Simulation of Urban Development in Tabriz Using CA-Markov Model and Multi-criteria Decision Making

Document Type : Research Paper

Authors

1 PhD Student in Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

2 Assistant Professor of Remote Sensing and Geographic Information Systems, Faculty of Geography, University of Tehran, Iran

3 Assistant Professor of Remote Sensing and Geographic Information Systems Faculty of Geography, University of Tehran

Abstract

Introduction
Today, developed and developing countries are experiencing rapid changes and growth in population. It is essential to have a suitable sustainable urban growth management and urban development planning to better understand patterns of urban growth. Satellite remote sensing in conjunction with Geographic Information Systems (GIS) has been recognized as a powerful and effective tool in detecting land use and land cover changes. Satellite remote sensing is a potentially powerful means of monitoring land-use change at high temporal resolution and lower costs than those associated with the use of traditional methods. This provides multi-spectral and multitemporal data that can be used to quantify the type, amount and location of land use and land cover changes. CA-Markov model is a dynamic model to simulate urban growth and land use changes maps obtained from a combination of automated cells and Markov chain. Markov chain is spatial sequence of random processes in which the result of any process at any time, only next time, will depend on the outcome of the process. Using Markov chain model, we have initially calculated the possibility of changing land use map classes to each other in terms of probability matrix applications based on area changes between time t0 and t1. Markov model output, literally, is the non-place model where there is no knowledge of the geographical location of land uses. To predict the location of land at the time t1, automated cell techniques can be used with this model. In this study, we are using Landsat images of TM5 and OLI applying the capabilities of IDRISI software and GIS to estimate changes in urban growth areas in Tabriz during 30 years, from 1973 to 2013.
 
Methodology
In this study, the tools available in IDRISI SELVA software and GIS functions have been used to simulate the changes in land use and urban growth in Tabriz in three main stages:
Tabriz is one of the major cities in Iran and the capital of East Azerbaijan province. The Tabriz City is the the third largest city of Iran following Tehran and Mashhad and it is a major hub for business, communication, commerce, and political, industrial, cultural and military activities.
In this study, in order to identify and create land use maps of Tabriz, images of Landsat in the years 1973, 1993 and 2013 were obtained from the Geological Society of America (USGS).
The land use map of the city of Tabriz has been categorized for three major landuses including urban areas, agricultural land and orchards, and barren areas. In order to make land use suitability map, we have also used topographic maps of 1: 2000 and 1: 50000 for the sheet of Tabriz, from the national cartographic center of Iran. The transition probability matrix is calculated using Markov chain analysis: for this purpose, Landsat satellite images in 1993, 2003 and 2013, using the techniques of USGS classified images to produce the maps of urban development. 

The calculation of urban suitability map using Multi Criteria Evaluation and Analytical Hierarchy Process (AHP)
Urban growth modeling with data collection: 1. urban areas Map 1993 as the base map. 2- Suitability map of urban growth in 2003. 3- The transition probability matrix from 2003 to 1993 was also combined by the operator, CA location. 

 
Results and discussion
The study area, Tabriz city, is located northwest Iran. The maps of land use and urban growth have been created using satellite image processing techniques and supervised classification. The overall accuracy of the land use/cover maps for 1972, 2003 and 2013 were 82 %, 85 % and 90 %, respectively. The Kappa index for the 1972, 1990 and 2006 of the land use/cover maps were found to be 76 %, 79 % and 89 %, respectively. The transfer matrix regions from 2003 to 2013 changed 12 percent of rural areas into urban. Real and simulated map of 2013 is shown in Figure 1. The overall accuracy and Kappa index between actual and predicted maps of 2013 was, respectively, 91 and 81. The 2028 map was produced using ca-markov model. In the map simulated for 2028, urban areas will grow 25 percent and from 11697 hectares to 14690. 
 
Conclusion
In the present study, Markov and CA- Markov models were helpful for predicting land use/cover changes and urban growth in 2013 and 2028. The results showed that the rate of the population growth in the areas built in the city of Tabriz surpassed the value. The gap between urban growth and population doubled in this period and shows that development was more horizontal than vertical during this period. In the map, several areas of industrial, commercial and residential development was found. The outcomes of this research indicate that Landsat TM images can be effectively used for generating accurate land use/cover maps as the overall accuracies of all the generated land cover maps were about 80 %. According to the results of the CA-Markov model, urban expansion will occur in the future. The combination of satellite remote sensing, GIS and Markov models provides useful information on land use/cover dynamics and trends which could help policy makers make better decisions for the future for the study area. The provided future projection could be effectively used for land use planning, decision making and land management, especially if its use is confined more to general trends than to specific land-use locations, where accuracy was lower. The predictive power of the CA-Markov model, especially in predicting the location of pixels, was not very high in this research but, in general, Markov models have indicated the capability for the prediction of land cover/land use trends. 

Keywords

Main Subjects

References

  1. خاکپور، برات‌علی و عقیل امیری، ۱۳۸۹، شهرهای جدید ایران: اهداف اولیه، واقعیت امروز، دومین کنفرانس برنامه‌ریزی و مدیریت شهری، مشهد، دانشگاه فردوسی.

Khappur, B., and Amiri A., 1389, New Iranian Cities, Primary Objectives, Today's Reality, Second Conference on Urban Planning and Management, Mashhad, Ferdowsi University of Mashhad.

  1. خوش‌گفتار محمدمهدی و  طالعی محمد، 1389، شبیه‌سازی رشد شهری تهران با استفاده از مدل CA-Markov، نشریه سنجش‌ازدور و GIS ایران، شمارة 2(6)، دورة 2، صص 17-33.

Khosh Goftar M., and Talie M., 2010, Simulation of Tehran's Urban Growth Using the CA-Markov Model, Remote sensing and GIS of Iran,  Vol. 2 , No., 2, pp. 17-33.

  1. ظاهری، محمد، 1387، سنجش گستردگی شهری و تأثیر آن بر تغییر کاربری اراضی سواحل جنوبی دریای مازندران با GIS، پایان‌نامة کارشناسی ارشد، دانشگاه زنجان.

Zaheri M., 2008, Measurement of urban sprawl and its impact on land use change in the southern shores of Mazandaran Sea with GIS, Master's thesis, Zanjan University.

  1. پورتال شهرداری تبریز، 1396، WWW. TABRIZ.IR.

The municipality portal of Tabriz, 2017, WWW. TABRIZ.IR

  1. علی‌محمدی عباس، موسیوند علی‌جعفر و سیاوش شایان، 1389، پیش‌بینی تغییرات کاربری اراضی و پوشش زمین با استفاده از تصاویر ماهواره‌ای و مدل زنجیره‌ای مارکوف، نشریة برنامه‌ریزی و آمایش فضا، دورة چهاردهم، شمارة 3، صص 117-129.

AliMohammadi A., Musivand A. J, and Shayan S., 2010, Prediction of land use change and land cover using satellite imagery and Markov chain model, Journal of Planning and Space Design, Vol. 14, No. 3, pp.117-129

  1. مرکز آمار ایران، 1391، گزارش نتایج سرشماری عمومی نفوس و مسکن 1390، معاونت برنامه‌ریزی ریاست جمهوری، تهران.

Statistical Center of Iran., 2011, Report of the Results of the Population and Housing Public Census 2011, Vice Presidential Planning, Tehran

 

  1. Alberti, M., 2005, The effects of urban patterns on ecosystem function, International regional science review, Vol. 28, No. 2, pp.168-192.
  2. Arsanjani, J. J., and et al., 2013, Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation,  Vol. 21 , No. 21, pp. 265-275.
  3. Brueckner, J. K., 2000, International Regional Science. International regional science review, Vol. 23,  No. 2, pp. 160-171.
  4. Burchell, R. W., and Mukherji, S., 2003, Conventional development versus managed growth: the costs of sprawl. American Journal of Public Health, Vol. 93,‌ No. 9, pp. 1534-1540.
  5. Frenkel, A., and Ashkenazi, M., 2008, The integrated sprawl index: measuring the urban landscape in Israel. The Annals of Regional Science, Vol. 42,  No. 1, pp. 99-121.
  6. Henríquez, C., Azócar, G., and Romero, H., 2006, Monitoring and modeling the urban growth of two mid-sized Chilean cities. Habitat International, Vol. 30, No. 4, pp. 945-964.
  7. Herold, M., Couclelis, H., and Clarke, K. C., 2005, The role of spatial metrics in the analysis and modeling of urban land use change. Computers, Environment and Urban Systems, Vol. 29, No. 4, pp. 369-399.
  8. Jensen, J. R., and Lulla, K., 1987, Introductory digital image processing: a remote sensing perspective.
  9. Kamusoko, C., Gamba, J., and Murakami, H., 2013, Monitoring urban spatial growth in harare metropolitan province, zimbabwe. Advances in Remote Sensing, Vol. 24,  No. 4, pp.126-139.
  10. Li, X., and Yeh, A. G. O., 1998, Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta, International Journal of Remote Sensing, Vol.19, No. 8, pp.1501-1518.
  11. Longley, P., and Batty, M., 2003, Advanced spatial analysis: the CASA book of GIS. ESRI, Inc.
  12. Madhavan, B. and et al., 2001, Appraising the anatomy and spatial growth of the Bangkok Metropolitan area using a vegetation-impervious-soil model through remote sensing. International Journal of Remote Sensing, Vol. 22, No. 5, pp. 789-806.
  13. Mills, D. E., 1981, Growth, speculation and sprawl in a monocentric city, Journal of Urban Economics, Vol. 10, No. 2, pp. 201-226.
  14. Norris, J. R., 1998, Markov chains. Reprint of 1997 original, Cambridge Series in Statistical and Probabilistic Mathematics, 2.
    1. Richardson, H. W., and Bae, C. H. C. (Eds), 2004, Urban Sprawl in Western Europe and the United States. London: Ashgate,  Vol. 22, No. 2, pp.115-124.
    2. Saaty, T. L., 1988, What is the analytic hierarchy process?, Springer Berlin Heidelberg, pp.109-121.
    3. Sang, L., and et al., 2011, Simulation of land use spatial pattern of towns and villages based on CA–Markov model, Mathematical and Computer Modelling, Vol. 54, No. 3, pp. 938-943.
    4. Sirakoulis, G. C., Karafyllidis, I., and Thanailakis, A., 2000, A cellular automaton model for the effects of population movement and vaccination on epidemic propagation, Ecological Modelling,Vol. 133, No. 3, pp. 209-223.
    5. Sturm, R., and Cohen, D. A., 2004, Suburban sprawl and physical and mental health, Public health, Vol.118, No.7, pp. 488-496.
    6. Sudhira, H. S., Ramachandra, T. V., and Jagadish, K. S., 2004, Urban sprawl: metrics, dynamics and modelling using GIS, International Journal of Applied Earth Observation and Geoinformation, Vol. 5, No. 1, pp.29-39.
    7. Sullivan, W. C., and Lovell, S. T., 2006, Improving the visual quality of commercial development at the rural–urban fringe, Landscape and Urban Planning, Vol. 77, No.1, pp.152-166.
    8. Sun, H., Forsythe, W., and Waters, N., 2007, Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada, Networks and spatial economics, Vol.7, No. 4, pp. 353-376.
    9. Sunar, F., 1998, An analysis of changes in a multi-date data set: a case study in the Ikitelli area, Istanbul, Turkey, International Journal of Remote Sensing, Vol.19, No. 2, pp.225-235.
    10. Torrens, P.; Alberti, M., 2000, Measuring Urban Sprawl; Working Paper Series, Centre for Advances Spatial Analysis, University College London: London, UK.
    11. United Nations Environment Programme., 2014, State of World Population Report 2014 (e), United Nations Publications.
    12. Weng, Q., 2002, Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling, Journal of environmental management, Vol. 64, No. 3, pp. 273-284.
    13. White, R., and et al., 2000, Developing an urban land use simulator for European cities, In Proceedings of the Fifth EC GIS Workshop: GIS of Tomorrow. European Commission Joint Research Centre, pp. 179-190.
    14. Williams, K., Burton, E., and Jenks, M. 2000, Achieving sustainable urban form: an introduction. Achieving sustainable urban form, 1-5.
    15. Xiyong, H., Bin, C., & Xinfang, Y., 2004, Land use change in Hexi corridor based on CA-Markov methods, Transactions of The Chinese Society of Agricultural Engineering, Vol. 5, No. 6, pp. 926-941.
    16. Xu, C., and et al., 2007, The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China, Landscape ecology, Vol. 22, No. 6, pp.925-937.
    17. Yeh, A. G. O., and Xia, L., 2001, Measurement and monitoring of urban sprawl in a rapidly growing region using entropy, Photogrammetric engineering and remote sensing, Vol.67,  No.1, pp. 83-90.
Human Geography Research
Volume 50, Issue 1
April 2018
Pages 217-231

Files

History

  • Receive Date: 10 January 2017
  • Revise Date: 17 September 2017
  • Accept Date: 18 September 2017

Share

How to cite

Statistics