modeling The possibility of changing urban growth using artificial neural network and logistic regression (Case Study: Mashhad)

Subject Areas :

farhad rostami gale ¹ (A Masters student in Civil Engineering, Geospatial Information System University of Ferdowsi Mashhad)
rouzbeh shad ² (Assisstant professor in Department of Civil Engineering, University of Ferdowsi Mashhad)
marjan ghaemi ³ (Assisstant professor in Department of Civil Engineering, University of Ferdowsi Mashhad)
yasaman lohrabi ⁴ (A Masters student of natural resources engineering, University of Shahrekord)

Received: 2017-09-19 Accepted : 2018-08-31 Published : 2018-09-23

Keywords: Mashhad, modeling The possibility of changing urban growth, Logistic Regression, neural network Multilayer Perceptron, ROC,

Abstract :

In developing countries, the high tendency for concentration of population in urban areas and consequently the rapid and uneven growth of cities have led urban designers and planners to use appropriate policies and strategies to avoid environmental and socio-economic damaging effects on the order Work. In this regard, spatial and temporal information related to growth rate patterns provides a better understanding of the urban growth process and provides appropriate tools for obtaining management and planning policies for urban managers. Therefore, the main objective of this research is to calculate the probability of growth change in Mashhad using logistic regression and artificial neural network. For this purpose, satellite images of Landsat 7 (2002) and Landsat 8 (2015) were used to provide land-use mapping. Then, using multi-layer perceptron artificial neural network (MLP), the classification of images was made and urban land use maps with a total accuracy of 948/0 and a Kappa index of 936 for 2002 as well as a general accuracy of 8177 and a Kappa index of 775 / 0 were extracted for 2015. Finally, with the implementation of logistic regression between urban land use map 2015 (as dependent variable) and effective factors such as physical factors and human factors along with 2002 map of lands (as independent variables), the potential map of urban land development was prepared. The evaluation of the regression model generated by two Pseudo-R2 and ROC indicators showed that this model has a ROC value of 0.87 and Pseudo-R2 of 345/0 has a high ability to show changes and determine areas prone to change, and fit The model is considered fairly    well.                                                                                                                                                                                                                                           

References:

1. آقامحمدی، میثم (1391): «مدلسازی توسعه شهری با استفاده از اتوماتای سلولی مبتنی بر منطق فازی با تاکید بر توسعه قوانین انتقال»، پایان نامه کارشناسی ارشد، دانشکده مهندسی نقشه برداری، دانشگاه خواجه نصیرالدین طوسی.


2. زارعی، رضا و اصغر آل شیخ (زمستان 1391): «مدلسازی توسعه شهری با استفاده از اتوماسیون سلولی و الگوریتم ژنتیک (منطقه مورد مطالعه: شهر شیراز)، مجله پژوهش و برنامه­ریزی شهری، سال سوم، شماره یازدهم، صص 16-1.


3. شیعه، اسماعیل (1393): مقدمه ای بر مبانی برنامه ریزی شهری، مرکز انتشارات دانشگاه علم و صنعت ایران، 212 ص.


4. Al-Ahmadi, K., See, L., Heppenstall, A., & Hogg, J. (2009): Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecological Complexity, 6(2), 80-101.


5. Al-kheder, S. Wang, J. and Shan, J. (2008): Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images. Int. J. Geogr. Inform. Sci, 22(11), 1271-1293.


6. Amar.org.ir (1394)


7. Barredo, J., Kasanko, M., Mccormic, N & Lavalle, C. (2003): Modelling Dynamic Spatial processes: Simulation of urban feuture scenarious through cellular automata. Landscape and Urban Planing, 64(10), 145-160.


8. Clark, W. A., Hosking, P. L., & WAV, C. (1986): Statistical methods for geographers, 310(5).


9. Eyoh, A., Olayinka, D. N., Nwilo, P., Okwuashi, O., Isong, M., & Udoudo, D. (2012): Modelling and predicting future urban expansion of lagos, nigeria from remote sensing data using logistic regression and GIS. International Journal of Applied, 2(5), 58-71.


10. Hu, Z., & Lo, C. P. (2007): Modeling urban growth in Atlanta using logistic regression. Computers. Environment and Urban Systems, 31(6), 667-688.


11. Jensen, J. R. (1996): Thematic information extraction: Image classification. Introductory Digital Image Processing. A Remote Sensing Perspective, 197-256.


12. Li, X. & Liu, X. (2006): An extended cellular automation using case-based reasoning for simulating urban development in a large complex region. International Journal of Geographical Information Science, 20(10), 1109-1136.


13. Li, X. Yang, Q. and Liu, X. (2008): Discovering and evaluating urban signatures for simulating compact development using cellular automata. Landscape and Urban Planning, 86(4), 177-186.


14. Li, X., Yeh, A., (2004): Modeling Sustainable urban development by the integration of constrained cellular automata and GIS.  Geographical Information Science, 14(2), 131-152.


15. Liu, Y., Dai, L., & Xiong, H. (2015): Simulation of urban expansion patterns by integrating auto-logistic regression, Markov chain and cellular automata models. Journal of Environmental Planning and Management, 58(6), 13-36.


16. Munshi, Talat, et al. (2014): Logistic regression and cellular automata-based modelling of retail, commercial and residential development in the city of Ahmedabad. India. 39(8), 68-86.


17. Nong, Y., & Du, Q. (2011): Urban growth pattern modeling using logistic regression. Geo-spatial Information Science, 14(1), 62-67.


18. Rossiter, D. G., & Loza, A. (2012): Analyzing land cover change with logistic regression in R. University of Twente. Faculty of Geo-Information Science & Earth Observation (ITC), Enschede (NL), 34-40.


19. Sears, D. O., Huddy, L., & Schaffer, L. G. (1986): A schematic variant of symbolic politics theory, as applied to racial and gender equality. Political cognition, 159-202.


20. UnitedNations, 2014. Human Development Report, New York: 10017.


21. Wu, F. M. D. (2002): Urban expansion simulation of Southeast England using population surface modeling and cellular automata. Environment and Planing, 23(2), 103-126.


22. Wu, F. W. C. (1998): Simulation of land development through the integration of cellular automata and multicriteria evaluation. Environment and Planning B, Planning and Design, 23(2), 103-126.