Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31203
Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks

Authors: D. Triantakonstantis, D. Stathakis


Urban areas have been expanded throughout the globe. Monitoring and modelling urban growth have become a necessity for a sustainable urban planning and decision making. Urban prediction models are important tools for analyzing the causes and consequences of urban land use dynamics. The objective of this research paper is to analyze and model the urban change, which has been occurred from 1990 to 2000 using CORINE land cover maps. The model was developed using drivers of urban changes (such as road distance, slope, etc.) under an Artificial Neural Network modelling approach. Validation was achieved using a prediction map for 2006 which was compared with a real map of Urban Atlas of 2006. The accuracy produced a Kappa index of agreement of 0,639 and a value of Cramer's V of 0,648. These encouraging results indicate the importance of the developed urban growth prediction model which using a set of available common biophysical drivers could serve as a management tool for the assessment of urban change.

Keywords: Artificial Neural Networks, CORINE, urban atlas, urban growth prediction

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2931


[1] A. J. Njoh,“Urbanization and development in sub-Saharan Africa,” Cities, vol. 20, pp. 167–174, 2003.
[2] D. E. Bloom, D. Canning, and G. Fink, “Urbanization and the wealth of nations,” Science, vol. 319, pp. 772–775, 2008.
[3] R. H. Ewing, “Characteristics, Causes and Effects of Sprawl: A Literature Review,” Urban Ecology, pp. 519-535, 2008.
[4] CEC, Cohesion Policy and Cities: the urban contribution to growth and jobs in the regions, Communication from the Commission to the council and parliament, COM(2006) 385, final, Brussels, Belgium, July 13th 2006.
[5] CEC, Territorial Agenda of the European Union 2020: towards an inclusive, smart and sustainable Europe of diverge regions, Hungary, May 19th 2011.
[6] A. Frankel, and M. Ashkenazi, “Measuring urban sprawl: how can we deal with it?,” Environment and Planning B: Planning and Design, vol. 35, pp. 56-79, 2008.
[7] C. Agarwal, G. M. Green, J. M. Grove, T. P. Evans, and C. M. Schweik, “A Review and Assessment of Land-Use Change Models: Dynamics of Space, Time, and Human Choice,” Apollo the International Magazine of Art and Antiques, vol. 1, no. 1, p. 61, 2002.
[8] P. M. Torrens, and D. O’Sullivan, “Cellular Automata and Urban Simulation: Where Do We Go from Here?,” Environment and Planning B: Planning and Design, vol. 28, no. 2, pp. 163-168, 2001.
[9] R. Schaldach, and J. A. Priess, “Integrated Models of the Land System: A Review of Modelling Approaches on the Regional to Global Scale,” Living Reviews in Landscape Research, vol. 2, no. 1, 34 p., 2008.
[10] D. Haase, and N. Schwarz, “Simulation Models on Hu- man—Nature Interactions in Urban Landscapes: A Review Including Spatial Economics, System Dynamics, Cellular Automata and Agent-Based Approaches,” Living Reviews in Landscape Research, vol. 3, no. 2, 2009.
[11] D. Triantakonstantis, and G. Mountrakis, “Urban Growth Prediction: A Review of Computational Models and Human Perceptions,” Journal of Geographical Information Systems, vol. 4, no. 6, pp. 555-587, 2012.
[12] D. Rumelhart, G. Hinton, and R. Williams, “Learning Internal Representations by Error Propagation,” In: D. E. Rumelhart and J. L. McClelland, ed., Parallel Distributed Processing: Explorations in the Microstructures of Cognition, MIT Press, Cambridge, 1986, pp. 318- 362.
[13] W. Liu, and K. C. Seto, “Using the ART-MMAP Neural Network to Model and Predict Urban Growth: A Spatio-temporal Data Mining Approach,” Environment and Planning B: Planning and Design, vol. 35, no. 2, pp. 296-317, 2008.
[14] K. Clarke, S. Hoppen, and L. Gaydos, “A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area,” Environment and Planning B: Planning and Design, vol. 24, pp. 247–261, 1997.
[15] K. C. Clarke, and L. J. Gaydos, “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, vol. 12, pp. 699–714, 1998.
[16] S. Feng, and L. Xu, “An Intelligent Decision Support System for Fuzzy Comprehensive Evaluation of Urban Development,” Expert System with Applications, vol. 16, no. 1, pp. 21-32, 1999.
[17] L. A. Díaz-Robles, J. C. Ortega, J. S. Fu, G. D. Reed, and J. C. Chow, “A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Particulate Matter in Urban Areas: The Case of Temuco, Chile,” Atmospheric Environment, vol. 42, no. 35, pp. 8331-8340, 2008.
[18] S. Lee, and R. G. Lathrop, “Subpixel Analysis of Landsat ETM+ Using Self-Organizing Map (SOM) Neural Networks for Urban Land Cover Characterization,” IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, pp. 1642-1654, 2006.
[19] C. Lavalle, L. Demicheli and M. Kasanko, N. McCormick, J. Barredo, M. Turchini,M. da GraçaSaraiva, F. N. da Silva,I. L. Ramos, F. P. Monteiro,I. P. Martins, “Towards an urban atlas. Assessment of spatial data on 25 European cities and urban areas,” Environmental issue report No 30. European Environment Agency, Copenhagen, Denmark, 2002.
[20] P. Prastacos, N. Chrysoulakis, and G. Kochilakis, “Urban Atlas, land use modelling and spatial metric techniques,” 51st European Congress of the Regional Science Association International. European Regional Science Accossiation. Barcelona, Spain, Aug. 30 – Sept. 3 2011.
[21] P. Dzieszko, “Land-cover modelling using CORINE Land Cover data and multi-layer perceptron,” QuaestionesGeographicae,vol. 33, no. 1, pp. 5–22, 2014.
[22] C. Schmit, M. D. A. Rounsevell, and I. la Jeunesse, “The limitations of spatial land use data in environmental analysis,” Environmental Science and Policy, vol. 9, no. 2, pp. 174–188, 2006.
[23] J. R. Eastman, Idrisi Selva Manual. Clark University, Worcester, 324 p. 2012.
[24] L. Leontidou, The Mediterranean City in Transition: Social Change and Urban Development. Cambridge University Press, Cambridge, 1990.
[25] L. Leontidou, A.Afouxenidis, E.Kourliouros, and E.Marmaras, Infrastructure-related urban sprawl: mega-events and hybrid peri-urban landscapes in Southern Europe. In Urban Sprawl in Europe: Landscapes, Land-use Change and Policy, In: C. Couch, L. Leontidou and G. Petschel-Held, ed, 2007,pp. 71-101, Oxford: Blackwell.
[26] L. Salvati, A. Sateriano, and S. Bajocco, “To grow or to sprawl? Land Cover Relationships in a Mediterranean City Region and implications for land use management,” Cities, vol. 30, pp. 113-121, 2013.