IDEAS home Printed from https://ideas.repec.org/a/sae/envirb/v47y2020i9p1605-1621.html
   My bibliography  Save this article

Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia

Author

Listed:
  • Yi Lu

    (University of New South Wales, Australia)

  • Shawn Laffan

    (University of New South Wales, Australia)

  • Chris Pettit

    (University of New South Wales, Australia)

  • Min Cao

Abstract

The loss of accuracy in vector-raster conversion has always been an issue for land use change models, particularly for raster based Cellular Automata models. Here we describe a vector-based cellular automata (CA) model that uses land parcels as the basic unit of analysis, and compare its results with a raster CA model. Transition rules are calibrated using an artificial neural network (ANN) and historical land use data. Using Ipswich City in Queensland, Australia as the study area, the simulation results show that the vector and raster CA models achieve 96.64% and 93.88% producer’s spatial accuracy, respectively. In addition, the vector CA model achieves a higher kappa coefficient and more consistent frequency of misclassification, while also having faster processing times. Consequently, the vector-based CA model can be applied to explore regulations of land use transformation in urban growth process, and provide a better understanding of likely urban growth to inform city planners.

Suggested Citation

  • 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.
    • Handle: RePEc:sae:envirb:v:47:y:2020:i:9:p:1605-1621
    DOI: 10.1177/2399808319830971
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/2399808319830971
    Download Restriction: no
    File URL: https://libkey.io/10.1177/2399808319830971?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. Bo Huang & Chenglin Xie & Richard Tay & Bo Wu, 2009. "Land-Use-Change Modeling Using Unbalanced Support-Vector Machines," Environment and Planning B, , vol. 36(3), pages 398-416, June.
    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.
    3. Ying Long & Kang Wu, 2017. "Simulating Block-Level Urban Expansion for National Wide Cities," Sustainability, MDPI, vol. 9(6), pages 1-19, May.
    4. Yan Liu & Yongjiu Feng & Robert Gilmore Pontius, 2014. "Spatially-Explicit Simulation of Urban Growth through Self-Adaptive Genetic Algorithm and Cellular Automata Modelling," Land, MDPI, vol. 3(3), pages 1-20, July.
    5. F Wang, 1994. "The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment," Environment and Planning A, , vol. 26(2), pages 265-284, February.
    6. Jun Yang & Weiling Liu & Yonghua Li & Xueming Li & Quansheng Ge, 2018. "Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian," Complexity, Hindawi, vol. 2018, pages 1-17, August.
    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. Svitlana Pyrohova & Jiafei Hu & Jonathan Corcoran, 2023. "Urban land use transitions: Examining change over 19 years using sequence analysis. The case of South-East Queensland, Australia," Environment and Planning B, , vol. 50(9), pages 2579-2593, November.

    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. Pankaj Bajracharya & Selima Sultana, 2022. "Examining the Use of Urban Growth Boundary for Future Urban Expansion of Chattogram, Bangladesh," Sustainability, MDPI, vol. 14(9), pages 1-21, May.
    2. Lin, Huiyan & Lu, Kang Shou & Espey, Molly & Allen, Jeffery, 2005. "Modeling Urban Sprawl and Land Use Change in a Coastal Area-- A Neural Network Approach," 2005 Annual meeting, July 24-27, Providence, RI 19364, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Peng Zeng & Sihui Wu & Zongyao Sun & Yujia Zhu & Yuqi Chen & Zhi Qiao & Liangwa Cai, 2021. "Does Rural Production–Living–Ecological Spaces Have a Preference for Regional Endowments? A Case of Beijing-Tianjin-Hebei, China," Land, MDPI, vol. 10(11), pages 1-21, November.
    4. Baetens, J.M. & Van der Weeën, P. & De Baets, B., 2012. "Effect of asynchronous updating on the stability of cellular automata," Chaos, Solitons & Fractals, Elsevier, vol. 45(4), pages 383-394.
    5. Zimu Jia & Long Chen & Jingjia Chen & Guowei Lyu & Ding Zhou & Ying Long, 2020. "Urban modeling for streets using vector cellular automata: Framework and its application in Beijing," Environment and Planning B, , vol. 47(8), pages 1418-1439, October.
    6. Ortigoza, Gerardo M., 2015. "Unstructured triangular cellular automata for modeling geographic spread," Applied Mathematics and Computation, Elsevier, vol. 258(C), pages 520-536.
    7. Mauricio Verardo & Pedro P. B. de Oliveira, 2019. "A Fully Operational Framework for Handling Cellular Automata Templates," Complexity, Hindawi, vol. 2019, pages 1-11, April.
    8. Yan Liu & Yongjiu Feng, 2016. "Simulating the Impact of Economic and Environmental Strategies on Future Urban Growth Scenarios in Ningbo, China," Sustainability, MDPI, vol. 8(10), pages 1-16, October.
    9. Tianqi Rong & Pengyan Zhang & Wenlong Jing & Yu Zhang & Yanyan Li & Dan Yang & Jiaxin Yang & Hao Chang & Linna Ge, 2020. "Carbon Dioxide Emissions and Their Driving Forces of Land Use Change Based on Economic Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the Lower Yellow River Region (1995–20," Energies, MDPI, vol. 13(10), pages 1-18, May.
    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. Bithell, M. & Macmillan, W.D., 2007. "Escape from the cell: Spatially explicit modelling with and without grids," Ecological Modelling, Elsevier, vol. 200(1), pages 59-78.
    12. Xia Li & Anthony Gar-On Yeh, 2001. "Calibration of Cellular Automata by Using Neural Networks for the Simulation of Complex Urban Systems," Environment and Planning A, , vol. 33(8), pages 1445-1462, August.
    13. Robert Gilmore Pontius, 2018. "Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance," Land, MDPI, vol. 7(3), pages 1-14, September.
    14. Elenna R Dugundji & László Gulyás, 2008. "Sociodynamic Discrete Choice on Networks in Space: Impacts of Agent Heterogeneity on Emergent Outcomes," Environment and Planning B, , vol. 35(6), pages 1028-1054, December.
    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. Hassan Mahmoudzadeh & Asghar Abedini & Farshid Aram, 2022. "Urban Growth Modeling and Land-Use/Land-Cover Change Analysis in a Metropolitan Area (Case Study: Tabriz)," Land, MDPI, vol. 11(12), pages 1-17, November.
    17. 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.
    18. Li, Zeyang & Luan, Weixin & Zhang, Zhenchao & Su, Min, 2020. "Relationship between urban construction land expansion and population/economic growth in Liaoning Province, China," Land Use Policy, Elsevier, vol. 99(C).
    19. Chenxi Li & Jingyao Wu & Zenglei Xi & Weiqiang Zhang, 2021. "Farmers’ Satisfaction with Land Expropriation System Reform: A Case Study in China," Land, MDPI, vol. 10(12), pages 1-16, December.
    20. Itzhak Benenson & Erez Hatna & Ehud Or, 2009. "From Schelling to Spatially Explicit Modeling of Urban Ethnic and Economic Residential Dynamics," Sociological Methods & Research, , vol. 37(4), pages 463-497, May.

    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:47:y:2020:i:9:p:1605-1621. 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.