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Evaluating Spatial Design Techniques for Solving Land-use Allocation Problems

Author

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  • Jeroen Aerts
  • Marjan Van Herwijnen
  • Ron Janssen
  • Theodor Stewart

Abstract

This study examines the use of spatial optimization techniques for multi-site land-use allocation problems (MLUA). 'Multi-site' refers to the problem of allocating more than one land-use type in an area, which are difficult problems as they involve multiple stakeholders with conflicting goals and objectives. Spatial optimization methods consist of (1) an optimization model and (2) an algorithm to solve the model. This study demonstrates a goal-programming model to solve the MLUA problem. The model is solved using both simulated annealing and genetic algorithms. Special attention has been given to introduce a spatial compactness objective in the model. It is shown that the compactness objectives in the optimization model generate compact patches of the same land use for using both the simulated annealing procedure and the genetic algorithm. In addition, it appears that using the proper settings of the compactness objectives, connectivity between patches of land use is promoted. The method is tested for a fictive study and then demonstrated for a real case study, both measuring 20 × 20 cells. The genetic algorithm generally performs better than simulated annealing in terms of solution time and achieving compactness.

Suggested Citation

  • Jeroen Aerts & Marjan Van Herwijnen & Ron Janssen & Theodor Stewart, 2005. "Evaluating Spatial Design Techniques for Solving Land-use Allocation Problems," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 48(1), pages 121-142.
    • Handle: RePEc:taf:jenpmg:v:48:y:2005:i:1:p:121-142
    DOI: 10.1080/0964056042000308184
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    Citations

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    Cited by:

    1. Dimitris Fotakis & Epameinondas Sidiropoulos, 2014. "Combined land-use and water allocation planning," Annals of Operations Research, Springer, vol. 219(1), pages 169-185, August.
    2. Chenhao Zhu & Jonah Susskind & Mario Giampieri & Hazel Backus O’Neil & Alan M. Berger, 2023. "Optimizing Sustainable Suburban Expansion with Autonomous Mobility through a Parametric Design Framework," Land, MDPI, vol. 12(9), pages 1-31, September.
    3. Xia Xu & Mengxi Guan & Honglei Jiang & Lingfei Wang, 2019. "Dynamic Simulation of Land Use Change of the Upper and Middle Streams of the Luan River, Northern China," Sustainability, MDPI, vol. 11(18), pages 1-15, September.
    4. Yao Lu & Min Zhou & Guoliang Ou & Zuo Zhang & Li He & Yuxiang Ma & Chaonan Ma & Jiating Tu & Siqi Li, 2021. "Sustainable Land-Use Allocation Model at a Watershed Level under Uncertainty," IJERPH, MDPI, vol. 18(24), pages 1-19, December.
    5. Daniel Caparros‐Midwood & Stuart Barr & Richard Dawson, 2017. "Spatial Optimization of Future Urban Development with Regards to Climate Risk and Sustainability Objectives," Risk Analysis, John Wiley & Sons, vol. 37(11), pages 2164-2181, November.
    6. Zhang, Wei & van der Werf, Wopke & Swinton, Scott M., 2010. "Spatially optimal habitat management for enhancing natural control of an invasive agricultural pest: Soybean aphid," Resource and Energy Economics, Elsevier, vol. 32(4), pages 551-565, November.
    7. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Khalifah, Zainab & Zakuan, Norhayati & Jusoh, Ahmad & Nor, Khalil Md & Khoshnoudi, Masoumeh, 2017. "A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 216-256.
    8. Kai Cao & Wenwen Li & Richard Church, 2020. "Big data, spatial optimization, and planning," Environment and Planning B, , vol. 47(6), pages 941-947, July.

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