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Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management

Author

Listed:
  • Magdalena Wagner

    (TUM Department of Civil, Geo and Environmental Engineering, Technical University of Munich, 80333 Munich, Germany)

  • Walter Timo de Vries

    (TUM Department of Civil, Geo and Environmental Engineering, Technical University of Munich, 80333 Munich, Germany)

Abstract

This paper discusses how and where technologies supporting decision-making can play, or are already playing, a role in both urban development and land management. The review analyzes and compares three types of technologies: cellular automata (CA), artificial intelligence (AI), and operational research (OR), and evaluates to which extent they can be useful when dealing with various land planning objectives and phases. CA is one of the most useful models for simulating urban growth, AI displays great potential as a solution to capture the dynamics of land change, and OR is useful in decision-making, for example to conduct locational analyses. The evaluation relies on a collection of relevant articles, selected on the basis of both content and actuality. The paper offers new perspectives towards innovative methods in urban planning and land management and highlights where and when which type of tool can be considered useful and valid. The existing gaps, i.e., phases or areas in spatial planning or land management where the methods have not been applied, are also discussed.

Suggested Citation

  • Magdalena Wagner & Walter Timo de Vries, 2019. "Comparative Review of Methods Supporting Decision-Making in Urban Development and Land Management," Land, MDPI, vol. 8(8), pages 1-13, August.
    • Handle: RePEc:gam:jlands:v:8:y:2019:i:8:p:123-:d:255538
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    References listed on IDEAS

    as
    1. Campos, Pedro Bueno Rocha & Almeida, Cláudia Maria de & Queiroz, Alfredo Pereira de, 2018. "Educational infrastructure and its impact on urban land use change in a peri-urban area: a cellular-automata based approach," Land Use Policy, Elsevier, vol. 79(C), pages 774-788.
    2. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, June.
    3. Ferretti, Valentina & Montibeller, Gilberto, 2016. "Key challenges and meta-choices in designing and applying multi-criteria spatial decision support systems," LSE Research Online Documents on Economics 65368, London School of Economics and Political Science, LSE Library.
    4. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    5. Silvia Angilella & Marta Bottero & Salvatore Corrente & Valentina Ferretti & Salvatore Greco & Isabella M. Lami, 2016. "Non Additive Robust Ordinal Regression for urban and territorial planning: an application for siting an urban waste landfill," Annals of Operations Research, Springer, vol. 245(1), pages 427-456, October.
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    Cited by:

    1. Ernest Uwayezu & Walter T. de Vries, 2020. "Can In-Kind Compensation for Expropriated Real Property Promote Spatial Justice? A Case Study Analysis of Resettlement in Kigali City, Rwanda," Sustainability, MDPI, vol. 12(9), pages 1-34, May.
    2. Guler, Dogus & Yomralioglu, Tahsin, 2021. "A reformative framework for processes from building permit issuing to property ownership in Turkey," Land Use Policy, Elsevier, vol. 101(C).

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