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2022 | No. 55 | 107--121
Tytuł artykułu

Urban Growth Models and Calibration Methods: a Case Study of Athens, Greece

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A number of urban growth models have been developed to simulate and predict urban expansion. Most of these models have common objectives; however, they diff er in terms of calibration and execution methodologies. GIS spatial computations and data processing capabilities have given us the ability to draw more eff ective simulation results for increasingly complex scenarios. In this paper, we apply and evaluate a methodology to create a hybrid cellular-automaton- (CA) and agent-based model (ABM) using raster and vector data from the Urban Atlas project as well as other open data sources. We also present and evaluate three diff erent methods to calibrate and evaluate the model. Th e model has been applied and evaluated by a case study on the city of Athens, Greece. However, it has been designed and developed with the aim of being applicable to any city available in the Urban Atlas project.(original abstract)
Rocznik
Numer
Strony
107--121
Opis fizyczny
Twórcy
  • National Technical University of Athens (NTUA)
Bibliografia
  • Un.Nations. (2016). The World's Cities in 2016 - Data Booklet. Department of Economics & Social Affairs.
  • Cohen, B. (2004). Urban growth in developing countries: A review of current trends and a caution regarding existing forecasts. World Development, 32(1), 23-51.
  • Li, X., & Gong, P. (2016). Urban growth models: progress and perspective. Science Bulletin, 61(21), 1637-1650.
  • Neumann, V., John, B., & W, A. (1966). Theory of self-reproducing automata. IEEE Transactions on Neural Networks, 5(1), 3-14.
  • Langton, C. G. (1997). Artificial life: An overview. MITPress.
  • Torrens, P. M. (2000). How land-use-transportation models work.
  • Wegener, M. (2004). Overview of land-use transport models. Handbook of transport geography and spatial systems, 5, 127-146.
  • Conway, J. (1970). The game of life. Scientific American, 223(4), 4.
  • Gilbert, N. (2008). Agent-based models. Sage.
  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99, 7280-7287.
  • Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q., Marilleau, N., Caillou, P., . . . Drogoul, A. (n.d.). GAMA Platform. Retrieved 7 1, 2019, from https://gama-platform.github.io/
  • Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q.-N., Marilleau, N., Caillou, P., . . . Drogoul, A. (2018). Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica, 1-24.
  • Moeckel, R., Llorca Garcia, C., Moreno Chou, A. T., & Okrah, M. B. (2018). Trends in integrated land use/transport modeling: An evaluation of the state of the art. Journal of Transport and Land Use.
  • Herold, M., Hemphill, J., Dietzel, C., & Clarke, K. (2005). Remote sensing derived mapping to support urban growth theory. URS.
  • Herold, M. a. (2009). Global Mapping of Human Settlement: Experiences, Datasets, and Prospects.
  • Montero, E., Van Wolvelaer, J., & Garzon, A. (2014). The European urban atlas. In Land use and land cover mapping in Europe (pp. 115-124). Springer.
  • Stathakis, D., & Triantakonstantis, D. (2015). Urban Growth Prediction Using Artificial Neural Networks in Athens, Greece. International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering.
  • Community, O. S. (n.d.). GEODATA.gov.gr. (geodata.gov.gr) Retrieved from http://geodata.gov.gr/
  • Taillandier, P., Banos, A., Drogoul, A., Gaudou, B., Marilleau, N., & Truong, Q. C. (2016). Simulating Urban Growth with Raster and Vector Models: A Case Study for the City of Can Tho, Vietnam. In Autonomous agents and multiagent systems (pp. 154-171). Springer International Publishing.
  • Raimbault, J., Banos, A., & Doursat, R. (2016). A Hybrid Network/Grid Model of Urban Morphogenesis and Optimization. CoRR.
  • Community, O. (2019). Open Street Map. (OSM) Retrieved from https://www.openstreetmap.org
  • Iacono, M., Levinson, D., & El-Geneidy, A. (2008). Models of transportation and land use change: A guide to the territory. Journal of Planning Literature, 22(4), 323-340.
  • Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., & Plourde, J. (2014). A big data urban growth simulation at a national scale: configuring the GIS and neural network based land transformation model to run in a high performance computing (HPC) environment. Environmental Modelling & Software, 51, 250-268.
  • Olmedo, M. T., Pontius Jr, R. G., Paegelow, M., & Mas, J.-F. (2015). Comparison of simulation models in terms of quantity and allocation of land change. Environmental Modelling & Software, 69, 214-221.
  • Kazemzadeh, A., Zanganeh, S., Salvati, L., & Neysani Samani, N. (2016). A spatial zoning approach to calibrate and validate urban growth models. International Journal of Geographical Information Science, 1-20.
  • Tsoularis, A., & Wallace, J. (2002). Analysis of Logistic Growth Models. Mathematical Biosciences, 179, 21-55.
  • Clarke, K. C., & Gaydos, L. J. (1998). 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, 12(7), 699-714.
  • Silva, E. A., & Clarke, K. C. (2002). Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal. Computers, Environment and Urban Systems, 26(6), 525-552.
  • Wu, F. (2002). Calibration of stochastic cellular automata: the application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795-818.
  • Clarke, K. C., Hoppen, S., & Gaydos, L. (1996). Methods and techniques for rigorous calibration of a cellular automaton model of urban growth. In Third International Conference/Workshop on Integrating GIS and Environmental Modeling (pp. 21-25). Citeseer.
  • Goldstein, N. C. (2004). Brains versus brawn-comparative strategies for the calibration of a cellular automata-based urban growth model. GeoDynamics, 249-272.
  • Kuhnert, M., Voinov, A., & Seppelt, R. (2005). Comparing raster map comparison algorithms for spatial modeling and analysis. Photogrammetric Engineering & Remote Sensing, 71(8), 975-984.
  • Pontius Jr, R. G., & Cheuk, M. L. (2006). A generalized cross-tabulation matrix to compare soft-classified maps at multiple resolutions. International Journal of Geographical Information Science, 20(1), 1-30.
  • Pontius Jr, R. G. (2002). Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogrammetric Engineering and Remote Sensing, 68(10), 1041-1050.
  • Visser, H., & De Nijs, T. (2006). The map comparison kit. Environmental Modelling & Software, 21(3), 346-358.
  • van Vliet, J., Bregt, A. K., & Hagen-Zanker, A. (2011). Revisiting Kappa to account for change in the accuracy assessment of land-use change models. Ecological Modelling, 222(8), 1367-1375.
  • Fleiss, J. L., Cohen, J., & Everitt, B. S. (1969). Large sample standard errors of kappa and weighted kappa. Psychological Bulletin, 72(5), 323.
  • Cohen, J. (1960). Kappa: Coefficient of concordance. Educ Psych Measurement, 20, 37-46.
  • Foody, G. M. (2007). Map comparison in GIS. Progress in Physical Geography, 31(4), 439-445.
  • Hagen-Zanker, A. (2009). An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science, 23(1), 61-73.
  • van Vliet, J., Hagen-Zanker, A., Hurkens, J., & van Delden, H. (2013). A fuzzy set approach to assess the predictive accuracy of land use simulations. Ecological Modelling, 32-42.
  • Meratnia, N., & de By, R. A. (2002). Aggregation and comparison of trajectories. Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems, 49-54.
  • Chaudhuri, G., & Clarke, K. (2013). The SLEUTH land use change model: A review. Environmental Resources Research, 1(1), 88-105.
  • Hagen-Zanker, A., & Martens, P. (2008). Map comparison methods for comprehensive assessment of geosimulation models. International Conference on Computational Science and Its Applications, 194-209.
  • Hu, Z., & Lo, C. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667-688.
  • Triantakonstantis, D., & Mountrakis, G. (2012). Urban growth prediction: a review of computational models and human perceptions. Journal of Geographic Information System, 4(6), 555.
  • Sante, I., Garcia, A. M., Miranda, D., & Crecente, R. (2010). Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 96(2), 108-122.
  • Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., & Gotts, N. M. (2007). Agent-based land-use models: a review of applications. Landscape Ecology, 22(10), 1447-1459.
  • Eugenio, B. D., & Glass, M. (2004). The kappa statistic: A second look. Computational Linguistics, 30(1), 95-101.
  • Beriatos, E. a. (2006). 'Glocalising' urban landscapes: Athens and the 2004 Olympics. Dialogues in Urban and Regional Planning, 83-116.
  • Grekousis, G., Manetos, P., & Photis, Y. N. (2013). Modeling urban evolution using neural networks, fuzzy logic and GIS: The case of the Athens metropolitan area. Cities, 30, 193-203.
  • Baptista, R., Farmer, J. D., Hinterschweiger, M., Low, K., Tang, D., & Uluc, A. (n.d.). Macroprudential policy in an agent-based model of the UK housing market. 2016: Bank of England Working Paper.
  • Martinez, L. M., & Viegas, J. M. (2017). Assessing the impacts of deploying a shared self-driving urban mobility system: An agent-based model applied to the city of Lisbon, Portugal. International Journal of Transportation Science and Technology, 1, pp. 13-27.
  • Musa, S. I., Hashim, M., & Reba, M. N. (2017). A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 32(8), pp. 813-833.
  • Mustafa, A. a., Saadi, I., Cools, M., & Teller, J. (2018). Comparing support vector machines with logistic regression for calibrating cellular automata land use change models. European Journal of Remote Sensing, pp. 391-401.
  • Chakraborti, S. a., Mondal, B., Shafizadeh-Moghadam, H., & Feng, Y. (2018). A neural network and landscape metrics to propose a flexible urban growth boundary: A case study. Ecological Indicators(93), pp. 952-965.
  • Shafizadeh-Moghadam, H. (2019). Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. Computers, Environment and Urban Systems, pp. 91-100.
  • Mustafa, A., Cools, M., Saadi, I., & Teller, J. (2017). Coupling agent-based, cellular automata and logistic regression into a hybrid urban expansion model (HUEM). Land Use Policy, pp. 529-540.
  • Buttner, G., Soukup, T., & Kosztra, B. (2014). CLC2012 addendum to CLC2006 technical guidelines. Copenhagen (EEA): European Environmental Agency.
  • Dongya, L., Xinqi, Z., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, 2020, p. 108924.
  • Liu, D., Zheng, X., & Wang, H. (2020). Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata. Ecological Modelling, p. 108924.
  • Tsagkis, P., & Photis, Y. (2018, 4). Using Gama platform and Urban Atlas Data to predict urban growth. The case of Athens. 11th International Conference of the Hellenic Geographical Society (ICHGS - 2018).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.ekon-element-000171642217

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