Mastering geographically weighted regression: key considerations for building a robust model
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, eith...
Main Authors: | Behzad Kiani, Benn Sartorius, Colleen L. Lau, Robert Bergquist |
---|---|
Format: | Article |
Language: | English |
Published: |
PAGEPress Publications
2024-02-01
|
Series: | Geospatial Health |
Subjects: | |
Online Access: | https://www.geospatialhealth.net/gh/article/view/1271 |
Similar Items
-
Spatial Modeling for Poverty: The Comparison of Spatial Error Model and Geographic Weighted Regression
by: Achi Rinaldi, et al.
Published: (2021-06-01) -
Non-Iterative Multiscale Estimation for Spatial Autoregressive Geographically Weighted Regression Models
by: Shi-Jie Gao, et al.
Published: (2023-02-01) -
Implementation of Gamma Regression and Gamma Geographically Weighted Regression on Case Poverty in Bengkulu Province
by: Ilham Alifa Azagi, et al.
Published: (2024-07-01) -
Simulating the Spatial Heterogeneity of Housing Prices in Wuhan, China, by Regionally Geographically Weighted Regression
by: Zengzheng Wang, et al.
Published: (2022-02-01) -
Identifying the spatial heterogeneity of housing financialization in China: Insights from a multiscale geographically weighted regression
by: Yang Wang, et al.
Published: (2024-03-01)