Understanding ggplot2 and Spatial Objects

In the world of data visualization, understanding how to effectively communicate spatial relationships between objects is crucial. This involves working with spatial objects such as points, polygons, and lines in a way that facilitates intuitive visualizations. One popular library for creating these types of plots is ggplot2, which, although versatile, can be challenging when dealing with spatial data.

In this blog post, we’ll delve into the specifics of using ggplot2 to visualize spatial objects, focusing on how to create gridded SpatialPolygonsDataFrame objects and plot them effectively. We’ll also discuss why converting from traditional sp objects (e.g., SpatialPointsDataFrame) is often recommended when working with ggplot2 for visualization purposes.

Introduction to Spatial Objects

To begin, let’s briefly cover what spatial objects are and how they’re represented in R. Spatial objects represent geographic locations or features using unique identifiers such as points, lines, or polygons. These can be used to create maps that display relevant data or patterns over a specific area of interest.

R provides several packages for working with spatial data, including sp, which is the foundation for many other packages dealing with geospatial data. However, when it comes to visualization using ggplot2, converting to objects defined by the sf (Simple Features) package is often preferred over traditional sp methods.

Converting Spatial Objects

Converting from a traditional sp object to an sf object can be accomplished using functions like st_as_sf() and others provided within packages such as broom. When converting, note that the geometry of your spatial objects changes, which affects how they’re processed during visualization.

library(sp)
library(sf)

# Creating a SpatialPointsDataFrame object for demonstration purposes

wgs.84 <- CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")
points <- data.frame(id = c(1, 2, 3), 
                     x = c(-76.82973, -76.82972, -76.82969),
                     y = c(28.26734, 28.26644, 28.26508))

sp_points_df <- SpatialPointsDataFrame(points, proj4string = wgs.84)

sf_points_df <- st_as_sf(sp_points_df)

Working with SpatialPolygonsDataFrame

SpatialPolygonsDataFrame objects are particularly useful for creating maps where the data consists of multiple polygons defined by boundary coordinates.

# Creating a sample dataset
data_points <- data.frame(
  id = c(1,2,3),
  long = c(-76.82973,-76.82972,-76.82969),
  lat = c(28.26734,28.26644,28.26508)
)

sp_polygons_df <- SpatialPolygonsDataFrame(data_points,
                                          proj4string = wgs.84)

Creating a Grid

To create a grid that represents your map area and has data associated with it, you can use the GridTopology function.

# Defining the cell size for our grid
cell_size_x <- 0.001
cell_size_y <- 0.001

grid_topology <- GridTopology(
  cellcentre.offset = c(-76.82973,-76.82972,-76.82969),
  cellsize = c(cell_size_x, cell_size_y), 
  cells.dim = c(3,7)
)

# Creating the spatial grid
sp_grid <- SpatialGrid(grid = grid_topology, proj4string = wgs.84)

Converting to SpatialPolygons and Adding Data

To convert your spatial grid into a set of polygons where each polygon has an associated ID or value.

# Defining a function that generates the appropriate values for each polygon based on its coordinates.
generate_polygon_values <- function(polygon) {
  # We want to assign an "id" that increments by one for every unique bounding box,
  # and a "values" string that denotes which part of the grid this cell is associated with.

  id_values <- paste("Gridvalue", 1:dim(polygon)[1], sep = ":")
  
  return(data.frame(id = polygon@data$id, values = id_values))
}

# Creating our polygons
sp_polygons <- SpatialPolygons(
  coordinates(sp_grid),
  IDs = generate_polygon_values(sp_grid$grid),
  proj4string = wgs.84
)

# Adding a "values" column to each polygon based on its associated ID.
sp_polygons$values <- paste("Gridvalue", 1:dim(sp_polygons)[1], sep = ":")

Plotting the SpatialPolygonsDataFrame

Once you have your spatial polygons, you can plot them using ggplot2 along with any additional data points.

# Load libraries
library(dplyr)
library(ggplot2)

# Converting our sp object into an sf one for better performance in visualization
sf_sp_polygons <- st_as_sf(sp_polygons, 
                          force = TRUE)

# Keeping only cells (grids) where there are data points.
sp_data_points <- datasetSP %>% filter(id %in% samplePointsInPolygons2$id)

# Creating the plot
ggplot() +
  geom_sf(colour = "red") +
  geom_sf_text(aes(label = values), 
               nudge_y = 0.0003, colour = "grey40") +
  geom_point(data = sp_data_points,
             aes(x = LONGITUDE,
                 y = LATITUDE)) +
  theme_void() + # Remove background to focus on the map

Discussion and Advice

When working with spatial data in R, converting traditional sp objects to sf objects often provides better performance for visualization tasks when using packages like ggplot2. This is largely due to how ggplot2 handles geospatial data; it’s optimized to work with the features provided by the sf package.

For more complex spatial analyses or tasks requiring precise control over your data structures, sticking with traditional sp objects might be a better choice. However, when visualization and performance are high on your priority list, converting to an sf object is usually the preferred approach.

Conclusion

In conclusion, effectively working with spatial data in R requires understanding how different libraries (like ggplot2, sp, and sf) interact with each other. This includes knowing when to use which package for analysis and visualization tasks, as well as having a solid grasp of the underlying geospatial concepts.

By choosing the right tools and understanding their capabilities, you can unlock more efficient data processing, better performance, and more effective visualizations that accurately communicate your findings.

Last modified on 2024-05-15