Space, time, and dynamics modeling in historical GIS databases: a fuzzy logic approach (original) (raw)

2001, Environment and Planning B

Abstract

Context and objectives Space and time are apparently well-known notions but, in order to explain them, they must be connected to other fundamental concepts such as change or process (Hazelton et al, 1992). As stated by Blaut (1961), since relative space is inseparably fused to relative time, nothing in the physical world is purely spatial or temporal; everything is process. Change must be seen as a composite of processes that occur on a wide band of timescale in space. Therefore, the link between space and time is through the process itself, where specific processes determine specific temporal and spatial conceptualization (Chrisman, 1998; Frank, 1998). With the powerful growth of computer capabilities, geographical information systems (GIS) are becoming a new geographical tool capable of dealing with processes by using space and time components of geographical dynamic data. The issue of time in GIS usually involves the question of how to store and manipulate temporal information with spatial information in a digital database (Vasiliev, 1998). The incorporation of the time component in GIS and the ability to reason about change are strongly influenced by three major factors. The first factor is the cartographic influence on the GIS representation of geographic data. Cartographers usually maintain the representation of the continually changing world by making static maps. This practice has been transferred from the analog to the digital world, and the data structures in use today are designed for, and limited to, the static GIS representation of dynamic geographic phenomena (Langran and Chrisman, 1988; Peuquet, 1994). Therefore, the time component in a GIS database remains just an attribute of space. The second factor stems from the nature of historical data. The available data are typically nonideal because data volumes may be large, coming from different sources, expressed using different spatial and temporal scales, and finally determined by circumstances that are, possibly beyond the analysts' control (Openshaw, 1994; Vrana, 1989).

Loading...

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

References (52)

  1. Allen P M, 1997, ``Cities and regions as evolutionary, complex systems'' Geographical Systems 4 103 ^130
  2. Batty M, Xie Y, 1994, ``From cells to cities'' Environment and Planning B: Planning and Design 21 s31 ^s48
  3. Blaut J M, 1961, ``Space and process'' The Professional Geographer 13(4) 1 ^7
  4. Burrough P A, 1989, ``Fuzzy mathematical methods for soil survey and land evaluation'' Journal of Soil Science 40 477 ^492
  5. Burrough P A, MacMillan R A, van Deursen W, 1992, ``Fuzzy classification methods for determining land suitability from soil profile observations and topography'' Journal of Soil Science 43 193 ^210
  6. Campari I, 1998, ``Analyzing temporal factors in urban morphology development'', in Spatial and Temporal Reasoning in Geographic Information Systems Eds M J Egenhofer, R G Golledge (Oxford University Press, New York) pp 220 ^237
  7. Chrisman N, 1998, ``Beyond the snapshot: changing the approach to change, error, and process'', in Spatial and Temporal Reasoning in Geographic Information Systems Eds M J Egenhofer, R G Golledge (Oxford University Press, New York) pp 85 ^93
  8. Clark S C, Starr J, Foresman T, Acevedo W, Solomon C, 1996, ``Development of the temporal transportation database for the analysis of urban development in the Baltimore Ŵashington region'' Proceedings ASPRS/ACSM Annual Convention and Exhibition 3 101 110
  9. Clarke K C, Gaydos L J, 1998, ``Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore'' International Journal of Geographical Information Science 7 699 ^714
  10. Clarke K C, Hoppen S, Gaydos L, 1997, ``A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area'' Environment and Planning B: Planning and Design 24 247 ^261
  11. 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 4 369 ^384
  12. Dragicevic S, Marceau D J, 1999, ``Spatio-temporal interpolation and fuzzy logic reasoning for GIS simulation of rural-urban transition'' Cartography and Geographic Information Systems 2 125 ^137
  13. Dragicevic 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
  14. Dubois D, Prade H, 1980 Fuzzy Sets and Systems: Theory and Applications (Academic Press, San Diego, CA)
  15. Frank A U, 1998, ``Different types of `Times' in GIS'', in Spatial and Temporal Reasoning in Geographic Information Systems Eds M J Egenhofer, R G Golledge (Oxford University Press, New York) pp 40 ^62
  16. Hall G B, Wang F, Subaryono, 1992, ``Comparison of Boolean and fuzzy classification methods in land suitability analysis by using geographical information systems'' Environment and Planning A 24 497 ^516
  17. Hazelton N W J, Leahy F J, Williamson I P, 1992, ``Integrating dynamic modeling and geographic information systems'' Journal of the Urban and Regional Information Systems Association 2 47 ^58
  18. Heuvelink G, Burrough P, Lenaers H, 1990, ``Error propagation in spatial modeling with GIS'', in Proceedings of European Conference on Geographic Information Systems Amsterdam, the Netherlands, 10 ^13 April, pages 453 ^462
  19. Kemp Z, Kowalczyk A, 1994, ``Incorporating the temporal dimension in a GIS'', in Innovations in GIS Ed. M F Worboys (Taylor and Francis, London) pp 89 ^103
  20. Kirtland D, Gaydos L, Clarke K, DeCola L, Johnson C B, 1994, ``An analysis of human-induced land transformations in the San Francisco Bay/Sacramento area'' World Resource Review 2 206 ^217
  21. Kollias V J, Voliotis A, 1991, ``Fuzzy reasoning in the development of geographical information systems FRSIS: a prototype soil information system with fuzzy retrieval capabilities'' International Journal of Geographical Information Systems 2 209 ^223
  22. Landis J D, 1994, ``The California Urban Futures Model: a new generation of metropolitan simulation models'' Environment and Planning B: Planning and Design 21 399 ^420
  23. Langran G, 1993, ``Issues of implementing a spatiotemporal system'' International Journal of Geographical Information Systems 4 305 ^314
  24. Langran G, Chrisman N R, 1988, ``A framework for temporal geographic information'' Cartographica 3 1 ^14
  25. Li X, Yeh A G, 2000, ``Modelling sustainable urban development by the integration of constrained cellular automata and GIS'' International of Geographical Information Systems 2 131 ^152
  26. Marceau D J, Dragicevic S, 1998, ``Linking space and time to model dynamics of change in a rural- urban environment'' Proceedings of The Modeling of Complex Systems Conference New Orleans, LA, 12 ^17 July
  27. Marois C, 1998, ``La population montre¨alaise'' [The Montreal population] in Montreal 2001: Visage et de¨fis d'une me¨tropole Eds C Manzagol, C Bryant (Les Presses de l'Universite¨de Montre¨al, Montre¨al) pp 83 ^97
  28. Mayer D G, Butler D G, 1993, `Statistical validation'' Ecological Modeling 68 21 ^32
  29. Openshaw S, 1994, ``Two exploratory space ^time-attribute pattern analysers relevant to GIS'', in Spatial Analysis and GIS Eds S Fotheringham, P Rogerson (Taylor and Francis, London) pp 83 ^104
  30. 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 3 441 ^461
  31. Phipps M, Langlois A, 1997, ``Spatial dynamics, cellular automata, and parallel processing computers'' Environment and Planning B: Planning and Design 24 193 ^204
  32. Portugali J, Benenson I, 1995, ``Artificial planning experience by means of a heuristic cell-space model: simulating international migration in the urban process'' Environment and Planning A 27 1647 ^1665
  33. Power M, 1993, ``The predictive validation of ecological and environmental models'' Ecological Modeling 68 33 ^50
  34. Rykiel E J, 1996, ``Testing ecological models: the meaning of validation'' Ecological Modeling 68 229 ^244
  35. Schlagel J D, Newton C M, 1996, ``A GIS-based statistical method to analyze spatial change'' Photogrammetric Engineering and Remote Sensing 7 839 ^844
  36. Snodgrass R T, 1992, ``Temporal databases'', in Theory and Methods of Spatio-Temporal Reasoning in Geographic Space Eds A U Frank, I Campari, U Formentini (Springer, Berlin) pp 22 ^64
  37. Statistics Canada, 1951 ^86 Census Data Statistics Canada Regional Reference Center, 200 ReneL evesque Boulevard West, Montreal, Quebec H2Z 1X4, Canada
  38. Stead S D, 1998,``Temporal dynamics and geographic information systems'', in Spatial and Temporal Reasoning in Geographic Information Systems Eds M J Egenhofer, R G Golledge (Oxford University Press, New York) pp 214 ^219
  39. Sui D Z, 1992,``A fuzzy GIS modeling approach for urban land evaluation'' Computers, Environment and Urban Systems 16 101 ^115
  40. Terano T, 1992 Fuzzy Systems Theory and its Applications (Academic Press, Boston, MA)
  41. Thapa K, Bossler J, 1992, ``Accuracy of spatial data used in geographic information systems'' Photogrammetric Engineering and Remote Sensing 6 835 ^641
  42. US Army Cerl, 1993 GRASS4.1 User's Manual Grass Support Center, DBA Systems Inc, Fairfax, VA
  43. Vasiliev I R, 1998, ``Mapping time'' Cartographica monograph 49, page 51
  44. Vrana R, 1989,``Historical data as an explicit component of land information systems'' International Journal of Geographical Information Systems 1 33 ^49
  45. Wang F, Hall B G, 1996, ``Fuzzy representation of geographical boundaries in GIS'' International Journal of Geographical Information Systems 5 573 ^590
  46. White R, Engelen G, 1997, ``Cellular automata as the basis of integrated dynamic regional modelling'' Environment and Planning B: Planning and Design 24 235 ^246
  47. White R, Engelen G, Uljee I, 1997, ``The use of constrained cellular automata for high-resolution modelling of urban land-use dynamics''Environment and Planning B: Planning and Design 24 323 ^343
  48. Wu F, Webster C J, 1998, ``Simulation of land development through the integration of cellular automata and multicriteria evaluation'' Environment and Planning B: Planning and Design 25 103 ^126
  49. Xie Y, Batty M, 1997, ``Automata-based exploration of emergent urban form'' Geographical Systems 4 83 ^102
  50. Zadeh L A, 1965, ``Fuzzy sets'' Information and Control 8 338 ^353
  51. Zadeh L A, 1978, ``Fuzzy sets as a basis for a theory of possibility'' Fuzzy Sets and Systems 1 3 ^28
  52. Zimmermann H-J, 1985 Fuzzy Set Theory and Its Applications (Kluwer, Dordrecht) ß 2001 a Pion publication printed in Great Britain