عنوان مقاله [English]
The growth of large cities and its consequent spatial effects has led to alteration of vast rural lands to urban areas and has influenced and dominated the life patterns of many countries during the recent centuries. Urban growth in Iran has been accelerated during last decades by mass immigration of rural population to urban areas. The urbanization rate will increase to about 70.6% and 78.2% by 2020 and 2050; respectively. The hastening increase in the population of the cities, lack of urban infrastructures, shifting the land use and the consequent vanish of ecologically valuable lands in the countries, industrial pollutions, illegal settlement in the suburbs and other human activities which have influenced the growth of cities in Iran, make it necessary to study and analyze the growth of Urmia (The capital of Western Azerbaijan Province which is one of the large cities in Iran). According to 2006 census, the population of the city was 583255 that shows a 30% increase during a decade. Border lines with Iraq and Turkey, mild climate, strategic position and natural resources are the characteristics that make further developments possible. Urban growth and its driving factors are important topics in recent urban research analysis. Several physical, economic and social factors influence the urban growth while they have nonlinear and complex relationship. Although urban growth is a prevalent procedure, its patterns and driving factors are almost vague and there is no inclusive collection of factors which may describe the urban growth process since the characteristics of cities are unique. To evaluate the results of urban growth plans and predict the condition of city boundaries and land use changes, urban planners need to analyze urban growth practically. So that, this paper focuses on spatiotemporal recognition of Urmia’s urban growth as a case study for large cities in Iran whose development has caused merging of the outskirts with the main city, creating chaotic and informal settlements in recent decades and consequently discontinuous development policy has been taken into account for the city master plan. The article is organized as follows: reviewing the existing literature on urban growth, explaining selected variables associated with urban growth pattern, listing out the data used for modeling and explaining the applied methodology and the framework of Logistic Regression model. The goal of this study is to define and recognize the urban growth system, affecting factors on urban growth, quantize the relation between the urban growth and the driving factors and analyze the spatial growth patterns according to historical land use changes for the city of Urmia, assuming that some special driving factors and local patterns which are consistent with geographic, economic, physical and social structure of the city, have influenced its growth. Reviewing different studies shows several approaches of urban growth pattern analysis, selection of different variables and different results. So, by taking into consideration local characteristics and data availability, 14 indicators depicting urban growth were selected for the purposes of this study. The data used in this study included land use, terrain, demographic and transportation network. Historical land use/cover data of Urmia (1999-2006) were acquired through classification of Landsat ETM images and data derived from master plan of city of Urmia. In addition, a conceptual model is presented to analyze the urban growth of Urmia which includes the relevant urban growth variables. Then the modelling approach (Logistic Regression) is chosen through studying different methods of modelling and thereafter the urban growth analysis of Urmia is achieved by Logistic Regression modelling to identify the driving factors and quantize the relation between the urban growth and the driving factors. This paper introduced two series of influencing factors on Urmia urban growth: factors with favorable effects including: slope (%), bare lands, farm lands and populated areas and factors with unfavorable effects including: distance from main roads, residential areas, industrial centers and commercial sites. Finally, it was found that the specifications of physical growth pattern of Urmia did not only depend on the existing situation of the pattern, but also on the factors influencing it as well. It is concluded that residential areas and roads are the main influencing urban growth factors of Urmia. It shows that the mutual effect pattern of land use_ transportation will be of high importance for future planning of the city. The results of this research could be suitable means to analyze and compare pattern and factors influencing on urban growth of Urmia with other large cities of Iran to be utilized by urban planners and managers.
Allen, J. & Lu, K. (2003). Modeling and Prediction of Future Urban Growth in the Charleston Region of South Carolina: A GIS-Based Integrated Approach. Conservation Ecology, 8, 2.
Batisani, N. & Yarnal, B. (2009). Urban Expansion in Centre County, Pennsylvania: Spatial Dynamics and Landscape Transformations. Applied Geography. 29, 235–249.
Batty, M. & Longley, P. (1994). Fractal Cities: a Geometry of Form and Function. San Diego: Academic Press.
Braimoh, A. K. & Onishi, T. (2006). Spatial Determinants of Urban Land Use Change in Lagos, Nigeria. AMS Online Journals, 8, 21.
Cetin, M. & Demirel, H. (2010). Modelling and Simulation of Urban Dynamics. Fresenius Environmental Bulletin, 9, 10A.
Cheng, J. & Masser, I. (2003). Urban Growth Pattern Modeling: A Case Study of Wuhan City, PR China. Landscape and Urban Planning, 62, 199-217.
Cheng, J. (2003). Modelling Spatial & Temporal Urban Growth. Doctoral Dissertation, Faculty of Geographical Sciences, Utrecht University.
Clarke, K. C. & Gaydos, L. J. (1998). Loose-coupling a CA Model and GIS: Long-term Urban Growth Prediction for San Franciso and Washington/Baltimore. International Journal of Geographical Information Science, 12 (7), 699-714.
Clarke, K. C., Hoppen, S. & Gaydos, L. (1997). A Self-modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area. Environment and Planning B: Planning and Design, 24 (2), 247–261.
Dendoncker, N., Bogaert, P. & Rounsevell, M. (2007). Spatial Logistic Regression Models to Analyse the Role of Neighbourhood Variables in Land Use Distributions in Belgium. Computers, Environment and Urban Systems, 31, 188-205.
Fang, S., Gertner, G. Z., Sun, Z., & Anderson, A. A. (2005). The Impact of Interactions in Spatial Simulation of the Dynamics of Urban Sprawl. Landscape and Urban Planning.
Forman, R. T. T. (1995). Land Mosaics: The Ecology of Landscapes and Regions. Cambridge Univ. Press: Cambridge.
Harvey, R.O. & Clark, W.A.V. (1965). The Nature of Economics and Urban Sprawl, Land Economics, XLI (1), 1 – 9.
Hu, Z. & Lo, C.P. (2007). Modeling Urban Growth in Atlanta Using Logistic Regression. Computers, Environment and Urban Systems, 31(6), 6.
Huang, B., Zhang, L. & Wu, B. (2009) .Spatiotemporal Analysis of Rural-urban Land Conversion. International Journal of Geographic Information Science, 23(3), 379-398.
Long, Y., Mao, Q. & Dang, A. (2009). Beijing Urban Development Model: Urban Growth Analysis and Simulation, Tsinghua. Science and Ecology, 14(6), 782-794.
Luo, J. & Wei, Y.H.D. (2009). Modeling Spatial Variations of Urban Growth Patterns in Chinese Cities: The Case of Nanjing. Landscape and Urban Planning, 91(2), 51-64.
Mubareka, S., Koomen, E., Estreguil, C. & Lavalle, C. (2011). Development of a Composite Index of Urban Compactness for Land Use Modelling Applications. Landscape and Urban Planning, 103, 303–317.
Poelmans, L. & VanRompaey, A. (2009). Complexity and Performance of Urban Expansion Models. Computers. Environment and Urban Systems, 34(1), 17-27.
Shamsuddin, S. & Yaakup, A. (2007). Predicting and Simulating Future Land Use Pattern: A Case Study of Seremban District. Journal Alam Bina, 9, 1.
Shen, T.Y., Wang, W.D., Hou, M., Guo, Z.C., Xue, L. & Yang, K.Z. (2008). Study on Spatio-temporal System Dynamic Models of Urban Growth. System Engineering Theories and Practices, 27(1), 10-17.
Tarh & Amayesh Consulting Engineers. (2010). Revised Master Plan of Urmia. The West Azarbaijan Organization of Housing & Urban Developement.
United Nations. (2011).World Urbanization Prospects: The 2011 Revision.
Verburg, P.H., Schot, P., Dijst, M. & Veldkamp, A. (2004). Land use Changemodelling: Current Practice and Research Priorities. Geo journal, 61, 309–324.
White, R. & Engelen, G. (2000). High-Resolution Integrated Modeling of the Spatial Dynamics of Urban and Regional Systems. Computers, Environment and Urban Systems, 24(5), 383-400.
Wilson, E., Hurd, J., Civco, D., Prisloe, M. & Arnold, C. (2003). Development of a Geospatial Model to Quantify, Describe and Map Urban Growth. Remote Sensing of Environment, 86, 275–285.
Wu, F. (2000). A Parameterized Urban Cellular Model Combining Spontaneous and Self- Organizing Growth in GIS and Geo-computation (Innovations in GIS 7). (pp.73-86). In Atkinson, P and Martin, D (Eds). New York: Taylor & Francis.
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.
Xiaoping, L. & Xia, L. (2008). Simulating Complex Urban Development Using Kernel-based Non-linear Cellular Automata. Ecological Modelling, 211(1-2), 169-181.
Xie, C., Huang, B., Claramunt, C. & Chandramouli, C. (2009). Spatial Logistic Regression and GIS to Model Rural-urban Land Conversion. International Journal of Geographic Information Science, 23, 3.
Yeh, A.G. & Li, X. (2001). A Constrained CA Model for the Simulation and Planning of Sustainable Urban Forms by Using GIS. Environment and Planning B: Planning and Design, 28, 733–753.
Zeng, Y.N., Wua, G.P., Zhanb, F.B. & Zhanga ,H.H. Spatial Land Use Pattern Using Auto Logistic Regression, A School of Info-physics and Geomatics Engineering, Central South University, Changsha, China.