Urban Growth Modelling Using Determinism and Stochasticity in a Touristic Village in Western Greece

Abstract

Urban development has acquired an important magnitude in touristic places in Greece. Many villages, especially in seaside areas have adapted to touristic requirements by the necessary infrastructures and activities. Pogonia, located in Vonitsa Etoloakarnanias, is a village which has welcomed the opportunity of touristic development. As a result, the house settlements increased 57.5% during the last 8 years. Urban growth modelling using Artificial Neural Networks (ANNs) was applied in order to simulate the urban development in Pogonia village using two methods: determinism and stochasticity. The variables used for deterministic simulation were: distances to roads, urban areas and coastline, slope and elevation. It was found that urban development can be better described using the network of distances between all urban settlements (stochastic approach) rather than using determinism. This can be explained by the importance of the neighbourhood relationships and the interaction between urban settlements, occurred within the interconnected network of the self-organized urban system.

Share and Cite:

D. Triantakonstantis, "Urban Growth Modelling Using Determinism and Stochasticity in a Touristic Village in Western Greece," Open Journal of Civil Engineering, Vol. 2 No. 1, 2012, pp. 42-48. doi: 10.4236/ojce.2012.21007.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] L. Poelmans and V. A. Rompaey, “Complexity and Performance of Urban Expansion Models,” Computers, Environment and Urban Systems, Vol. 34, No. 1, 2010, pp. 17-27. doi:10.1016/j.compenvurbsys.2009.06.001
[2] UNFPA, 2007. http://www.unfpa.org/public/
[3] X. Li and A. G. Yeh, “Analyzing Spatial Restructuring of Land Use Patterns in a Fast Growing Region Using Remote Sensing and GIS,” Landscape and Urban Planning, Vol. 69, No. 4, 2004, pp. 335-354. doi:10.1016/j.landurbplan.2003.10.033
[4] M. Herold, H. Couclelis and K. C. Clarke, “The Role of Spatial Metrics in the Analysis and Modeling of Urban Land Use Change,” Computers, Environment and Urban Systems, Vol. 29, No. 4, 2005, pp. 369-399. doi:10.1016/j.compenvurbsys.2003.12.001
[5] K. McGarigal, S. Tagil and S. Cushman, “Surface Metrics: An Alternative to Patch Metrics for the Quantification of Landscape Structure,” Landscape Ecology, Vol. 24, No. 3, 2009, pp. 433-450. doi:10.1007/s10980-009-9327-y
[6] M. Deng, “A Spatially Autocorrelated Weights of Evidence Model,” Natural Resources Research, Vol. 19, No. 1, 2010, pp. 33-44. doi:10.1007/s11053-009-9107-z
[7] J. Liu and W. W. Taylor, “Integrating Landscape Ecology into Natural Resource Management,” Cambridge University Press, Cambridge, 2002.
[8] A. Paez and D. Scott, “Spatial Statistics for Urban Analysis: A Review of Techniques with Examples,” GeoJournal, Vol. 61, No. 1, 2005, pp. 53-67. doi:10.1007/s10708-005-0877-5
[9] D. Triantakonstantis, G. Mountrakis and J. Wang, “A Spatially Heterogeneous Expert Based (SHEB) Urban Growth Model Using Model Regionalization,” Journal of Geographic Information System,Vol. 3, No. 3, 2011, pp. 195-210. doi:10.4236/jgis.2011.33016
[10] I. Santé, A. M. García, D. Miranda and R. Crecente, “Cellular Automata Models for the Simulation of Real- World Urban Processes: A Review and Analysis,” Landscape and Urban Planning, Vol. 96, No. 2, 2010, pp. 108- 122. doi:10.1016/j.landurbplan.2010.03.001
[11] R. White, G. Engelen and I. Uljee, “The Use of Constrained Cellular Automata for High-Resolution Modelling of Urban Land-Use Dynamics,” Environment and Planning B: Planning and Design, Vol. 24, No. 3, 1997, pp. 323-343. doi:10.1068/b240323
[12] J. I. Barredo, M. Kasanko, N. McCormick and C. Lavalle, “Modelling Dynamic Spatial Processes: Simulation of Urban Future Scenarios through Cellular Automata,” Landscape and Urban Planning, Vol. 64, No. 3, 2003, pp. 145-160. doi:10.1016/S0169-2046(02)00218-9
[13] A. D. Syphard, K. C. Clarke and J. Franklin, “Using a Cellular Automaton Model to Forecast the Effects of Urban Growth on Habitat Pattern in Southern California,” Ecological Complexity, Vol. 2, No. 2, 2005, pp. 185-203. doi:10.1016/j.ecocom.2004.11.003
[14] J. Vliet, R. White and S. Dragicevic, “Modeling Urban Growth Using a Variable Grid Cellular Automaton,” Computers, Environment and Urban Systems, Vol. 33, No. 1, 2009, pp. 35-43. doi:10.1016/j.compenvurbsys.2008.06.006
[15] J. R. Quinlan, “Probabilistic Decision Tress,” In: K. Yves and R. Michalski, Eds., Machine Learning: An Artificial Intelligence Approach, Volume III, San Mateo, Morgan Kaufmann, 1983, pp. 140-152.
[16] K. M. Osei-Bryson, “Post-Pruning in Decision Tree Induction Using Multiple Performance Measures,” Computers & Operations Research, Vol. 34, No. 11, 2007, pp. 3331-3345. doi:10.1016/j.cor.2005.12.009
[17] W. Cheng, K. Wang and X. Zhang, “Implementation of a COM-Based Decision-Tree Model with VBA in Arc GIS,” Expert Systems with Applications, Vol. 37, No. 1, 2010, pp. 12-17. doi:10.1016/j.eswa.2009.01.006
[18] J. A. F. Diniz-Filho, L. M. Bini and B. A. Hawkins, “Spatial Autocorrelation and Red Herrings in Geographical Ecology,” Global Ecology and Biogeography, Vol. 12, No. 1, 2003, pp. 53-64. doi:10.1046/j.1466-822X.2003.00322.x
[19] B. Lees, “The Spatial Analysis of Spectral Data: Extracting the Neglected Data,” Applied GIS, Vol. 2, No. 2, 2006, pp. 14.1-14.13.
[20] F. E. Nelson, K. M. Hinkel, N. I. Shiklomanov, G. R. Mueller, L. L. Miller and D. A. Walker, “Active-Layer Thickness in North Central Alaska: Systematic Sampling, Scale, and Spatial Autocorrelation,” Journal of Geophysical Research, Vol. 103, No. D22, 1998, pp. 28963- 28973. doi:10.1029/98JD00534
[21] B. C. Pijanowski, D. G. Brown, B. A. Shellito and G. A. Manik, “Using Neural Networks and Gis to Forecast Land Use Changes: A Land Transformation Model,” Computers, Environment and Urban Systems, Vol. 26, No. 6, 2002, pp. 553-575. doi:10.1016/S0198-9715(01)00015-1
[22] W. Liu and K. C. Seto, “Using the ART-MMAP Neural Network to Model and Predict Urban Growth: A Spatiotemporal Data Mining Approach,” Environment and Planning B: Planning and Design, Vol. 35 No. 2, 2008, pp. 296-317. doi:10.1068/b3312
[23] C. M. Almeida, J. M. Gleriani, E. F. Castejon and B. S. Soares-Filho, “Using Neural Networks and Cellular Automata for Modelling Intra-Urban Land-Use Dynamics,” International Journal of Geographical Information Science, Vol. 22, No. 9, 2008, pp. 943-963. doi:10.1080/13658810701731168
[24] Y. Mahajan and P. Venkatachalam, “Neural Network Based Cellular Automata Model for Dynamic Spatial Modeling in GIS,” Springer, Berlin/Heidelberg, 2009, pp. 341-352.
[25] B. Pijanowski, S. Pithadia, B. Shellito and A. Alexandridis, “Calibrating a Neural Network-Based Urban Change Model for Two Metropolitan Areas of the Upper Midwest of the United States,” International Journal of Geographical Information Science, Vol. 19, No. 2, 2005, pp. 197- 215. doi:10.1080/13658810410001713416
[26] Q. Guan, L. Wang and K. C. Clarke, “An Artificial-Neural-Network-Based, Constrained CA Model for Simulating Urban Growth,” Cartography and Geographic Information Science, Vol. 32, No. 4, 2005, pp. 369-380. doi:10.1559/152304005775194746
[27] M. Batty and P. A. Longley, “Fractal Cities: A Geometry of Form and Function,” Academic Press, London, 1994.
[28] L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, 1965, pp. 338-353. doi:10.1016/S0019-9958(65)90241-X

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.