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

Although geographically weighted Poisson regression (GWPR) is a popularregression for spatially indexed count data, its development is relativelylimited compared to that found for linear geographically weighted regression(GWR), where many extensions (e.g., multiscale GWR, scalable GWR) have beenproposed. The weak development of GWPR can be attributed to the computationalcost and identification problem in the underpinning Poisson regression model.This study proposes linearized GWPR (L-GWPR) by introducing a log-linearapproximation into the GWPR model to overcome these bottlenecks. Because theL-GWPR model is identical to the Gaussian GWR model, it is free from theidentification problem, easily implemented, computationally efficient, andoffers similar potential for extension. Specifically, L-GWPR does not require adouble-loop algorithm, which makes GWPR slow for large samples. Furthermore, weextended L-GWPR by introducing ridge regularization to enhance its stability(regularized L-GWPR). The results of the Monte Carlo experiments confirmed thatregularized L-GWPR estimates local coefficients accurately and computationallyefficiently. Finally, we compared GWPR and regularized L-GWPR through a crimeanalysis in Tokyo.

A linearization for stable and fast geographically weighted Poisson regression