. Geo Informatica 3:61–69.

    Google Scholar 

  2. Ahlqvist O., Keukelaar J., Oukbir K. (2000) Rough classification and accuracy assessment. International Journal of Geographical Information Science 14(5) :475–496.

    Article  Google Scholar 

  3. Al-Taha K.K., Snodgrass R., Soo M.D. (1994) Bibliography on spatiotemporal databases. International Journal of Geographical Information Systems 8(1):95–103.

    Article  Google Scholar 

  4. Anile M.A.P., Furno P., Gallo G., Massolo A. (2003) A fuzzy approach to visibility maps creation over digital terrains. Fuzzy Sets and Systems 135:63–80.

    Article  Google Scholar 

  5. Bittner T., Stell J. (2002) Vagueness in rough location. Geo Informatica, 6:99–121.

    Google Scholar 

  6. Blaut J.M. (1961) Space and Process. The Professional Geographer 13(4):1–7.

    Article  Google Scholar 

  7. Burrough P.A. (1989) Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science 40:477–492.

    Article  Google Scholar 

  8. Burrough P.A., MacMillan R.A., Deursen W.V. (1992) Fuzzy classification methods for determining land suitability from soil profile observations and topography. Journal of Soil Science 43:193–210.

    Article  Google Scholar 

  9. Burrough P.A. (1996) Natural objects with indeterminate boundaries. In: Burrough P.A., Frank A. (Eds.) Geographic objects with indeterminate boundaries, Taylor and Francis, London, pp. 3–28.

    Google Scholar 

  10. Burrough P.A., Frank A. (1996) Geographic Objects with Intermediate Boundaries. Innovations in GIS 2. Taylor and Francis, London.

    Google Scholar 

  11. Cheng T., Molenaar M., Lin H. (2001) Formalizing fuzzy objects from uncertain classification results. International Journal of Geographical Information Sciences 15(1):27–42.

    Article  Google Scholar 

  12. Cheng T. (2002) Fuzzy objects: their changes and uncertainties. Photogrammetric Engineering and Remote Sensing 98(1):41–49.: 41–49.

    Google Scholar 

  13. Chrisman N.R. (1998) Beyond the snapshot: changing the approach to change, error and process. In: Egenhofer M.J., Golledge R.G. (Eds.) Spatial and Temporal Reasoning in Geographical Information Systems. Oxford University Press, New York, pp. 85–93.

    Google Scholar 

  14. Claramunt C., Theriault M. (1995). Managing time in GIS: an event-oriented approach. In: Recent Advances in Temporal Databases: Proceedings of the International Workshop on Temporal Databases. Zurich, Switzerland.

    Google Scholar 

  15. Claramunt C., Theriault M. (1997). A qualitative representation of evolving spatial entities in two-dimensional spaces. In: Carver S. (Ed.) Innovations in GIS V. Taylor and Francis, London, pp. 119–129.

    Google Scholar 

  16. Couclelis H. (1999) Space, time, geography. In: Longley P., Goodchild M., Maguire D., Rhind D. (Eds.) Geographical information systems: principles and technical issues. John Wiley and sons, New York, pp. 29–38.

    Google Scholar 

  17. Couclelis H. (1999) Towards an operational typology of geographic entities with ill-defined boundaries. In: Burrough P.A., Frank A. (Eds.) Geographic objects with indeterminate boundaries. Taylor and Francis, London, pp. 45–55.

    Google Scholar 

  18. Davidson D.A., Theocharopoulos S.P., Bloksma R.J. (1994) A land evaluation project in Greece using GIS and based on Boolean and fuzzy set methodologies. International Journal of Geographical Information Systems 8(4):369–384.

    Article  Google Scholar 

  19. Dragićević S., Marceau D.J. (1999) Spatio-temporal interpolation and fuzzy logic for GIS simulation of rural-to-urban transition. Cartography and Geographical Information Science 26(2):125–137.

    Article  Google Scholar 

  20. Dragićević S., Marceau D.J. (2000) An application of fuzzy logic reasoning for GIS temporal modeling of dynamic processes. Fuzzy Sets and Systems 113:69–80.

    Article  Google Scholar 

  21. Dragićević S., Marceau D.J. (2000) A fuzzy set approach for modelling time in GIS. International Journal of Geographical Information Science 14(3):225–245.

    Article  Google Scholar 

  22. Dragićević S., Marceau D.J., Marois C. (2001) Space, time, and dynamics modeling in historical GIS databases: a fuzzy logic approach. Environment and Planning B: Planning and Design 28:545–562.

    Article  Google Scholar 

  23. Egenhofer M., Golledge R.G. (1998) Spatial and temporal reasoning in geographic information systems. Oxford University Press, Oxford.

    Google Scholar 

  24. Fisher P. (1996) Boolean and fuzzy regions. In: Burrough P.A., Frank A. (Eds.) Geographic objects with indeterminate boundaries. Taylor and Francis, London, pp. 87–94.

    Google Scholar 

  25. Fisher P. (1997) Concepts and paradigms of spatial data. In: Craglia M., Couclelis H. (Eds.) Geographic Information Research: Bridging the Atlantic. Taylor & Francis, London, pp. 297–307.

    Google Scholar 

  26. Fisher P. (2001) Alternative set theories for uncertainty in spatial information. In: Hunsaker C., Goodchild M., Friedl M., Case T. (Eds.) Spatial Uncertainty in Ecology. Springer, New York, pp. 351–362.

    Chapter  Google Scholar 

  27. Foody G.M. (1996) Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data. International Journal of Remote Sensing 17(7) :1317–1340.

    Article  Google Scholar 

  28. Frank A.U. (1998) Different types of ‘times’ in GIS. In: Egenhofer M.J., Golledge R.G. (Eds.) Spatial and Temporal Reasoning in Geographical Information Systems. Oxford University Press, New York, pp. 40–62.

    Google Scholar 

  29. Hagerstrand T. (1970) What about people in regional science? Papers of the Regional Science Association 14(7):7–21.

    Google Scholar 

  30. Hall G.B., Wang F., Subaryono (1992) Comparison of Boolean and fuzzy classification methods in land suitability analysis using geographical information systems. Environment and Planning A 24:497–516.

    Article  Google Scholar 

  31. Heikkila E.J., Shen T.-Y., Yang K.-Z. (2003) Fuzzy urban sets: theory and application to desakota regions in China. Environment and Planning B: Planning and Design 30:239–254.

    Article  Google Scholar 

  32. Hornsby K., Egenhofer M., Hayes P. (1999) Modeling cyclic change. In: Chen P., Embley D., Koulondjan J., Liddle S., Reddinck J. (Eds.) Advances in Conceptual Modelling. Springer-Verlag Lecture Notes in Computer Science, Berlin, pp. 98–109.

    Chapter  Google Scholar 

  33. Kelmelis J.A. (1998) Process dynamics, temporal extent, and casual propagation as the basis for linking space and time. In: Egenhofer M.J., Golledge R.G. (Eds.) Spatial and Temporal Reasoning in Geographical Information Systems. Oxford University Press, New York, pp. 94–103.

    Google Scholar 

  34. Langran G. (1992) Time in geographic information systems. Taylor and Francis, London.

    Google Scholar 

  35. Langran G. (1993) Issues of implementing a spatiotemporal system. International Journal of Geographical Information Systems 7(4): 305–314.

    Article  Google Scholar 

  36. Langran G., Chrisman R. (1988) A framework for temporal geographic information. Cartographica 25(3):1–14.

    Article  Google Scholar 

  37. Leung Y. (1987) On the imprecision of boundaries. Geographical Analysis 19:125–151.

    Article  Google Scholar 

  38. Liu X.H., Andersson C. (2004) Assessing the impact of temporal dynamics on land-use change modeling. Computers, Environment and Urban Systems 18(1–2):107–124.

    Article  Google Scholar 

  39. Liu Y., Phinn R. (2003) Modelling urban development with cellular automata incorporating fuzzy-set approaches. Computers, Environment and Urban Systems 27(6): 637–658.

    Article  Google Scholar 

  40. Lodwick W.A., Santos J. (2003) Constructing consistent fuzzy surfaces from fuzzy data. Fuzzy Sets and Systems 135:259–277.

    Article  Google Scholar 

  41. Marceau D.J., Guindon L., Bruel M., Marois C. (2001) Building temporal topology in a GIS database to study the land-use changes in a rural-urban environment. Professional Geographer 53(4):546–558.

    Article  Google Scholar 

  42. McBratney A., Moore A. (1985) Application of fuzzy sets to climatic classification. Agricultural and Forest Meteorology 35:165–185.

    Article  Google Scholar 

  43. McBratney A., Odeh I. (1997) Application of fuzzy sets in soil sciences: Fuzzy logic, fuzzy measurements, and fuzzy decision. Geoderma 77:85–113.

    Article  Google Scholar 

  44. Pang M.Y.C., Shi W. (2002) Development of a process-based model for dynamic interaction in spatio-temporal GIS. Geo Informatica 6(4):323–344.

    Google Scholar 

  45. Parkes D., Thrift N. (1980). Times, spaces, and places. John WIley and Sons, New York.

    Google Scholar 

  46. Pawlak Z. (1982). Rough sets. International Journal of Computer and Information Sciences 11:241–356.

    Google Scholar 

  47. Peuquet D.J. (1994) Its about time: a conceptual framework for the representation of temporal dynamics in geographical information systems. Annals of the Association of American Geographers 84(3):441–461.

    Article  Google Scholar 

  48. Peuquet D.J., Duan N. (1995) An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data. International Journal of Geographical Information Systems 9(1):7–24.

    Article  Google Scholar 

  49. Peuquet D.J. (2001) Making space for time: issues in space-time data representation. Geo Informatica 5(1):11–32.

    Google Scholar 

  50. Peuquet D.J. (2002) Representations of Space and Time. The Guilford Press, New York.

    Google Scholar 

  51. Pfoser D., Tryfona N. (1999) Capturing fuzziness and uncertainty of spatiotemporal objects. In: Proc. of the 6th International Symposium on the Advances in Spatial Databases, Hong Kong, pp. 111–132.

    Chapter  Google Scholar 

  52. Robinson V.B. (1988) Some implications of fuzzy set theory applied to geographical databases. Computers, Environment and Urban Systems 12:89–97.

    Article  Google Scholar 

  53. Robinson V.B. (2003) A perspective on the fundamentals of fuzzy sets and their use in geographical information systems. Transitions in GIS 7(1):3–30.

    Article  Google Scholar 

  54. Roddick J., Patrick J. (1992) Temporal semantics in geographic information systems — a survey. Information Systems 17:249–267.

    Article  Google Scholar 

  55. Santos J., Lodwick W.A., Neumaier A. (2002) A new approach to incorporate uncertainty in terrain modeling. In: Egenhofer M.J., Mark D.M. (Eds.) Geographic Information Science: Second International Conference, GlScience 2002, Boulder, Colorado, USA, September 2002. Springer, Germany, pp. 291–299.

    Google Scholar 

  56. Schneider M. (1999) Uncertainty management for spatial data in databases: fuzzy spatial data types. In: Guting R.H., Papadias D., Lochovsky F. (Eds.) SSD’99. Springer-Verlag, Germany, pp. 330–351.

    Google Scholar 

  57. Shibasaki R., Huang S. (1996) Spatio-temporal interpolation by integrating observation data and a behavioural model. In: Kraak M., Molenaar M. (Eds.) SDH’96, Advances in GIS Research II. Taylor and Francis, Delft, The Netherlands, pp. 655–666.

    Google Scholar 

  58. Snodgrass R.T. (1992) Temporal databases. In: Frank A.U., Campari J., Fozmentini U. (Eds.) GIS — From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning. Theory and Methods of Spatio-Temporal Reasoning in Geographical Space, Pisa, Italy, pp. 22–64.

    Chapter  Google Scholar 

  59. Stead S.D. (1998) Temporal dynamics and geographical information systems. In: Egenhofer M.J., Golledge R.G. (Eds.) Spatial and Temporal Reasoning in GIS. Oxford University Press, New York, pp. 214–219.

    Google Scholar 

  60. Thrift N. (1977) An introduction to time geography. Concepts and Techniques in Modern Geography 3:3–36.

    Google Scholar 

  61. Vasilev I.R. (1997) Mapping Time. Cartographica 34(2):1–51.

    Article  Google Scholar 

  62. Wachowicz M. (1999) Object-oriented design for temporal GIS. Taylor and Francis, London.

    Google Scholar 

  63. Wachowicz M., Healey R.G. (1994) Towards temporality in GIS. In: Worboys M.F. (Ed.) Innovations in GIS. Taylor and Francis, UK, pp. 105–115.

    Google Scholar 

  64. Wang F. (1990) Improving remote sensing image analysis through fuzzy information representation. Photogrammetric Engineering and Remote Sensing 56(8):1163–1169.

    Google Scholar 

  65. Wang F. and Hall G.B. (1996) Fuzzy representation of geographical boundaries in GIS. International Journal of Geographical Information Systems 10(5):573–590.

    Google Scholar 

  66. Worboys M. (1994) A unified model for spatial and temporal information. The Computer Journal 37:26–33.

    Article  Google Scholar 

  67. Wu F. (1998) Simulating urban encroachment on rural land with fuzzy-logiccontrolled cellular automata in a geographical information system. Journal of Environmental Management 53:293–308.

    Article  Google Scholar 

  68. Yao Y. (1998) A comparative study of fuzzy sets and rough sets. Information Sciences 109:227–241.

    Article  Google Scholar 

  69. Yeh A.G.-O., Li X. (2002) A cellular automata model to simulate development density for urban planning. Environment and Planning B: Planning and Design 29:431–450.

    Article  Google Scholar 

  70. Zadeh L.A. (1965) Fuzzy Sets. Information and Control 8:338–353.

    Article  Google Scholar 

  71. Zhang J., Foody G.M. (1998) A fuzzy classification method of sub-urban land cover from remotely sensed imagery. International Journal of Remote Sensing 19:21–38.

    Article  Google Scholar 

  72. Zhang J., Stuart N. (2001) Fuzzy methods for categorical mapping with imagebased land cover data. International Journal of Geographical Information Science 15(2):175–195.

    Article  Google Scholar 

  73. Zhang W., Hunter G. (2000) Temporal interpolation of spatially dynamic objects. Geolnformatica 4(4):403–418.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Spatial Analysis and Modeling Laboratory, Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada

    Suzana Dragićević

Authors
  1. Suzana Dragićević
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Telecommunications and Information Processing, Computer Science Laboratory, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Rita de Caluwe  & Guy de Tré  & 

  2. CNR IDPA — Institute for Environmental Process Dynamics, Italian National Research Council, Via Pasubio 5, 24044, Dalmine (BG), Italy

    Gloria Bordogna

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dragićević, S. (2004). Fuzzy Sets for Representing the Spatial and Temporal Dimensions in GIS Databases. In: de Caluwe, R., de Tré, G., Bordogna, G. (eds) Spatio-Temporal Databases. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-09968-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-09968-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-06070-0

  • Online ISBN: 978-3-662-09968-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation