IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i20p5579-d274971.html
   My bibliography  Save this article

Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

Author

Listed:
  • Ismail Ercument Ayazli

    (Department of Geomatics Engineering, Sivas Cumhuriyet University, 58140 Sivas, Turkey)

Abstract

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

Suggested Citation

  • Ismail Ercument Ayazli, 2019. "Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods," Sustainability, MDPI, vol. 11(20), pages 1-14, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5579-:d:274971
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/20/5579/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/20/5579/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. K C Clarke & S Hoppen & L Gaydos, 1997. "A Self-Modifying Cellular Automaton Model of Historical Urbanization in the San Francisco Bay Area," Environment and Planning B, , vol. 24(2), pages 247-261, April.
    2. Claire A Jantz & Scott J Goetz & Mary K Shelley, 2004. "Using the Sleuth Urban Growth Model to Simulate the Impacts of Future Policy Scenarios on Urban Land Use in the Baltimore-Washington Metropolitan Area," Environment and Planning B, , vol. 31(2), pages 251-271, April.
    3. Yilun Liu & Yueming Hu & Shaoqiu Long & Luo Liu & Xiaoping Liu, 2017. "Analysis of the Effectiveness of Urban Land-Use-Change Models Based on the Measurement of Spatio-Temporal, Dynamic Urban Growth: A Cellular Automata Case Study," Sustainability, MDPI, vol. 9(5), pages 1-15, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gizem Mestav Sarica & Tinger Zhu & Wei Jian & Edmond Yat-Man Lo & Tso-Chien Pan, 2021. "Spatio-temporal dynamics of flood exposure in Shenzhen from present to future," Environment and Planning B, , vol. 48(5), pages 1011-1024, June.
    2. Anahi Molar-Cruz & Lukas D Pöhler & Thomas Hamacher & Klaus Diepold, 2022. "Who settles where? Simulating urban growth and socioeconomic level using cellular automata and random forest regression," Environment and Planning B, , vol. 49(6), pages 1697-1714, July.
    3. Derya Ozturk & Nergiz Uzel-Gunini, 2022. "Investigation of the effects of hybrid modeling approaches, factor standardization, and categorical mapping on the performance of landslide susceptibility mapping in Van, Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2571-2604, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yishao Shi & Jie Wu & Shouzheng Shi, 2017. "Study of the Simulated Expansion Boundary of Construction Land in Shanghai Based on a SLEUTH Model," Sustainability, MDPI, vol. 9(6), pages 1-15, May.
    2. Taher Osman & Prasanna Divigalpitiya & Takafumi Arima, 2016. "Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on land use in the Giza Governorate, Greater Cairo Metropolitan region," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 20(3), pages 407-426, September.
    3. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    4. Ayanlade, Ayansina & Howard, Michael T., 2017. "Understanding changes in a Tropical Delta: A multi-method narrative of landuse/landcover change in the Niger Delta," Ecological Modelling, Elsevier, vol. 364(C), pages 53-65.
    5. Liu, Dongya & Zheng, Xinqi & Zhang, Chunxiao & Wang, Hongbin, 2017. "A new temporal–spatial dynamics method of simulating land-use change," Ecological Modelling, Elsevier, vol. 350(C), pages 1-10.
    6. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    7. Eda Ustaoglu & Brendan Williams & Laura O. Petrov & Harutyun Shahumyan & Hedwig Van Delden, 2017. "Developing and Assessing Alternative Land-Use Scenarios from the MOLAND Model: A Scenario-Based Impact Analysis Approach for the Evaluation of Rapid Rail Provisions and Urban Development in the Greate," Sustainability, MDPI, vol. 10(1), pages 1-34, December.
    8. A’kif AL-FUGARA & Abdel Rahman AL-SHABEEB & Yahya AL-SHAWABKEH & Hani AL-AMOUSH & Rida AL-ADAMAT, 2018. "Simulation And Prediction Of Urban Spatial Expansion In Highly Vibrant Cities Using The Sleuth Model: A Case Study Of Amman Metropolitan, Jordan," Theoretical and Empirical Researches in Urban Management, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(1), pages 37-56, February.
    9. Alireza Salahi Moghadam & Ali Soltani & Bruno Parolin, 2018. "Transforming and changing urban centres: the experience of Sydney from 1981 to 2006," Letters in Spatial and Resource Sciences, Springer, vol. 11(1), pages 37-53, March.
    10. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    11. Jaekyung Lee & Galen Newman & Yunmi Park, 2018. "A Comparison of Vacancy Dynamics between Growing and Shrinking Cities Using the Land Transformation Model," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    12. Bernardino Romano & Lorena Fiorini & Alessandro Marucci, 2019. "Italy without Urban ‘Sprinkling’. A Uchronia for a Country that Needs a Retrofit of Its Urban and Landscape Planning," Sustainability, MDPI, vol. 11(12), pages 1-16, June.
    13. Acevedo, Miguel A. & Marcano, Mariano & Fletcher, Robert J., 2012. "A diffusive logistic growth model to describe forest recovery," Ecological Modelling, Elsevier, vol. 244(C), pages 13-19.
    14. Wickramasuriya, Rohan Chandralal & Bregt, Arnold K. & van Delden, Hedwig & Hagen-Zanker, Alex, 2009. "The dynamics of shifting cultivation captured in an extended Constrained Cellular Automata land use model," Ecological Modelling, Elsevier, vol. 220(18), pages 2302-2309.
    15. Mahdis Sadat & Mahmood Zoghi & Bahram Malekmohammadi, 2020. "Spatiotemporal modeling of urban land cover changes and carbon storage ecosystem services: case study in Qaem Shahr County, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 8135-8158, December.
    16. Aquilué, Núria & De Cáceres, Miquel & Fortin, Marie-Josée & Fall, Andrew & Brotons, Lluís, 2017. "A spatial allocation procedure to model land-use/land-cover changes: Accounting for occurrence and spread processes," Ecological Modelling, Elsevier, vol. 344(C), pages 73-86.
    17. Erqi Xu & Yimeng Chen, 2019. "Modeling Intersecting Processes of Wetland Shrinkage and Urban Expansion by a Time-Varying Methodology," Sustainability, MDPI, vol. 11(18), pages 1-24, September.
    18. Shaoying Li & Xiaoping Liu & Zhigang Li & Zhifeng Wu & Zijun Yan & Yimin Chen & Feng Gao, 2018. "Spatial and Temporal Dynamics of Urban Expansion along the Guangzhou–Foshan Inter-City Rail Transit Corridor, China," Sustainability, MDPI, vol. 10(3), pages 1-18, February.
    19. Merlin, Louis A. & Levine, Jonathan & Grengs, Joe, 2018. "Accessibility analysis for transportation projects and plans," Transport Policy, Elsevier, vol. 69(C), pages 35-48.
    20. Jun Ren & Wei Zhou & Xuelu Liu & Liang Zhou & Jing Guo & Yonghao Wang & Yanjun Guan & Jingtian Mao & Yuhan Huang & Rongrong Ma, 2019. "Urban Expansion and Growth Boundaries in an Oasis City in an Arid Region: A Case Study of Jiayuguan City, China," Sustainability, MDPI, vol. 12(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:20:p:5579-:d:274971. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.