Open AccessThis article is
- freely available
Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models
Institute for Geocomputational Analysis and Statistics (GeoCAS), University of the Virgin Islands, 2 John Brewer's Bay, St. Thomas, VI 00802, USA
Department of Computer and Computational Sciences, College of Science and Mathematics, University of the Virgin Islands, 2 John Brewer's Bay, St. Thomas, VI 00802, USA
Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47906, USA
* Author to whom correspondence should be addressed.
Received: 2 May 2013; in revised form: 9 June 2013 / Accepted: 13 June 2013 / Published: 25 June 2013
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
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
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.
Alexandridis K, Pijanowski BC. Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models. Entropy. 2013; 15(7):2480-2509.
Alexandridis, Kostas; Pijanowski, Bryan C. 2013. "Spatially-Explicit Bayesian Information Entropy Metrics for Calibrating Landscape Transformation Models." Entropy 15, no. 7: 2480-2509.