Photis, Yorgos N. and Manetos, Panos and Grekoussis, George (2003): Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece. Published in: Proceedings of the 3rd Euroconference: The European City in Transition , Vol. 1, No. 1 (29. November 2003): pp. 96-108.
Download (202Kb) | Preview
While during the past decades, urban areas experience constant slow population growth, the spatial patterns they form, by means of their limits and borders, are rapidly changing in a complex way. Furthermore, urban areas continue to expand to the expense of "rural” intensifying urban sprawl. The main aim of this paper is the definition of the evolution of urban areas and more specifically, the specification of an urban model, which deals simultaneously with the modification of population and building use patterns. Classical theories define city geographic border, with the Aristotelian division of 0 or 1 and are called fiat geographic boundaries. But the edge of a city and the urbanization "degree" is something not easily distinguishable. Actually, the line that city ends and rural starts is vague. In this respect a synthetic spatio - temporal methodology is described which, through the adaptation of different computational methods aims to assist planners and decision makers to gain an insight in urban - rural transition. Fuzzy Logic and Neural Networks are recruited to provide a precise image of spatial entities, further exploited in a twofold way. First for analysis and interpretation of up - to - date urban evolution and second, for the formulation of a robust spatial simulation model, the theoretical background of which is that the spatial contiguity between members of the same or different groups is one of the key factors in their evolution. The paper finally presents the results of the model application in the prefecture of Attica in Greece, unveiling the role of the Athens Metropolitan Area to its current and future evolution, by illustrating maps of urban growth dynamics.
|Item Type:||MPRA Paper|
|Original Title:||Modeling urban evolution by identifying spatiotemporal patterns and applying methods of artificial intelligence.Case study: Athens, Greece.|
|Keywords:||urban growth; urban dynamics; neural networks; fuzzy logic; Greece; Athens|
|Subjects:||O - Economic Development, Technological Change, and Growth > O1 - Economic Development > O18 - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics
|Depositing User:||Yorgos N. Photis|
|Date Deposited:||22. Feb 2010 11:27|
|Last Modified:||16. Feb 2013 20:18|
Feng, S., Flowerdew, R. (1998), Fuzzy geodemographics: a contribution from fuzzy clustering Methods, in “Innovations in GIS: Innovations in GIS 5”, (Selected Papers from the fifth National conference on GIS Research UK), Taylor & Francis, UK.
Feng, S., Xu L. (1999), "An Intelligent Decision Support System for Fuzzy Comprehensive Evaluation of Urban Development", Expert systems with applications, 16 (1999) 21-32, Elsevier Science.
Haykin, S. (1999), Neural Networks: A comprehensive foundation, Prentice-Hall, New Jersey.
Hewitson, B. Crane, R. (1994), Neural Nets: Application in Geography, Kluwer Academic Publishers, Netherlands.
Kosko, B. (1997), Fuzzy Thinking: The New Science of Fuzzy Logic, Prentice Hall, New Jersey.
Kropp, J. (1998), "A Neural Network Approach to the Analysis of City Systems", Applied Geography, Vol. 18, No 1, pp. 83-96, Elsevier Science Ltd.
Leontidou, L. (1996), "Alternatives to modernism in (southern) urban theory: Exploring in-between spaces", International Journal of Urban and Regional Research, 20/2, 178-195.
Openshaw, S. (1989), “Making Geodemographics more sophisticated”, Journal Market Research Society, 31.
Openshaw, S. (1997), Artificial Intelligence in Geography, John Wiley & Son Ltd, London.
Principe, J.C., Euliano, N.R., Lefebvre, W.C. (2000), Neural and Adaptive Systems: Fundamentals Through Simulations, John Wiley & Sons, Inc.
Rocky Mountain Institute (1998), Green development: integrating ecology and real estate, John Wiley and Sons, New York.
Visvalingam, M. (1991), Areal units and the linking of data: some conceptual issues, in Worrall Les “Spatial Analysis andSpatial Policy using Geographic Information Systems”, Belhaven Press, London.