Measuring the Effective Variables on Urban Expansion and Physical Development Simulation of Hamadan City Using Integrated Model of Cellular automata, Logistic Regression and Markov Chain

Subject Areas : Urban and Regional Planning Studies

Saeid Hajibabaei ¹ (Ph.D in Urban Planning, Qazvin Branch, Islamic Azad University, Qazvin, Iran)
keramatollah ziari ² (Professor of Geography & Urban Planning, Faculty of Geography, University of Tehran, Tehran, Iran)
kianoosh zakerhaghighi ³ (Associate Professor, Department of Urban Planning and Design, Hamedan Branch, Islamic Azad University, Hamedan, Iran)

Received: 2020-08-06 Accepted : 2020-10-27 Published : 2022-05-22

Keywords: cellular automata, physical development, Markov chain, Hamedan, Logistic regression,

Abstract :

Urban development and irregular migration of rural population to urban areas are significant phenomena that have damaged agricultural lands, natural landscapes, and public open spaces. This issue doubles the need for informed guidance and spatial organization to better understand the processes of urban development for future planning. The present study aimed to evaluate the growth of Hamedan city from 1996 to 2019 and then simulate until 2041. The research method is descriptive-analytical, and the cellular automation model was used to simulate physical development, and logistic regression was applied to analyze the impact of different variables on physical growth and the Markov chain was used to analyze user changes. The validity of Landsat satellite images is also evaluated with respect to the kappa value and acceptable overall accuracy. The results indicate that city center and agricultural land variables with ROC of 0.873 and 0.881, respectively, had the most impact on Hamadan urban growth during the last 23 years. The area of urban areas in 1996 was doubled compared to the year 2011, and almost 2.5 times more than in 2019. On the other hand, population growth increased 1.48 times over the past 23 years. This indicates that the growth rate of urban areas exceeded the population growth rate in Hamadan. The results of the model evaluation indicate that the integrated model is able to provide a precise understanding of urban processes and developments such as evaluating past developments and predicting directions and rates of future physical development.

References:

جباری، محمد کاظم و احمدی، سیمین. (1391). مدل سازی توسعه شهری با سیستم­های اطلاعات جغرافیایی و اتوماتای ​​سلولی. زنجان: آذرکلک.

غلامی، یونس؛ باعقدیه رودی، نصراله؛ خلجی، نسرین و حیاتی، سلمان. (1396). توسعه پایدار شهری با تاکید بر الگوی رشد هوشمند، تهران: عارف

کرم، امیر؛ حجه فروش نیا، شیلا و حکیمی، حمید رضا. (1389). مدلسازی فضایی توسعه شهری با استفاده از روش رگرسیون لجستیک، مطالعه موردی: شهرکرد. مجله تحقیقات کاربردی علوم جغرافیایی، 10(11)، 64-41.

 

Balzter, H. (2000). Markov Chain Models for Vegetation Dynamics. Ecological Modeling, 126, 139–154.

Barredo, J. I. (2003). Modelling dynamic spatial rocesses: simulation of urban future scenarios through cellular automata. Landscape and Urban Planning, 64, 145–160.

Cheng, J. (2003). Modelling Spatial and Temporal Urban Growth. PhD Thesis. International institute for Geoinformation Science and Earth Observation (ITC). The Netherlands.

Chopard, B., Luthi, P.O., & Queloz, P-A. (1996). Cellular automata model of car traffic in a two-dimensional street network. Journal of Physics A: Mathematical and General, 29 (10), 2325– 2336.

Fang, S., Z Gertner, G., Sun Z., & A. Anderson, A. (2005). The impact of interactions in spatial simulation of the dynamics of urban sprawl. Landscape and urban planning, 73, 294- 306.

Hathout, S. (2002). the use of GIS for monitoring and predicting urban growth in east and west ST paul, Winnipeg, maintoba. Canada, journal of environmental management, 66, 229-238.

Khammar, G, & Namazi, B. (2017). Prediction and simulation of spatial-physical development pattern of the city of Chabahar, in the horizon of the year 1420 (2041), using RS and automated cells models. journal of spatial planning, 7(2), 78-94. (In Persian)

Liao, J, Shao, G, Wang, C, Tang, L, Huang, Q, & Qiu, Q. (2019). Urban sprawl scenario simulations based on cellular automata and ordered weighted averaging ecological constraints, Ecological Indicators, 107.

Liu, Y., & Phinn, S. R. (2003). Modelling urban development with cellular automata incorporating fuzzy-set approaches. Computers, Environment and Urban Systems, 27(6), 637-658.

Majedi, H., Zebardast, E., & Mojarabi Kermani, B. (2018). The Analysis of Factors Affecting Urban Growth in Urmia, Using Logistic Regression. Armanshahr Architecture & Urban Development, 10(21), 377-392. (In Persian)

Mirkatooli, J., ghadami, M., mahdian, M., & mohamadi, S. (2011). Study and survey of trend and physical- space expansion of Babolsar city with using Shannon’s Entropy and Holdern models. Journal of Studies of Human Settlements Planning, 6(16), 115-133 (In Persian)

Mundia, C. N., & Aniya, M. (2005). Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS. International Journal of Remote Sensing, 26(13), 2831–2849.

Nagel, K., & Schreckenburg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique, 2, 2221-2229.

Natale, V., & Junquera, Z. (2015). Assessment of the Conservation Status of Natural and Semi-Natural Patches Associated with urban Areas through Habitat Suitability Indices. International journal environmental research, 9, 495-504.

Park, S., Jeon, S., Kim, S. & Choi, C. (2011). Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea. Landscape and urban planning, 99(2), 104-114.

Rahnama, M., & Abaszadehm G. (2010). A comparative study and analyzing compactness/sprawl ratio in the metropolitan cities of Mashah and Sydney. Journal of Geography and Regional Development, 4(6), 101 – 128. (In Persian)

Rezazadeh, R., & Mirahmadi, M. (2009). Cellular automata model, a new approach in urban growth simulation. Journal of education,  4(1), 47-55. (In Persian)

Rolando, E., Caravantes, D., & Sanchez-flores, E. (2010). Water transfer effects on peri-urban landuse/ landcover: a case study in a Semi- Arid region of Mexico. Applied geography, 31(2), 413-425.

Sang, L., Zhang, Ch., Yang, J., & WenjuYun, D. (2011). Simulation of land use spatial pattern of towns and villages based on CA–Markov model. Mathematical and Computer Modelling, 54(3), 938-943.

Syphard, D., Clarke, C., & Franklin, J. (2005). Using a cellular automaton model to forecast the effects of urban growth on habitat pattern in southern California. Ecological Complexity, 185–203.

Tayyebi, A., & DarrelJenerette, G. (2016). Increases in the climate change adaption effectiveness and availability of vegetation across a coastal to desert climate gradient in metropolitan Los Angeles, CA, USA. Science of the Total Environment, 548–549, 60–71.

Wang, H., He, S, liu, H., Dai, L., Pan, P., hong, S., & Zhang, W. (2012). Simulating urban xpansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China. Landscape and Urban Planning, 30(4), 591-611.

Weerakoon, K. (2017). Analysis of spatio-temporal urban expansion using GIS integrated urban gradient analysis; Colombo District, Sri Lanka. American Journal of Geographic Information System, 6(3), 83–89.

White, R., & Engelen, G. (2000). High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers. Environment and Urban Systems, 24(5), 383–400.

Xu, T., & GAO, J. (2019). Directional multi-scale analysis and simulation of urban expansion in Auckland, New Zealand using logistic cellular automata, Computers. Environment and Urban Systems, 78(2),101390

Yu, D., yanxu, l., & Bojie, f. (2019). Urban growth simulation guided by ecological constraints in Beijing city: Methods and implications for spatial planning. Journal of Environmental Management, 243, 402-410.

Yu, W., Zang, Sh., Wu, Ch., liu, W., & Na, X. (2011). analyzing and modeling land use land cover change(LUCC) in the daqing city. china, applied Geography, 31(2), 600-608.

Zhou, l., dang, X., Sun, Q., & Wang, S. (2020). Multi-scenario simulation of urban land change in Shanghai by random forest and CA-Markov model. Sustainable Cities and Society, 107, 2411-2502.