A Bayesian Implementation of the Multiscale Geographically Weighted Regression Model with Inla
Bibliographic Data
| ID | 3775528 |
|---|---|
| Authors | Zhihua Ma (0000-0002-0214-5110, Shenzhen University), Zhelin Huang (0000-0002-2544-2636, Shenzhen University) |
| Year | 2023 |
| Volume | 113 |
| Issue | 6 |
| Pages | 1501-1515 |
| Publication date | 2023-07-03 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2023.2187756 |
| OpenAlex | W4372258149 |
| Language | EN |
| Citations received | 3 |
| References cited | 18 |
The multiscale geographically weighted regression (MGWR) model is an important extension of the classical geographically weighted regression (GWR) model that can be used to explore the spatial nonstationarity of the regression relationship in spatial analysis, but also allows for different scales on conditional relationships between response and different predictors. A Bayesian version of the MGWR model is proposed to obtain estimates of the spatially varying coefficients and the bandwidths simultaneously. The hierarchical form of the Bayesian MGWR model has attractive features, including obtaining posterior estimates of the bandwidths and local parameters simultaneously, and their uncertainty can be easily measured. For Bayesian posterior inference, an efficient algorithm based on integrated nested Laplace approximation is introduced to provide a great alternative of the classical Markov chain Monte Carlo algorithm under the Bayesian framework. The performance of the proposed method is evaluated through simulation study, and it is shown that the proposed approach can correctly identify the differences between scales of parameter surfaces and also obtain precise posterior estimates. Finally, for illustration, this approach is used to analyze monthly housing cost data in the state of Georgia
Algorithm · Bayesian average · Bayesian hierarchical modeling · Bayesian inference · Bayesian linear regression · Bayesian probability · Data mining · Inference · Laplace's method · Markov chain Monte Carlo · Posterior probability · Statistics · Computer Science · Economic and Environmental Valuation · Land Use and Ecosystem Services · Mathematics · Spatial and Panel Data Analysis · Artificial Intelligence
Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
MGWR
Geographically Weighted Regression
Inference in Multiscale Geographically Weighted Regression
Geographically Weighted Regression
Distance metric choice can both reduce and induce collinearity in geographically weighted regression
A Computer Movie Simulating Urban Growth in the Detroit Region
Multiscale Geographically Weighted Regression (MGWR)
Estimation and Inference of Special Types of the Coefficients in Geographically and Temporally Weighted Regression Models
Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights
Scalable GWR
Geographically Weighted Regression Modeling for Multiple Outcomes
| Unique citing works | 3 |
|---|---|
| Citations per year | 1,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |