Random parameters and spatial heterogeneity using Rchoice in R
M Sarrias - REGION, 2020 - region.wu.ac.at
This study focus on models with spatially varying coefficients using simulations. As shown by
Sarrias (2019), this modeling strategy is intended to complement the existing approaches by …
Sarrias (2019), this modeling strategy is intended to complement the existing approaches by …
Heterogeneous spatial models in R: spatial regimes models
This paper presents the progress made so far in the development of the R package hspm.
The package hspm aims at implementing a variety of models and methods to control for …
The package hspm aims at implementing a variety of models and methods to control for …
[PDF][PDF] GWmodel: an R package for exploring spatial heterogeneity
In the very early developments of quantitative geography, statistical techniques were
invariably applied at a 'global'level, where moments or relationships were assumed constant …
invariably applied at a 'global'level, where moments or relationships were assumed constant …
An investigation of the impact of various geographical scales for the specification of spatial dependence
Ecological studies are based on characteristics of groups of individuals, which are common
in various disciplines including epidemiology. It is of great interest for epidemiologists to …
in various disciplines including epidemiology. It is of great interest for epidemiologists to …
Introducing the GWmodel R and python packages for modelling spatial heterogeneity
In the very early developments of quantitative geography, statistical techniques were
invariably applied at a 'global'level, where moments or relationships were assumed constant …
invariably applied at a 'global'level, where moments or relationships were assumed constant …
Benchmarking Regression Models Under Spatial Heterogeneity
N Wiedemann, H Martin… - … on Geographic Information …, 2023 - drops.dagstuhl.de
Abstract Machine learning methods have recently found much application on spatial data,
for example in weather forecasting, traffic prediction, and soil analysis. At the same time …
for example in weather forecasting, traffic prediction, and soil analysis. At the same time …
spsur: an R package for dealing with spatial seemingly unrelated regression models
Spatial seemingly unrelated regression (spatial SUR) models are a useful multiequational
econometric specification to simultaneously incorporate spatial effects and correlated error …
econometric specification to simultaneously incorporate spatial effects and correlated error …
GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models
Spatial statistics is a growing discipline providing important analytical techniques in a wide
range of disciplines in the natural and social sciences. In the R package GWmodel, we …
range of disciplines in the natural and social sciences. In the R package GWmodel, we …
Joint variable selection of both fixed and random effects for Gaussian process-based spatially varying coefficient models
Spatially varying coefficient (SVC) models are a type of regression models for spatial data
where covariate effects vary over space. If there are several covariates, a natural question is …
where covariate effects vary over space. If there are several covariates, a natural question is …
Endogenous spatial regimes
The pioneering work of Getis and Ord on local spatial statistics has a counterpart in spatial
econometrics in treating spatial heterogeneity. This can be approached from a continuous or …
econometrics in treating spatial heterogeneity. This can be approached from a continuous or …