ggspatreg

ggspatreg extends ggplot2, allowing for simple visualization of spatial regression models. Included are functions geom_krige(), geom_krige_contour(), and geom_krige_contour_lines() for plotting interpolation of spatial data.

Installation

ggspatreg is being actively developed and is likely to undergo large changes in the future. It is not available on CRAN, but you can install the development version from GitHub:

if (!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("jamesotto852/ggspatreg")

Simple Examples

First, we simulate some spatially correlated data with geoR::grf():

library("tidyverse"); theme_set(theme_bw())
library("ggspatreg")

set.seed(1)

grid <- expand_grid(x = 0:100, y = 0:100) 

df <- slice_sample(grid, n = 600) 
df$z <- geoR::grf(grid = df, cov.model = "exp", cov.pars = c(5, 30), nugget = 1)$data

ggplot(df, aes(x, y, fill = z)) +
  geom_point(shape = 21, size = 3) +
  scale_fill_viridis_c() +
  coord_fixed()

Now, we can use geom_krige() to plot the resulting interpolated kriging surface. By default, an exponential covariance model is used. This can be changed via the model argument.

ggplot(df, aes(x, y)) +
  geom_krige(aes(z = z)) +
  geom_point(aes(fill = z), shape = 21, size = 3) +
  scale_fill_viridis_c() +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE)

Also included are geom_krige_contour() and geom_krige_contour_lines():

ggplot(df, aes(x, y, z = z)) +
  geom_krige_contour(bins = 6, show.legend = FALSE) +
  scale_fill_viridis_d() +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE)

ggplot(df, aes(x, y, z = z)) +
  geom_krige_contour_lines(bins = 6, show.legend = FALSE) +
  scale_color_viridis_d() +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE)

---

In ggspatreg, it is also possible to model spatially correlated data with trends. Let’s add a simple linear trend to the previously simulated data:

df <- mutate(df, z = z + 3/20 * x - 1/20 * y)
  
ggplot(df, aes(x, y, fill = z)) +
  geom_point(shape = 21, size = 3) +
  scale_fill_viridis_c() +
  coord_fixed()

When formula is provided, geom_krige() models both the spatial dependence and the specified linear relationship.

ggplot(df, aes(x, y)) +
  geom_krige(aes(z = z), formula = z ~ x + y) +
  geom_point(aes(fill = z), shape = 21, size = 3) +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE) +
  scale_fill_viridis_c()

Again, we can do the same thing with contours:

ggplot(df, aes(x, y, z = z)) +
  geom_krige_contour(bins = 8, formula = z ~ x + y, show.legend = FALSE) +
  scale_fill_viridis_d() +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE)

ggplot(df, aes(x, y, z = z)) +
  geom_krige_contour_lines(bins = 8, formula = z ~ x + y, show.legend = FALSE) +
  scale_color_viridis_d() +
  coord_fixed(xlim = c(0, 100), ylim = c(0, 100), expand = FALSE)

Latitude/Longitude Data

ggspatreg works well with latitude longitude data. Below, we simulate spatial data for coordinates in Waco, TX.

set.seed(1)

df <- tibble(
  lon = runif(250, -97.3, -97),
  lat = runif(250, 31.4, 31.7),
  z = geoR::grf(grid = cbind(lon, lat), cov.model = "exp", cov.pars = c(3, .1), nugget = 1)$data
) 

Below, we set up ggmap and visualize our sample points:

library("ggmap")

theme_update(
  axis.title = element_blank(),
  axis.text = element_blank(),
  axis.ticks = element_blank() 
)

waco_map <- get_map(location = c(-97.3, 31.4, -97, 31.7), color = "bw")

ggmap(waco_map) +
  geom_point(data = df, aes(lon, lat, fill = z), shape = 21, size = 3) +
  guides(color = guide_legend(), size = guide_legend()) +
  scale_fill_viridis_c()

It is simple to include kriging layers on top of ggmap raster images, we need only adjust alpha:

ggmap(waco_map) +
  geom_krige(data = df, aes(lon, lat, z = z), alpha = .4) +
  geom_point(data = df, aes(lon, lat, fill = z), shape = 21, size = 3) +
  scale_fill_viridis_c()

ggmap(waco_map) +
  geom_krige_contour(data = df, aes(lon, lat, z = z), alpha = .4, bins = 7) +
  scale_fill_viridis_d()

ggmap(waco_map) +
  geom_krige_contour_lines(data = df, aes(lon, lat, z = z), bins = 7) +
  scale_fill_viridis_d()