A Typology of Spatiotemporal Information Queries

  • May Yuan
  • John McIntosh
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 699)


This chapter presents the fundamentals of spatiotemporal information and queries that are central to understanding of the dynamic world. A typology of spatiotemporal information queries is developed to summarize distinct dimensionalities of inquired information in space and time. The typology distinguishes 11 query types: attribute queries, 3 types of spatial queries, 3 types of temporal queries, and 4 types of spatiotemporal queries. Mining spatiotemporal information requires functions to support automatic query for all 11 types of spatiotemporal queries to provide a full spectrum of information search for interesting patterns in space and time.

Key words

Spatiotemporal information and query types 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahl, V. and T. F. H. Allen (1996). Hierarchy Theory: A Vision, Vocabulary, and Epistemology. New York, Columbia University Press.Google Scholar
  2. Allen, J. F. (1983). Maintaining Knowledge about temporal intervals. Commun. ACSM 26(11): 832–843.zbMATHGoogle Scholar
  3. Allen, T. F. H. and T. B. Starr (1982). Hierarchy: Perspectives for Ecological Complexity. Chicago and London, The University of Chicago Press.Google Scholar
  4. Burough, P. A. and A. U. Frank, Eds. (1996). Geographic objects with indeterminate boundaries. GISDATA. London, Taylor & Francis.Google Scholar
  5. Couclelis, H. (1992). People manipulate objects (but cultivate fields): beyond the raster-vector debate in GIS. Theories and methods of spatio-temporal reasoning in geographic space. I. Campari, A. U. Frank and U. Formentini. Berlin, Springer Verlag.Google Scholar
  6. Downs, R. M. and D. Stea (1977). Maps in Minds: Reflections on Cognitive Mapping. New York, Harper and Row.Google Scholar
  7. Egenhofer, M. J. (1997). Query Processing in Spatial-Query-by-Sketch. Journal of Visual Languages and Computing 8(4): 403–424.CrossRefGoogle Scholar
  8. Egenhofer, M. J. and K. Al-Taha (1992). Reasoning about gradual changes of topological relationships. Theories and Methods of Spatio-Temporal Reasoning in Geographic Space. A. Frank, I. Campari and U. Formentin. New York, Springer-Verlag: 196–219.CrossRefGoogle Scholar
  9. Egenhofer, M. J. and R. Franzosa (1991). Point-set topological spatial relations. International journal of geographical information systems 5: 161–174.CrossRefGoogle Scholar
  10. Egenhofer, M. J. and J. Herring (1990). A mathematical framework for the definition of topological relationships. Fourth International Symposium on Spatial Data Handling, Columbus, International Geographical Union.Google Scholar
  11. Frank, A. (1998). Different Types of “Times” in GIS. Spatial and Temporal Reasoning in Geographic Information Systems. M. J. Egenhofer and R. G. Golledge. New York, Oxford University Press: 40–62.Google Scholar
  12. Freksa, C. (1992). Temporal reasoning based on semi-intervals. Artificial Intelligence 54: 199–227.MathSciNetCrossRefGoogle Scholar
  13. Gancarski, S. (1999). Database Versions to Represent Bitemporal Databases. Lecture notes in computer science: 832.Google Scholar
  14. Ittelson, W. H. (1973). Environment perception and contemporary perceptual theory. Environment and Cognition. W. H. Ittelson. New York, Seminar: 1–19.Google Scholar
  15. Kanellakis, P. C., S. Ramaswamy and D. E. Vengroff (1993). Indexing for data models with constraints and classes. ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems.Google Scholar
  16. Knuth, D. E. (1973). The art of computer programming, volume 1, Fundamental algorithms. Reading, MA, Addison-Wesley.Google Scholar
  17. Kumar, A., V. J. Tsotras and C. Faloutos (1998). Designing Access Methods for Bitemporal Databases. IEEE Transactions on Knowledge and data engineering. 10: 1 (20 pages).Google Scholar
  18. Langran, G. (1992). Time in Geographic information systems. London, Taylor & Francis.Google Scholar
  19. Langran, G. (1993). Manipulation and analysis of temporal geographic information. The Canadian conference on GIS, Ottawa.Google Scholar
  20. Lmielinski, T. and H. Mannila (1996). A database perspective on knowledge discovery. Communications of the ACM 39(11): 58–64.CrossRefGoogle Scholar
  21. Mandler, J. M. (1983). Representation. Handbook of Child Psychology. P. Mussen. New York, John Wiley and Sons. 3: 420–494.Google Scholar
  22. Mark, D. M. (1992). Spatial metaphors for human-computer interaction. Fifth International Symposium on Spatial Data Handling.Google Scholar
  23. Mark, D. M. and M. J. Egenhofer (1994). Modelling spatial relations between lines and regions: combining formal mathematical models and human subject testing. Cartography and Geographic Information Systems 21(4): 195–212.Google Scholar
  24. Mark, D. M. and S. M. Freundschuh (1995). Spatial Concepts and Cognitive Models for Geographic Information Use. Cognitive Aspects of Human-Computer Interaction for Geographic Information Systems.T. L. Nyerges, D. M. Mark, R. Laurini and M. Egenhofer. Dordrecht, Kluwer Academic Publishers: 21–28.CrossRefGoogle Scholar
  25. Montello, D. (1993). Scale and Multiple Psychologies of Space. Spatial Information Theory: A Theoretical Basis for GIS. A. U. Frank and I. Campari. Berlin, Springer-Verlag: 312–321.CrossRefGoogle Scholar
  26. Ooi, B. C., C. H. Goh and K.-L. Tan (1998). Indexing bitemporal databases as points. Information and software technology. 40: 327 (12 pages).Google Scholar
  27. Peuquet, D. J. (1994). It’s about time: a conceptual framework for the representation of temporal dynamics in geographic information systems. Annals of the Association of American Geographers 84(3): 441–462.CrossRefGoogle Scholar
  28. Pinxten, R., I. van Dooren and F. Harvey (1983). Anthropology of Space. Philadelphia, University of Pennsylvania Press.Google Scholar
  29. Rigaux, P., M. Scholl and A. Voisard (2002). Spatial databases with application to GIS. San Francisco, Morgan Kaufmann Publishers.Google Scholar
  30. Salthe, S. N. (1991). Two Forms of Hierarchy Theory in Western Discourses. International Journal of General Systems 18: 251–264.CrossRefGoogle Scholar
  31. Samet, H. (1989). The design and analysis of spatial data structure. Reading, Addison-Wesley Publishing Company, Inc.Google Scholar
  32. Snodgrass, R. and I. Ahn (1986). Temporal databases. IEEE Computer, September: 35–42.Google Scholar
  33. Tansel, A. U., J. Clifford, S. Gadia, S. Jajodia, A. Segev and R. Snodgrass, Eds. (1993). Temporal Databases: Theory, Design, and Implementation. Reading, MA, The Benjamin/Cummings Publishing Company, Inc.Google Scholar
  34. Tsotras, V., C. Jensen and R. Snodgrass (1998). An Extensible Notation for Spatiotemporal Index Queries. SIGMOD record 27(1): 47–56.CrossRefGoogle Scholar
  35. Verma, R. M. and J. H. Vaishnav (1997). An efficient multiversion access structure. IEEE Transactions on Knowledge and data engineering 9(3): 391–409.CrossRefGoogle Scholar
  36. Verma, R. M. and P. J. Varman (1994). Efficient archivalable time index: a dynamic indexing scheme for temporal data. International conference on computer systems and education.Google Scholar
  37. Willis, K. J. and R. J. Whittaker (2002). Species Diversity — Scale Matters. Science 295(5558): 1245–1249.CrossRefGoogle Scholar
  38. Worboys, M. F. (1994). A Unified Model of Spatial and Temporal Information. Computer Journal 37(1): 26–34.CrossRefGoogle Scholar
  39. Yuan, M. (1996). Modelling semantical, temporal, and spatial information in geographic information systems. Geographic Information Research: Bridging the Atlantic. M. Craglia and H. Couclelis. Lodon, Taylor & Francis: 334–347.Google Scholar
  40. Yuan, M. (1997). Knowledge acquisition for building wildfire representation in Geographic Information Systems. The International Journal of Geographic Information Science 11(8): 723–745.CrossRefGoogle Scholar
  41. Yuan, M. (1999). Use of a Three-Domain Representation to Enhance GIS Support for Complex Spatiotemporal Queries. Transactions in GIS 3, no. 2: 137–159.CrossRefGoogle Scholar
  42. Yuan, M. (2000). Modeling geographic information to support spatiotemporal queries. Life and Motion of Socio-Economic Units. A. U. Frank, J. Raper and J. P. Cheyland. London, Taylor and Francis.Google Scholar
  43. Yuan, M. (2001). Representing Complex Geographic Phenomena with both Object- and Fieldlike Properties. Cartography and Geographic Information Science 28(2): 83–96.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • May Yuan
    • 1
  • John McIntosh
    • 1
  1. 1.Department of GeographyThe University of OklahomaOklahomaUSA

Personalised recommendations