README.Rmd

---
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%"
)
```


# spInterp: Spatial Interpolation

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## Installation

You can install the development version of spInterp from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("rpkgs/spInterp")
```

## On top of 

1. <https://github.com/PanfengZhang/adw>
2. <https://github.com/rrodrigojrr/ADW>

## TODO

- [ ] Thin Plate Spline
- [ ] P-Spline
- [ ] Krige

## Performance

```{r}
library(spInterp)
library(adw)

data(TempBrazil) # Temperature for some poins of Brazil

X <- TempBrazil[, 1:2] %>% set_names(c("lon", "lat"))
Y <- TempBrazil[, 3] %>% as.matrix()  # Vector with observations in points

range <- c(-78, -34, -36, 5)
res = 1
# Compare with the R package adw
dd = cbind(X, value = Y[,1])
```

```{r}
system.time({
  r_adw <- adw::adw(dd, range, cdd = 450 * 1e3, gridsize = res)
})

# profvis::profvis({
system.time({
  r <- spInterp_adw(X, Y, range, res = res, cdd = 450)
})
```

```{r, message=FALSE}
df = rbind(
  r %$% cbind(coord, value = predicted[, 1], method = "spInterp"), 
  cbind(r_adw, method = "adw"))

library(ggplot2)
ggplot(df, aes(lon, lat)) + 
  geom_raster(aes(fill = value)) +
  geom_point(data = X, size = 1, shape = 3, color = "red") +
  facet_wrap(~method) + 
  lims(x = range[1:2], y = range[3:4]) -> p
p
# Ipaper::write_fig(p, "figure1.pdf", 10, 6)
```


`spInterp` used the exactly same algrithm as that of `adw` package. 
But unlike `adw`, `spInterp` avoids using `sf` for spatial data processing such as 
buffer, intersect. Surprisingly, `spInterp` is dozens of times faster. 

There is a slight difference from `adw` in the calculation of the distance between 
two points on the sphere, because `spInterp` uses self defined function, while 
`adw` uses `sf::st_distance`. But the difference is tiny (might about 1%), can be
ignored at most situation.

## References

1. Xavier, A. C., King, C. W., & Scanlon, B. R. (2016). Daily gridded 
   meteorological variables in Brazil (1980–2013). International Journal of
   Climatology, 36(6), 2644–2659. <doi:10.1002/joc.4518>