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Manuscript ID : JZPM-1808-3077 (R1) Visit : 105 Page: 39 - 56

20.1001.1.22516735.1398.9.35.4.1

Article Type: Original Research

Prediction of future land use changes in Tehran Metropolitan Region (TMR) with the combination of logistic regression, Markov chain, and cellular automata

Subject Areas : Regional Planning

هاشم داداش پور 1 , Hossein Panahi 2 , Ali Shamsoddini 3

1 - عضو هیات علمی دانشگاه تربیت مدرس
2 - Department of Urban and Regional Planning, Faculty of Art and Architecture, Tarbiat Modares Uinversity, Tehran, Islamic Republic of Iran
3 - Department of Remote Sensing, Faculty of Human Science, Tarbiat Modares University, Tehran, Islamic Republic of Iran

Received: 2018-08-17 Accepted : 2019-08-18 Published : 2019-09-23

Keywords: Land use change, Logistic Regression, spatial simulation, Automata Cellular, Tehran metropolitan region,

Abstract :

The metropolitan regions, especially in developing countries, have experienced rapid population growth due to the absorption of economic immigrants, which have had destructive effects on change in land use environment in the past decades. The current planning process of land use makes it necessary to identify the future pattern of land use on the basis of appropriate criteria with the natural, economic and social environment. Changes in land use occur in a dynamic and complex process due to the mutual effect of natural, social and economic factors and the impact of each factor in different time and scales. Simulation as an efficient way to understand these changes and assess the potential impact of land use changes on the ecology system and future patterns of change is proposed. Accordingly, this study aims to predict future land use changes in the Tehran metropolitan region. In the first step of land use changes in the region. By identifying the effect of changing factors and potential of land use, land use changes are drawn based on past years for future years. So, for this purpose, first, of 1985, 2000 and 2015, using ENVI software and SVM method are classified and analyzed. In the second step, the effect of factors effect is determined after recognition of the factors driving change with the logistic regression method. The prediction of future changes is simulated by a combination of Markov and cellular automata methods for future changes in 2030 and 2045. the results of the study show that the trend of last changes in Tehran metropolitan region has led to the destruction of pastures, agricultural land, and arid land and this trend will increase the damage to the built-up areas of natural and environmental resources, which have the greatest impact on the change in roads, distance from built areas and natural factors. Changes in the 2030s and 2045s will be the trend in the past, and the developed regions will increase, and in the western, southern and eastern axes, the most changes will occur.

References:


  1. داداش پور, هاشم و نریمان جهانزاد. (1394). شبیه سازی تغییرات آتی کاربری زمین بر اساس الگوی بهینۀ اکولوژیک در مجموعۀ شهری مشهد. پژوهش های جغرافیایی برنامه ریزی شهری,سال 3،شماره 3, تهران، صص 343–359.
    2.
  2. داداش‌پور,  ‌هاشم و فردیس سالاریان. (1394). تحلیل تاثیر پراکنده‌رویی بر تغییر کاربری زمین در منطقه شهری ساری. پژوهش های جغرافیایی برنامه ریزی شهری,سال  7، شماره 3,صص 145–164.
    3.
  3. داداش‌پور,  ‌هاشم و امیررضا میری لواسانی. (1394). تحلیل الگوهای فضایی پراکنده‌رویی در منطقه کلان‌شهری تهران. برنامه ریزی فضایی (جغرافیا),سال  16،شماره 5 , اصفهان، صص 123– 146.
    4.
  4. داداش پور, هاشم, خیرالدین, رضا, یعقوب خانی, مرتضی و بهنام چمنی. (1393). مدلسازی تغییرات کاربری زمین در کلانشهر تهران با استفاده از مدل ,MOLAND.فصلنامه برنامه ریزی منطقه ای، سال 4،شماره 16، مرودشت، صص 49-64.
    5.
  5. شمس الدینی، علی و محمدرضا امیری فهلیانی.(1394). بررسی عوامل اثرگذار بر مدیریت کاربری اراضی روستایی در شهرستان ممسنی(با استفاده از مدل ترکیبیTOPSIS-SWOT)، فصلنامه برنامه­ریزی منطقه­ای، سال5، شماره19، مرودشت، صص85-100
    6.
  6. عبدی دانشپور, زهره و مسعود تارنتاش. (2017). آشکارسازی دگرگونی کاربرد زمین: تحلیل ویژگی‌های گسترش برنامه‌ریزی‌نشده در منطقة کلان‌شهری تهران. نشریه هنرهای زیبا- معماری و شهرسازی, سال 33، شماره3, صص 15–31.
    7.
  7. کاویانی,  ‌آزاده, فرهودی,  ‌رحمت‌الله, و آزیتا رجبی. (1394). تحلیل الگوی رشد شهر تهران با رویکرد بوم شناسی سیمای سرزمین. پژوهش های جغرافیایی برنامه ریزی شهری, سال 9، شماره3, تهران، تهران، صص407–429
    8.
  8. محمودزاده, حسن و قهرمان خوش روی. (1394). کاربرد رگرسیون لجستیک در مدل سازی توسعه شهری(مطالعه موردی: منطقه شهری بناب). فصلنامه مطالعات شهری, شماره 14, سنندج، صص31–46
    9.
  9. سرشماری عمومی نفوس و مسکن،1395، مرکز آمار ایران
    10.
  10. سازمان مدیریت و برنامه ریزی کشور،آمایش استان تهران، 1388
    11.
  11. منصوریان, حسین. (1395). پویش جمعیتی و الگوهای پوشش زمین در منطقۀ کلان‌شهری تهران. پژوهش های جغرافیایی برنامه ریزی شهری, سال 4، شماره 4, تهران، صص613–633
    12.
  12. وزرات کشور،تقسیمات اداری و سیاسی کشور،1393

    1. Aburas, M. M., Ho, Y. M., Ramli, M. F., & Ash’aari, Z. H. (2016). The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 52, pp380–389.
      2.
    2. Al-sharif, A., & Pradhan, B. (2014). Monitoring and predicting land use change in Tripoli Metropolitan City using an integrated Markov chain and cellular automata models in GIS. Arabian Journal of Geosciences, 7(10), pp4291–4301.
      3.
    3.  Amini Parsa, V., & Salehi, E. (2016). Spatio-temporal analysis and simulation pattern of land use/cover changes, case study: Naghadeh, Iran. Journal of Urban Management, 5(2), pp43–51.
      4.
    4. Arsanjani, J. J., Helbich, M., Kainz, W., & Boloorani, A. D. (2012). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21(1), pp265–275.
      5.
    5. Arsanjani, J. J., Kainz, W., & Mousivand, A. J. (2011). Tracking dynamic land-use change using spatially explicit Markov chain based on cellular automata: The case of Tehran. International Journal of Image and Data Fusion, 2(4), pp329–345.
      6.
    6. Cheng, J., & Masser, I. (2004). Understanding spatial and temporal processes of urban growth: Cellular automata modelling. Environment and Planning B: Planning and Design, 31(2), pp167–194.
      7.
    7. Dadashpoor, H., & Alidadi, M. (2017). Towards decentralization: Spatial changes of employment and population in Tehran Metropolitan Region, Iran. Applied Geography, 85, pp51–61.
      8.
    8. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran. Sustainable Cities and Society, 47, 101502.
      9.
    9. Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Land use change, urbanization, and change in landscape pattern in a metropolitan area. Science of The Total Environment, 655, pp707-719.
      10.
    10. Dadashpoor, H., & Salarian, F. (2018). Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran. Environment, Development and Sustainability, pp1-22.
      11.
    11. Dadashpoor, H., & Ahani, S. (2019). Land tenure-related conflicts in peri-urban areas: A review. Land Use Policy, 85, pp218-229.
      12.
    12. Dadashpoor, H., & Ahani, S. (2019). A conceptual typology of the spatial territories of the peripheral areas of metropolises. Habitat International, 90, 102015.
      13.
    13. Dadashpoor, H., & Nateghi, M. (2017). Simulating spatial pattern of urban growth using GIS-based SLEUTH model: a case study of eastern corridor of Tehran metropolitan region, Iran. Environment, Development and Sustainability, 19(2), pp527–547.
      14.
    14. El-Khoury, A. (2012). Modeling Land-Use Changes in the South Nation Watershed using Dyna-CLUE. University of Ottawa.
      15.
    15. Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20–22), pp3761–3772.
      16.
    16. Han, H., Yang, C., & Song, J. (2015). Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China. Sustainability, 7(4), pp4260–4279.
      17.
    17. Han, Y., & Jia, H. (2017). Simulating the spatial dynamics of urban growth with an integrated modeling approach: A case study of Foshan, China. Ecological Modelling, 353, pp107–116.
      18.
    18. Hosseinali, F., Alesheikh, A. A., & Nourian, F. (2013). Agent-based modeling of urban land-use development, case study: Simulating future scenarios of Qazvin city. Cities, 31, pp105–113.
      19.
    19. Hu, Y., Zheng, Y., & Zheng, X. (2013). Simulation of land-use scenarios for Beijing using CLUE-S and Markov composite models. Chinese Geographical Science, 23(1), 92–100.
      20.
    20. Irwin, E. G., & Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85(1–3), pp7–24.
      21.
    21. Jiang, W., Chen, Z., Lei, X., He, B., Jia, K., & Zhang, Y. (2016). Simulation of urban agglomeration ecosystem spatial distributions under different scenarios: A case study of the Changsha-Zhuzhou-Xiangtan urban agglomeration. Ecological Engineering, 88(April), pp112–121.
      22.
    22. Koomen, E., Koekoek, A., & Dijk, E. (2011). Simulating Land-use Change in a Regional Planning Context. Applied Spatial Analysis and Policy, 4(4), pp223–247.
      23.
    23. Ku, C.-A. (2016). Incorporating spatial regression model into cellular automata for simulating land use change. Applied Geography, 69, pp1–9.
      24.
    24. Lambin, E. F., Geist, H., & Rindfuss, R. R. (2004). Chapter 1 Introduction : Local Processes with Global Impacts. Land-Use and Land-Cover Change, (Turner), pp1–8.
      25.
    25. Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1), pp159-174.
      26.
    26. Liu, X., Liang, X., Li, X., Xu, X., Ou, J., Chen, Y., Pei, F. (2017). A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects. Landscape and Urban Planning, 168(October), pp94–116.
      27.
    27. Manuschevich, D., & Beier, C. M. (2016). Simulating land use changes under alternative policy scenarios for the conservation of native forests in south-central Chile. Land Use Policy, 51, pp350–362.
      28.
    28. Oğuz, H. (2004). Modeling urban growth and land use/land cover change in the Houston Metropolitan Area from 2002-2030, (May), pp1–163.
      29.
    29. Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrammetric Engineering and Remote Sensing,66(8),1011-1016
      30.
    30. Pontius, R. G., & Millones, M. (2011). Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32(15), pp4407–4429.
      31.
    31. Puertas, O. L., Henríquez, C., & Meza, F. J. (2014). Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010-2045. Land Use Policy, 38, pp415–425.
      32.
    32. Rounsevell, M. D. A, Pedroli, B., Erb, K. H., Gramberger, M., Busck, A. G., Haberl, H., Wolfslehner, B. (2012). Challenges for land system science. Land Use Policy, 29(4), pp899-910.
      33.
    33. Rounsevell, M. D. A., Reginster, I., Araújo, M. B., Carter, T. R., Dendoncker, N., Ewert, F., Tuck, G. (2006). A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems and Environment, 114(1), pp57–68.
      34.
    34. Shafizadeh-Moghadam, H., Asghari, A., Tayyebi, A., & Taleai, M. (2017). Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth. Computers, Environment and Urban Systems, 64, pp297–308.
      35.
    35. 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), pp525–552.
      36.
    36. Sun, P., Xu, Y., Yu, Z., Liu, Q., Xie, B., & Liu, J. (2016). Scenario simulation and landscape pattern dynamic changes of land use in the Poverty Belt around Beijing and Tianjin: A case study of Zhangjiakou city, Hebei Province. Journal of Geographical Sciences, 26(3), pp272-296
      37.
    37. Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S. S. A. (2002). Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management, 30(3), pp391–405.
      38.
    38. Wu, Q., Li, H. qing, Wang, R. song, Paulussen, J., He, Y., Wang, M., Wang, Z. (2006). Monitoring and predicting land use change in Beijing using remote sensing and GIS. Landscape and Urban Planning, 78(4), pp322–333.
      39.
    39. Zarei, A., Dadashpoor, H., & Amini, M. (2016). Determination of the optimal land use allocation pattern in Nowshahr County, Northern Iran. Environment, Development, and Sustainability, 18(1), 37–56.
      40.


 

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