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

Entropy is a measure of heterogeneity widely used in applied sciences, oftenwhen data are collected over space. Recently, a number of approaches has beenproposed to include spatial information in entropy. The aim of entropy is tosynthesize the observed data in a single, interpretable number. In otherstudies the objective is, instead, to use data for entropy estimation; severalproposals can be found in the literature, which basically are corrections ofthe estimator based on substituting the involved probabilities withproportions. In this case, independence is assumed and spatial correlation isnot considered. We propose a path for spatial entropy estimation: instead ofcorrecting the global entropy estimator, we focus on improving the estimationof its components, i.e. the probabilities, in order to account for spatialeffects. Once probabilities are suitably evaluated, estimating entropy isstraightforward since it is a deterministic function of the distribution.Following a Bayesian approach, we derive the posterior probabilities of amultinomial distribution for categorical variables, accounting for spatialcorrelation. A posterior distribution for entropy can be obtained, which may besynthesized as wished and displayed as an entropy surface for the area understudy.

Estimation of entropy measures for categorical variables with spatial correlation