IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v48y2021i7p1841-1859.html
   My bibliography  Save this article

Cellular automata for simulating land-use change with a constrained irregular space representation: A case study in Nanjing city, China

Author

Listed:
  • Jie Zhu

    (Nanjing Forestry University, China)

  • Yizhong Sun
  • Shuyin Song
  • Jing Yang

    (Nanjing Normal University, China)

  • Hu Ding

Abstract

Traditional cell-based cellular automata (CA) models use a regular cellular grid to represent geographic space, and new approaches to CA models have explored the use of a vector representation of space instead of a regular grid to characterize urban space more realistically. However, less attention has been paid to modeling the interaction between the geospatial information and the irregular cells. To date, the majority of spatial boundaries have been created by individual agencies in an uncoordinated manner. As a consequence, the potential uses of the data collected for land-use change models are limited. In this paper, we propose a new vector-based CA model based on a new constrained irregular space representation using the theory of hierarchical spatial reasoning. For dividing the geographic space considering different items, first land patches are considered as the minimum division unit; then aggregation rules, including attribute, geometric and boundary barrier constraints, are defined; and finally different levels of spatial units are formed through land patches based on aggregation rules. The proposed model is used to simulate the land-use changes in Nanjing, Jiangsu Province, China. The performance validation and comparison illustrate the feasibility of the proposed space representation in a CA model. By using this model, it is expected that the use of the real spatial boundaries that are employed in urban planning could help provide a flexible paradigm to consider various drivers or constraints for realistically simulating land-use changes.

Suggested Citation

  • Jie Zhu & Yizhong Sun & Shuyin Song & Jing Yang & Hu Ding, 2021. "Cellular automata for simulating land-use change with a constrained irregular space representation: A case study in Nanjing city, China," Environment and Planning B, , vol. 48(7), pages 1841-1859, September.
    • Handle: RePEc:sae:envirb:v:48:y:2021:i:7:p:1841-1859
    DOI: 10.1177/2399808320949889
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808320949889
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2399808320949889?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    2. Andreas Flache & Rainer Hegselmann, 2001. "Do Irregular Grids Make a Difference? Relaxing the Spatial Regularity Assumption in Cellular Models of Social Dynamics," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 4(4), pages 1-6.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    2. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    3. Ju-Sung Lee & Tatiana Filatova & Arika Ligmann-Zielinska & Behrooz Hassani-Mahmooei & Forrest Stonedahl & Iris Lorscheid & Alexey Voinov & J. Gareth Polhill & Zhanli Sun & Dawn C. Parker, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
    4. Ortigoza, Gerardo M., 2015. "Unstructured triangular cellular automata for modeling geographic spread," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 520-536.
    5. Margaret Gitau & Nathaniel Bailey, 2012. "Multi-Layer Assessment of Land Use and Related Changes for Decision Support in a Coastal Zone Watershed," Land, MDPI, vol. 1(1), pages 1-27, December.
    6. 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.
    7. Charlotte Shade & Peleg Kremer, 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies," Land, MDPI, vol. 8(2), pages 1-19, February.
    8. Wu, Wei & Yeager, Kevin M. & Peterson, Mark S. & Fulford, Richard S., 2015. "Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM)," Ecological Modelling, Elsevier, vol. 303(C), pages 55-69.
    9. Yi Lu & Shawn Laffan & Chris Pettit & Min Cao, 2020. "Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia," Environment and Planning B, , vol. 47(9), pages 1605-1621, November.
    10. 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.
    11. Prashanti Sharma & Rajesh Bahadur Thapa & Mir Abdul Matin, 2020. "Examining forest cover change and deforestation drivers in Taunggyi District, Shan State, Myanmar," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5521-5538, August.
    12. Cláudia M. Viana & Jorge Rocha, 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    13. Javier Muro & Leo Zurita-Arthos & José Jara & Esteban Calderón & Richard Resl & Andreas Rienow & Valerie Graw, 2020. "Earth Observation for Settlement Mapping of Amazonian Indigenous Populations to Support SDG7," Resources, MDPI, vol. 9(8), pages 1-17, August.
    14. van Vliet, Jasper & Hagen-Zanker, Alex & Hurkens, Jelle & van Delden, Hedwig, 2013. "A fuzzy set approach to assess the predictive accuracy of land use simulations," Ecological Modelling, Elsevier, vol. 261, pages 32-42.
    15. Elsenbroich, Corinna & Payette, Nicolas, 2020. "Choosing to cooperate: Modelling public goods games with team reasoning," Journal of choice modelling, Elsevier, vol. 34(C).
    16. Ramita Manandhar & Inakwu O.A. Odeh & Tihomir Ancev, 2014. "Assessment of Spatial-Temporal Expansion of Built-up and Residential-Commercial Dwellings with Some Economic Implications: A Case Study in the Lower Hunter of Eastern Australia," Land, MDPI, vol. 3(1), pages 1-21, March.
    17. Gustavo Larrea‐Gallegos & Ian Vázquez‐Rowe, 2022. "Exploring machine learning techniques to predict deforestation to enhance the decision‐making of road construction projects," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 225-239, February.
    18. Yongjiu Feng & Jiafeng Wang & Xiaohua Tong & Yang Liu & Zhenkun Lei & Chen Gao & Shurui Chen, 2018. "The Effect of Observation Scale on Urban Growth Simulation Using Particle Swarm Optimization-Based CA Models," Sustainability, MDPI, vol. 10(11), pages 1-20, November.
    19. van Vliet, Jasper & Bregt, Arnold K. & Hagen-Zanker, Alex, 2011. "Revisiting Kappa to account for change in the accuracy assessment of land-use change models," Ecological Modelling, Elsevier, vol. 222(8), pages 1367-1375.
    20. Luoman Pu & Jiuchun Yang & Lingxue Yu & Changsheng Xiong & Fengqin Yan & Yubo Zhang & Shuwen Zhang, 2021. "Simulating Land-Use Changes and Predicting Maize Potential Yields in Northeast China for 2050," IJERPH, MDPI, vol. 18(3), pages 1-21, January.

    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:sae:envirb:v:48:y:2021:i:7:p:1841-1859. 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: SAGE Publications (email available below). General contact details of provider: .

    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.