An application of fuzzy logic reasoning for GIS temporal modeling of dynamic processes

Fuzzy Sets and Systems

Volumn 113, Issue 1, 2000, Pages 69-80

  Dragicevic, Suzana ^a   Marceau, Danielle J ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Dynamic process; Fuzzy logic; Spatio temporal interpolation; Temporal GIS modeling

Indexed keywords

COMPUTER SIMULATION; DATABASE SYSTEMS; GEOGRAPHIC INFORMATION SYSTEMS; INTERPOLATION; LAND USE; MATHEMATICAL MODELS; RURAL AREAS; URBAN PLANNING;

FUZZY LOGIC REASONING; SPATIO-TEMPORAL INTERPOLATION;

FUZZY SETS;

EID: 0033748884     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(99)00013-5     Document Type: Article

References (24)

1. Acevedo W., Masuoka P. Time-series animation techniques for visualizing urban growth. Comput. Geosci. 4:1997;423-435.
2. Allen P.M. Cities and regions as evolutionary, complex systems. Geograph. Systems. 4:1997;103-130.
3. Batty M., Xie Y. From cells to cities. Envir. Plann. B: Plann. Des. 21:1994;531-548.
4. Bell E.J. Stochastic analysis of urban development. Envir. Plann. A. 7:1975;35-39.
5. Bourne L.S. Monitoring change and evaluating the impact of planning policy of urban structure: a Markov chain experiment. Plan Canada. 16:1976;5-14.
6. Burrough P.A. Fuzzy mathematical methods for soil survey and land evaluation. J. Soil Sci. 40:1989;477-492.
7. Chrisman N. Beyond the snapshot. changing the approach to change, error, and process Egenhofer M.J., Golledge R.G. Spatial and Temporal Reasoning in Geographic Information Systems. 1998;85-93 Oxford University Press, Oxford.
8. Clarke K.C., Hoppen S., Gaydos L. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Envir. Plann. B: Plann. Des. 24:1997;247-267.
9. W.P.A. Deursen, Geographical Information Systems and Dynamic Models, Netherlands Geographical Studies, vol. 190, Utrecht, 1995.
10. S. Dragicevic, D.J. Marceau, Fuzzy logic in spatio-temporal GIS database, Proc. Ann. Conf. GIS/LIS'97, Cincinnati, Ohio, October 28-30, 1997.
11. S. Dragicevic, D.J. Marceau, Spatio-temporal interpolation and fuzzy logic reasoning for GIS simulation of rural-urban transition, Cartography Geograph. Inform. Systems, in press.
12. Kelmelis J.A. Process Dynamics, Temporal Extent, and Causal Propagation as the Basis for Linking Space and Time. Egenhofer M.J., Golledge R.G. Spatial and Temporal Reasoning in Geographic Information Systems. 1998;94-103 Oxford University Press, Oxford.
13. Kollias V.J., Voliotis A. Fuzzy reasoning in the development of geographical information systems FRSIS: a prototype soil information system with fuzzy retrieval capabilities. Internat. J. Geograph. Inform. Systems. 2:1991;209-223.
14. Landis J.D. The California urban futures model: a new generation of metropolitan simulation models. Envir. Plann. B: Plann. Des. 21:1994;399-420.
15. Langran G., Chrisman N.R. A framework for temporal geographic information. Cartographica. 3:1988;1-14.
16. Méaille R., Wald L. Using geographical information system and satellite imagery within a numerical simulation of regional urban growth. Internat. J. Geograph. Inform. Systems. 4:1990;445-456.
17. Mitas L., Brown W.B., Mitasova H. Role of dynamic cartography in simulations of landscape processes based on multivariate fields. Comput. Geosci. 4:1997;437-446.
18. Robinson V.B. Information theory and sequences of land use: an application. Professional Geographer. 2:1978;174-179.
19. Robinson V.B. On the use of markovian equilibrium distributions for land use policy evaluation. J. Socio-Econom. Plann. Sci. 14:1980;85-89.
20. Robinson V.B. Some implications of fuzzy set theory applied to geographic databases. Comput. Envir. Urban Systems. 12:1988;89-97.
21. STATISTICS CANADA, Census of Canada, 1951-1986.
22. U.S. Army Cerl, GRASS4.1 User's Manual, 1993.
23. Zadeh L.A. Fuzzy sets. Inform. and Control. 8:1965;338-353.
24. Zadeh L.A. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems. 1:1978;3-28.