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

. Local spatiotemporal nonstationarity occurs in various naturaland socioeconomic processes. Many studies have attempted to introduce timeas a new dimension into a geographically weighted regression (GWR) model,but the actual results are sometimes not satisfying or even worse than theoriginal GWR model. The core issue here is a mechanism for weighting the effectsof both temporal variation and spatial variation. In many geographical andtemporal weighted regression (GTWR) models, the concept of time distance hasbeen inappropriately treated as a time interval. Consequently, the combinedeffect of temporal and spatial variation is often inaccurate in theresulting spatiotemporal kernel function. This limitation restricts theconfiguration and performance of spatiotemporal weights in many existingGTWR models. To address this issue, we propose a new spatiotemporal weightedregression (STWR) model and the calibration method for it. A highlight ofSTWR is a new temporal kernel function, wherein the method for temporalweighting is based on the degree of impact from each observed point to aregression point. The degree of impact, in turn, is based on the rate ofvalue variation of the nearby observed point during the time interval. Theupdated spatiotemporal kernel function is based on a weighted combination ofthe temporal kernel with a commonly used spatial kernel (Gaussian orbi-square) by specifying a linear function of spatial bandwidth versus time.Three simulated datasets of spatiotemporal processes were used to test theperformance of GWR, GTWR, and STWR. Results show that STWR significantlyimproves the quality of fit and accuracy. Similar results were obtained byusing real-world data for precipitation hydrogen isotopes (δ2H) in the northeastern United States. The leave-one-out cross-validation(LOOCV) test demonstrates that, compared with GWR, the total predictionerror of STWR is reduced by using recent observed points. Predictionsurfaces of models in this case study show that STWR is more localized thanGWR. Our research validates the ability of STWR to take full advantage ofall the value variation of past observed points. We hope STWR can bringfresh ideas and new capabilities for analyzing and interpreting localspatiotemporal nonstationarity in many disciplines.

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time