English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The aim of this paper is to explore and develop advanced spatial Bayesianassessment methods and techniques for land use modeling. The paper provides acomprehensive guide for assessing additional informational entropy value ofmodel predictions at the spatially explicit domain of knowledge, and proposes afew alternative metrics and indicators for extracting higher-order informationdynamics from simulation tournaments. A seven-county study area inSouth-Eastern Wisconsin (SEWI) has been used to simulate and assess theaccuracy of historical land use changes (1963-1990) using artificial neuralnetwork simulations of the Land Transformation Model (LTM). The use of theanalysis and the performance of the metrics helps: (a) understand and learn howwell the model runs fits to different combinations of presence and absence oftransitions in a landscape, not simply how well the model fits our given data;(b) derive (estimate) a theoretical accuracy that we would expect a model toassess under the presence of incomplete information and measurement; (c)understand the spatially explicit role and patterns of uncertainty insimulations and model estimations, by comparing results across simulation runs;(d) compare the significance or estimation contribution of transitionalpresence and absence (change versus no change) to model performance, and thecontribution of the spatial drivers and variables to the explanatory value ofour model; and (e) compare measurements of informational uncertainty atdifferent scales of spatial resolution.

Developing Bayesian Information Entropy-based Techniques for Spatially Explicit Model Assessment