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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Geographically weighted models are widely used for analyzing the spatial data. There is a problem with spatial data processing of extracting a potentially lower number of unobserved variables while a set of correlated variables is observed. The factor analysis is commonly used to overcome this problem. A bandwidth selection is a main difficulty during the identification a spatial heterogeneity of factor loadings. In the paper an original bandwidth selection criterion is proposed. It is based on the testing the difference between factor loadings of global and geographically weighted model. Using the simulated data it is shown that the criterion proposed allows to define accurately the appropriate number of nearest neighbors. The proposed approach is used to analyze real data on performance metrics of Russian universities.

Optimal bandwidth selection in geographically weighted factor analysis for education monitoring problems