Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks
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.
[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.
[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.
@article{"International Journal of Architectural, Civil and Construction Sciences:69154", author = "D. Triantakonstantis and D. Stathakis", title = "Urban Growth Prediction in Athens, Greece, Using Artificial Neural Networks", abstract = "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.", volume = "9", number = "3", pages = "234-5", }
