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Entropy 2013, 15(7), 2480-2509; doi:10.3390/e15072480
Article

Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models

1,2,*  and 3
Received: 2 May 2013; in revised form: 9 June 2013 / Accepted: 13 June 2013 / Published: 25 June 2013
(This article belongs to the Special Issue Entropy and Urban Sprawl)
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Abstract: Assessing spatial model performance often presents challenges related to the choice and suitability of traditional statistical methods in capturing the true validity and dynamics of the predicted outcomes. The stochastic nature of many of our contemporary spatial models of land use change necessitate the testing and development of new and innovative methodologies in statistical spatial assessment. In many cases, spatial model performance depends critically on the spatially-explicit prior distributions, characteristics, availability and prevalence of the variables and factors under study. This study explores the statistical spatial characteristics of statistical model assessment of modeling land use change dynamics in a seven-county study area in South-Eastern Wisconsin during the historical period of 1963–1990. The artificial neural network-based Land Transformation Model (LTM) predictions are used to compare simulated with historical land use transformations in urban/suburban landscapes. We introduce a range of Bayesian information entropy statistical spatial metrics for assessing the model performance across multiple simulation testing runs. Bayesian entropic estimates of model performance are compared against information-theoretic stochastic entropy estimates and theoretically-derived accuracy assessments. We argue for the critical role of informational uncertainty across different scales of spatial resolution in informing spatial landscape model assessment. Our analysis reveals how incorporation of spatial and landscape information asymmetry estimates can improve our stochastic assessments of spatial model predictions. Finally our study shows how spatially-explicit entropic classification accuracy estimates can work closely with dynamic modeling methodologies in improving our scientific understanding of landscape change as a complex adaptive system and process.
Keywords: artificial neural networks; land use change; Bayesian information; Bayesian entropy; maximum entropy artificial neural networks; land use change; Bayesian information; Bayesian entropy; maximum entropy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Alexandridis, K.; Pijanowski, B.C. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models. Entropy 2013, 15, 2480-2509.

AMA Style

Alexandridis K, Pijanowski BC. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models. Entropy. 2013; 15(7):2480-2509.

Chicago/Turabian Style

Alexandridis, Kostas; Pijanowski, Bryan C. 2013. "Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models." Entropy 15, no. 7: 2480-2509.


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