Urban growth models and calibration methods: a case study of Athens, Greece
DOI:
https://doi.org/10.12775/bgss-2022-0008Keywords
gis, urban growth modelsAbstract
A number of urban growth models have been developed to simulate and predict urban expansion. Most of these models have common objectives; however, they differ in terms of calibration and execution methodologies. GIS spatial computations and data processing capabilities have given us the ability to draw more effective 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 different methods to calibrate and evaluate the model. The 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.
References
Baptista, R., Farmer, J. D., Hinterschweiger, M., Low, K., Tang, D. & Uluc, A. (2016). Macroprudential policy in an agent-based model of the UK housing market. Bank of England Staff Working Paper No 216.
Beriatos, E.A. (2006). Glocalising’urban landscapes: Athens and the 2004 Olympics. In: Dialogues in Urban and Regional Planning, 83-116, Routledge.
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the national academy of sciences, 99: 7280-7287.
Buttner, G., Soukup, T. & Kosztra, B. (2014). CLC2012 addendum to CLC2006 technical guidelines. Copenhagen (EEA): European Environmental Agency.
Chakraborti, S., Nath Das, D., 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: 952-965.
Chaudhuri, G. & Clarke, K. (2013). The SLEUTH land use change model: A review. Environmental Resources Research, 1(1): 88-105.
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.
Clarke, K.C., Hoppen, S. & Gaydos, L. (1996). Methods and techniques for rigorous calibration of a cellular automaton model of urban growth. V Third International Conference/Workshop on Integrating GIS and Environmental Modeling, 21-25, Citeseer.
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.
Cohen, J. (1960). Kappa: Coefficient of concordance. Educ Psych Measurement, 20: 37-46.
community, O.S. (brez datuma). GEODATA.gov.gr. (geodata.gov.gr) Pridobljeno iz http://geodata.gov.gr/
Conway, J. (1970). The game of life. Scientific American, 223(4): 4.
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, 417(1): 108924.
Drogoul, A., Taillandier, P., Gaudou, B., Grignard, A., Nghi, H., Minh, T.T., Marilleau, N., Caillou, P., Vo, D-A. & Xuan, V.T. (2019). GAMA Platform. Available at: https://gama-platform.github.io/ (Access 1 July 2019).
Eugenio, B.D. & Glass, M. (2004). The kappa statistic: A second look. Computational linguistics, 30(1): 95-101.
Fleiss, J.L., Cohen, J. & Everitt, B.S. (1969). Large sample standard errors of kappa and weighted kappa. Psychological bulletin, 72(5): 323.
Foody, G.M. (2007). Map comparison in GIS. Progress in Physical Geography, 31(4): 439-445.
Gilbert, N. (2008). Agent-based models. Sage.
Goldstein, N.C. (2004). Brains versus brawn-comparative strategies for the calibration of a cellular automata-based urban growth model. GeoDynamics, 249-272.
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. DOI: https://doi.org/10.1016/j.cities.2012.03.006.
Hagen-Zanker, A. (2009). An improved Fuzzy Kappa statistic that accounts for spatial autocorrelation. International Journal of Geographical Information Science, 23(1): 61-73.
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.
Herold, M. a. (2009). Global Mapping of Human Settlement: Experiences, Datasets, and Prospects.
Herold, M., Hemphill, J., Dietzel, C. & Clarke, K. (2005). Remote sensing derived mapping to support urban growth theory. URS.
Hu, Z. & Lo, C. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6): 667-688.
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.
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, 31(4): 1-20.
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.
Langton, C.G. (1997). Artificial life: An overview. Mit press.
Li, X. & Gong, P. (2016). Urban growth models: progress and perspective. Science Bulletin, 61(21): 1637-1650. DOI: https://doi.org/10.1007/s11434-016-1111-1.
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, 417: 108924.
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: 13-27. DOI: https://doi.org/10.1016/j.ijtst.2017.05.005.
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.
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.
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, 11(1): 463-476.
Montero, E., Van Wolvelaer, J. & Garzon, A. (2014). The European urban atlas. V Land use and land cover mapping in Europe, 115-124, Springer.
Musa, S.I., Hashim, M. & Reba, M.N. (2017). A review of geospatial-based urban growth models and modelling initiatives. Geocarto International, 32(8): 813-833. DOI: https://doi.org/10.1080/10106049.2016.1213891.
Mustafa, A., Rienow, 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, 51(1): 391-401.
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, 69: 529-540. DOI: https://doi.org/10.1016/j.landusepol.2017.10.009.
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.
OpenStreetMap. (2019). Open Street Map. (OSM) Available at: https://www.openstreetmap.org.
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.
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.
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.
Raimbault, J., Banos, A. & Doursat, R. (2016). A Hybrid Network/Grid Model of Urban Morphogenesis and Optimization. CoRR.
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.
Shafizadeh-Moghadam, H. (2019). Improving spatial accuracy of urban growth simulation models using ensemble forecasting approaches. Computers, Environment and Urban Systems, 76: 91-100. DOI: https://doi.org/10.1016/j.compenvurbsys.2019.04.005.
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.
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, 9(3): 234-238.
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: International conference Autonomous Agents and Multiagent Systems (AAMAS 2016), 9 May 2016 - 10 May 2016 (Singapore, Singapore). DOI: https://doi.org/10.1007/978-3-319-46840-2_10.
Taillandier, P., Gaudou, B., Grignard, A., Huynh, Q-N., Marilleau, N., Caillou, P., Philippon, D. & Drogoul, A. (2018). Building, composing and experimenting complex spatial models with the GAMA platform. GeoInformatica, 23(2): 299-322.
Torrens, P.M. (2000). How land-use-transportation models wor. Centre for Advanced Spatial Analysis, London.
Triantakonstantis, D. & Mountrakis, G. (2012). Urban growth prediction: a review of computational models and human perceptions. Journal of Geographic Information System, 4(6): 555.
Tsagkis, P. & Photis, Y. (2018). 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), Athens.
Tsoularis, A. & Wallace, J. (2002). Analysis of Logistic Growth Models. Mathematical biosciences, 179: 21-55.
Un.Nations. (2016). The World’s Cities in 2016 – Data Booklet. Department of Economics & Social Affairs.
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.
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, 261-262: 32-42.
Visser, H. & De Nijs, T. (2006). The map comparison kit. Environmental Modelling & Software, 21(3): 346-358.
Wegener, M. (2004). Overview of land-use transport models. Handbook of transport geography and spatial systems, 5: 127-146.
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.
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