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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
cache = TRUE
)
```
# Geographically Weighted Poisson Regression
<!-- badges: start -->
<!-- badges: end -->
## Installation
You can install the package from [GitHub](https://github.com/hoxo-m/gwpr).
```{r eval=FALSE}
install.packages("remotes") # if you have not installed "remotes" package
remotes::install_github("hoxo-m/gwpr")
```
## Example
```{r}
set.seed(123)
df <- lctools::random.test.data(10, 10, 2, "poisson")
head(df)
```
```{r, results='hide'}
library(gwpr)
df_coord <- df[c("X", "Y")]
params <- gwpr(df, dep ~ vars, df_coord,
kernel = "gaussian", bandwidth = 1)
```
```{r out.width=400}
library(ggplot2)
df_gg <- cbind(params, df_coord)
ggplot(df_gg, aes(X, Y)) + geom_raster(aes(fill = vars)) +
scale_fill_gradient2()
```
### Change bandwidth
```{r, results='hide'}
params_bw2 <- gwpr(df, dep ~ vars, df_coord,
kernel = "gaussian", bandwidth = 2)
params_bw3 <- gwpr(df, dep ~ vars, df_coord,
kernel = "gaussian", bandwidth = 3)
```
```{r fig.height=5, out.width=800}
df_gg_bw2 <- cbind(params_bw2, df_coord)
df_gg_bw3 <- cbind(params_bw3, df_coord)
p1 <- ggplot(df_gg, aes(X, Y)) + geom_raster(aes(fill = vars)) +
scale_fill_gradient2() + ggtitle("bandwidth = 1")
p2 <- ggplot(df_gg_bw2, aes(X, Y)) + geom_raster(aes(fill = vars)) +
scale_fill_gradient2() + ggtitle("bandwidth = 2")
p3 <- ggplot(df_gg_bw3, aes(X, Y)) + geom_raster(aes(fill = vars)) +
scale_fill_gradient2() + ggtitle("bandwidth = 3")
cowplot::plot_grid(p1, p2, p3)
```
## References
- Nakaya, Tomoki & Fotheringham, Alexander & Brunsdon, Chris & Charlton, Martin. (2005). Geographically Weighted Poisson Regression for Disease Association Mapping. Statistics in medicine. 24. 2695-717. 10.1002/sim.2129.
## Related Work
- [spgwr: Geographically Weighted Regression](https://cran.r-project.org/package=spgwr)
- [](https://cran.r-project.org/package=spgwr)
[](https://cran.r-project.org/package=spgwr)
- `ggwr()`: Generalised geographically weighted regression
- Fotheringham, A.S., Brunsdon, C., and Charlton, M.E., 2002, Geographically Weighted Regression, Chichester: Wiley; http://gwr.nuim.ie/
- [GWmodel: Geographically-Weighted Models](https://cran.r-project.org/package=GWmodel)
- [](https://cran.r-project.org/package=GWmodel)
[](https://cran.r-project.org/package=GWmodel)
- `ggwr.basic()`: Generalised GWR models with Poisson and Binomial options
- Nakaya, T., A. S. Fotheringham, C. Brunsdon & M. Charlton (2005) Geographically weighted Poisson regression for disease association mapping. Statistics in Medicine, 24, 2695-2717.
- Nakaya, T., M. Charlton, S. Fotheringham & C. Brunsdon. 2009. How to use SGWRWIN (GWR4.0). Maynooth, Ireland: National Centre for Geocomputation.
- Fotheringham S, Brunsdon, C, and Charlton, M (2002), Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, Chichester: Wiley.
- [lctools: Local Correlation, Spatial Inequalities, Geographically Weighted Regression and Other Tools](https://cran.r-project.org/package=lctools)
- [](https://cran.r-project.org/package=lctools)
[](https://cran.r-project.org/package=lctools)
- `gw.glm()`: Generalised Geographically Weighted Regression (GGWR)
- Kalogirou, S. (2003) The Statistical Analysis and Modelling of Internal Migration Flows within England and Wales, PhD Thesis, School of Geography, Politics and Sociology, University of Newcastle upon Tyne, UK. http://gisc.gr/?mdocs-file=1245&mdocs-url=false
- Kalogirou, S. (2016) Destination Choice of Athenians: an application of geographically weighted versions of standard and zero inflated Poisson spatial interaction models, Geographical Analysis, 48(2),pp. 191-230. DOI: 10.1111/gean.12092 http://onlinelibrary.wiley.com/doi/10.1111/gean.12092/abstract