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A Bayesian Solution to the Modifiable Areal Unit Problem

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Book cover Foundations of Computational Intelligence Volume 2

Part of the book series: Studies in Computational Intelligence ((SCI,volume 202))

Summary

The Modifiable Areal Unit Problem (MAUP) prevails in the analysis of spatially aggregated data and influences pattern recognition. It describes the sensitivity of the measurement of spatial phenomena to the size (the scale problem) and the shape (the aggregation problem) of the mapping unit. Much attention has been recieved from fields as diverse as statistical physics, image processing, human geography, landscape ecology, and biodiversity conservation. Recently, in the field of spatial ecology, a Bayesian estimation was proposed to grasp how our description of species distribution (described by range size and spatial autocorrelation) changes with the size and the shape of grain. This Bayesian estimation (BYE), called the scaling pattern of occupancy, is derived from the comparison of pair approximation (in the spatial analysis of cellular automata) and join-count statistics (in the spatial autocorrelation analysis) and has been tested using various sources of data. This chapter explores how the MAUP can be described and potentially solved by the BYE. Specifically, the scale and the aggregation problems are analyzed using simulated data from an individual-based model. The BYE will thus help to finalize a comprehensive solution to the MAUP.

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Authors and Affiliations

  1. Centre of Excellence for Invasion Biology, Department of Botany and Zoology, University of Stellenbosch, Private Bag X1, Matieland, 7602, South Africa

    C. Hui

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  1. C. Hui
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Editors and Affiliations

  1. Faculty of Computers and Information Information Technology Department, Cairo University, 5 Ahmed Zewal St., Orman, Giza

    Aboul-Ella Hassanien

  2. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn,Washington, USA

    Ajith Abraham

  3. Soft Computing and Intelligent Information Systems Department of Computer Science and Artificial Intelligence ETS de Ingenierias Informática y de Telecomunicación, University of Granada, E-18071, Granada, Spain

    Francisco Herrera

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Hui, C. (2009). A Bayesian Solution to the Modifiable Areal Unit Problem. In: Hassanien, AE., Abraham, A., Herrera, F. (eds) Foundations of Computational Intelligence Volume 2. Studies in Computational Intelligence, vol 202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01533-5_7

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  • DOI: https://doi.org/10.1007/978-3-642-01533-5_7

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