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

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 . 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 OPEN ACCESSEntropy 2013, 15 2481 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.

Analysis of Spatial–Temporal Characteristics of Industrial Land Supply Scale in Relation to Industrial Structure in China

“…The information entropy plays an important role as a descriptive statistic in various disciplines linked to the spatial domain, e.g., landscape changes [23,24], land use, and land cover change [25]. For example, Kostas introduced the Bayesian information entropy statistical spatial metrics for simulating the historical land-use transformations in urban/suburban areas [26]. Although information entropy has unique advantages in reflecting the diversity of industrial structure, it fails to take all the characteristics of the spatial or the spatial-temporal dimension into account [27].…”