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Updated: Jul 1, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Mastering geographically weighted regression: key considerations for building a robust model
Behzad Kiani1, Benn Sartorius2, Colleen L Lau3
1UQ Centre for Clinical Research, The University of Queensland, Brisbane. B.kiani@uq.edu.au.
Abstract:
Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].
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