Scenario-Based Cellular Automata and Artificial Neural Networks in Urban Growth Modeling

Abstract

The speed at which cities are growing and developing today cannot be disregarded. Human activities and natural causes are both contributors to urban growth. The relationship between these factors is complex and the complexity makes it difficult for the human mind alone to understand cities. A model that helps reveal the complexity is needed for urban studies. Main objective of this study is to understand the effects of urban planning strategies on the future of the city by utilizing a Cellular Automata and Artificial Neural Networks based simulation model. Driving factors of urban growth according to development scenarios were used in the simulation process. Six different development scenarios were formulated according to the strategic plan of Izmir. Land use and driving factor data used in simulating scenarios were acquired from EarthExplorer and OpenStreetMap databases, and produced in QGIS. Future Land Use Simulation Model (FLUS) based on Cellular Automata (CA) and Artificial Neural Networks (ANN) was used. The results were assessed both by using FRAGSTATS which helped calculate fractal dimensions and visual analysis. Fractal dimension results of each scenario showed that the simulation model respected the overall urban complexity. A closer look at each scenario indicated the diverse local growth possibilities for different scenarios. The results show that urban simulation models when used as decision support tools promise a more inclusive and explicit planning process.

Keywords

• Urban growth
• Development scenarios
• Cellular automata
• Artificial neural networks
• GIS

References

1. [1] White, R., Engelen, G., Uljee, I., “Modeling Cities and Regions As Complex Systems: From Theory to Planning Applications” 1st ed., MIT Press, Cambridge, (2015).
2. [2] Barredo, J.I., Kasanka, M., McCormick, N., Lavalle, C., “Modelling dynamic spatial processes: Simulation of urban future scenarios through cellular automata”, Landscape and Urban Planning, 64(3): 145–160, (2003).
3. [3] White, R., Engelen, G., “Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land-use patterns”, Environment and Planning A, 25(8): 1175-1199, (1993).
4. [4] Batty, M., Xie, Y, “From cells to cities”, Environment and Planning B: Planning and Design, 21: 531–548, (1994).
5. [5] Pinto, N.N., Antunes, A.P., “A cellular automata model based on irregular cells: Application to small urban areas”, Environment and Planning B: Planning and Design, 37(6): 1095–1114, (2010).
6. [6] O’Sullivan, D., “Graph-cellular automata: A generalised discrete urban and regional model”, Environment and Planning B: Planning and Design, 28(5): 687–705, (2001).
7. [7] Li, X., Yeh, A.G.O., “Neural-network-based cellular automata for simulating multiple land use changes using GIS”, International Journal of Geographical Information Science, 16(4): 323–343, (2002).
8. [8] Al-Ahmadi, K., See, L., Heppenstall A., Hogg, J., “Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia”, Ecological Complexity, 6(2): 80–101, (2009).

9. [9] Li, X., Lin, J., Chen, Y., Liu, X., Ai, B., “Calibrating cellular automata based on landscape metrics by using genetic algorithms”, International Journal of Geographical Information Science, 27(3): 594–613, (2013).
10. [10] Camacho Olmedo, M.T., Paegelow, M., Mas, J.F., Escobar, F. (Eds.), “Geomatic Approaches for Modeling Land Change Scenarios” 1st ed., Springer International Publishing, (2018).
11. [11] Guan, C. H., Rowe, P. G., “Should big cities grow? Scenario-based cellular automata urban growth modeling and policy applications”, Journal of Urban Management, 5(2): 65–78, (2016).
12. [12] Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Li, S., Wang, S., Pei, F., “A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects”, Landscape and Urban Planning, Elsevier, 168: 94–116, (2017).
13. [13] Batty, M., “Modelling cities as dynamic systems”, Nature, 231(5303): 425–428, (1971).
14. [14] Megahed, Y., Cabral, P., Silva, J., Caetano, M., “Land cover mapping analysis and urban growth modelling using remote sensing techniques in greater Cairo region-Egypt”, ISPRS International Journal of Geo-Information, 4(3): 1750–1769, (2015).
15. [15] Veldkamp, A., Fresco, L. O., “CLUE: A conceptual model to study the conversion of land use and its effects”, Ecological Modelling, 85(2–3):253–270, (1996).
16. [16] Soares, B. S., Cerqueira, G. C., Pennachin, C. L., “DINAMICA - a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier”, Ecological Modelling, 154: 217–235, (2002).
17. [17] Silva, E. A., Clarke, K. C., “Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal”, Computers, Environment and Urban Systems, 26(6): 525–552, (2002).
18. [18] https://simlander.wordpress.com. Access Date: 08.01.2022.
19. [19] Roodposhti, M. S., Hewitt, R. J., Bryan, B. A., “Towards automatic calibration of neighbourhood influence in cellular automata land-use models”, Computers, Environment and Urban Systems, Elsevier, 79(January), (2020).
20. [20] Antoni, J.-P., Vuidel, G., And, H.O., Klein, O., “Geographic Cellular Automata for Realistic Urban form Simulations: How Far Should the Constraint be Contained?” in D'Acci L. (eds), The Mathematics of Urban Morphology, Modeling and Simulation in Science, Engineering and Technology, Springer International Publishing, 147–162, (2019).
21. [21] Feng, Y., Tong, X., “A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods”, International Journal of Geographical Information Science, 34(1): 74-97, (2019).
22. [22] Mahmoud, M., Liu, Y., Hartmann, H., Stewart, S., Wagener, T., Semmens, D., Stewart, R., Gupta, H., Dominguez, D., Dominguez, F., Hulse, D., Letcher, R., Rashleigh, B., Smith, C., Street, R., Ticehurst, J., Twery, M., van Delden, H., Waldick, R., White, D., Winter, L., “A formal framework for scenario development in support of environmental decision-making”, Environmental Modelling and Software, 24(7): 798–808, (2009).
23. [23] Benenson, I., Torrens, P.M., “Geosimulation: Automata-based Modeling of Urban Phenomena” 1st ed., Wiley, Chichester, (2004).
24. [24] Feng, Y., Wang, R., Tong, X., Shafizadeh-Moghadam, H., “How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth?”, Computers, Environment and Urban Systems, 76: 150–162, (2019).
25. [25] https://semiautomaticclassificationmanual.readthedocs.io/en/latest/index.html. Access Date: 04.10.2021.
26. [26] http://www.umass.edu/landeco/research/fragstats/fragstats.html. Access Date: 04.10.2021.
27. [27] https://data.tuik.gov.tr/Kategori/GetKategori?p=nufus-ve-demografi-109&dil=1. Access Date: 04.10.2021.
28. [28] Izmir Kalkınma Ajansı, “2014-2023 İzmir Bölge Planı”, Izmir Development Agency Report, Izmir, (2015).
29. [29] Turner, B.L.I.I., Meyer, W.B., Skole, D.L., “Global land-use/land-cover change: Towards an integrated study”, Ambio, 23(1): 91–95, (1994).
30. [30] Openshaw, S., “Neural network, genetic, and fuzzy logic models of spatial interaction”, Environment and Planning A, 30(10): 1857–1872, (1998).
31. [31] Gopal, S., “Artificial Neural Networks in Geospatial Analysis”, in D. Richardson, N. Castree, M.F. Goodchild, A. Kobayashi, W. Liu, R.A. Marston (eds.), International Encyclopedia of Geography: People, the Earth, Environment and Technology, 1-7, (2016).
32. [32] Liang, X., Liu, X., Li, D., Hui, Z., Chen, G., “Urban growth simulation by incorporating planning policies into a CA-based future land-use simulation model”, International Journal of Geographical Information Science, 32(11): 2294–2316, (2018).
33. [33] Reis, J.P., Silva, E.A., Pinho, P., “Spatial metrics to study urban patterns in growing and shrinking cities”, Urban Geography, 37(2): 246–271, (2016).
34. [34] Pickard, B.R., Meentemeyer, R.K., “Validating land change models based on configuration disagreement”, Computers, Environment and Urban Systems, 77: 101366, (2019).
35. [35] Vaughan, J., Ostwald, M., “The Relationship between the Fractal Dimension of Plans and Elevations in the Architecture of Frank Lloyd Wright: Comparing The Prairie Style, Textile Block and Usonian Periods”, Architecture Science ArS, 4: 21-44, (2011).
36. [36] Lionar, L. M., Ediz, Ö., “Measuring Architecture and Urban Fabric: The Case of the İMÇ and the SSK Complexes”, JCoDe: Journal of Computational Design, 2(1): 335-354, (2021).