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The Algorithmic Complexity of Landscapes

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  • Fivos Papadimitriou

Abstract

A method to evaluate the algorithmic complexity of landscapes is developed here, based on the notion of Kolmogorov complexity (or K-complexity). The K-complexity of a landscape is calculated from a string x of symbols representing the landscape's features (e.g. land use), whereby each symbol belongs to an alphabet L, and can be defined as the size of the shortest string y that fully describes x. K-complexity presents several useful aspects as a measure of landscape complexity: a) it is a direct measure of complexity and not a surrogate measure, well supported by the literature of Informatics; b) it is easy to apply to landscapes of 'small' size' c) it can be used to compare the complexity of two or more landscapes; d) it allows calculations of a landscape's changes in complexity with time; e) it can be a descriptor not only of the landscape's structural complexity, but also of its functional complexity; and f) it makes possible to distinguish two landscapes with the same diversity but with different complexity.

Suggested Citation

  • Fivos Papadimitriou, 2012. "The Algorithmic Complexity of Landscapes," Landscape Research, Taylor & Francis Journals, vol. 37(5), pages 591-611, October.
    • Handle: RePEc:taf:clarxx:v:37:y:2012:i:5:p:591-611
    DOI: 10.1080/01426397.2011.650628
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    Cited by:

    1. Kai Lu & Tao Tang & Chunhai Gao, 2020. "The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network," Sustainability, MDPI, vol. 12(13), pages 1-16, July.
    2. Yoonshin Kwak & Brian Deal & Grant Mosey, 2021. "Landscape Design toward Urban Resilience: Bridging Science and Physical Design Coupling Sociohydrological Modeling and Design Process," Sustainability, MDPI, vol. 13(9), pages 1-17, April.
    3. Yu-Hui Chen & Chun-Lin Lee & Guan-Rui Chen & Chiung-Hsin Wang & Ya-Hui Chen, 2018. "Factors Causing Farmland Price-Value Distortion and Their Implications for Peri-Urban Growth Management," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
    4. Ali Noudoostbeni & Kiran Kaur & Hashem Salarzadeh Jenatabadi, 2018. "A Comparison of Structural Equation Modeling Approaches with DeLone & McLean’s Model: A Case Study of Radio-Frequency Identification User Satisfaction in Malaysian University Libraries," Sustainability, MDPI, vol. 10(7), pages 1-16, July.

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