Geospatial analysis in R enables the exploration and visualization of spatial data, offering insights into geographical patterns and relationships.

With R’s powerful packages like sf and ggplot2, analyzing spatial data becomes efficient and insightful.

Introduction to Geospatial Analysis

First, I’ll give a brief overview about geospatial analysis.

• R provides tools to handle various types of spatial data formats, including shapefiles, GeoJSON, and raster data.
• Packages like sf offer simple features for manipulating and analyzing spatial data efficiently.
• Using packages like ggplot2, R can create visually appealing maps and plots to represent spatial data.
• These visualizations can showcase geographical distributions, spatial relationships, and patterns, aiding in better understanding and communication of insights.

Example Code

Now let’s write a simple example of geospatial analysis in R using the ggplot2 and sf (Simple Features) packages. In this example, I’ll show the distribution of earthquakes using a dataset from the US Geological Survey (USGS).

Let’s plot earthquake locations on a map of the world.

# Load required libraries
library(ggplot2)
library(sf)

# Load earthquake data from USGS
earthquakes <- st_read("https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/all_month.geojson")

# Check the structure of the data
str(earthquakes)

# Plot the map
world_map <- map_data("world")

ggplot() +
  geom_polygon(data = world_map, aes(x = long, y = lat, group = group), fill = "grey", color = "black") +
  geom_point(data = earthquakes, aes(x = longitude, y = latitude), color = "red", alpha = 0.5) +
  coord_fixed(1.3) +
  labs(title = "Earthquakes Worldwide (Last Month)",
       x = "Longitude",
       y = "Latitude")

Let me also explain the above code.

• Load the necessary libraries ggplot2 for plotting and sf for working with spatial data.
• Load earthquake data using st_read() function from the USGS GeoJSON feed. This data includes information about earthquakes worldwide.
• Inspect the structure of the data using str() function to understand its attributes.
• Use map_data() function to get the map data, and then use ggplot() to create a blank canvas. We use geom_polygon() to draw the world map with gray fill color and black borders. We use geom_point() to plot earthquake locations with red color and reduced opacity (alpha = 0.5).
• Adjust the coordinate system using coord_fixed() to ensure proper scaling, and add appropriate labels to the plot using labs().

This code creates a simple visualization of earthquake locations on a map of the world, allowing us to analyze their distribution geospatially.

Another Example Code

I’ll also give you another example which shows geospatial analysis in R, focusing on creating a choropleth map to visualize population density in different regions using a built-in dataset.

# Load required libraries
library(ggplot2)
library(sf)

# Load world map data
world_map <- st_read(system.file("shape/nc.shp", package = "sf"))

# Plot choropleth map of population density
ggplot() +
  geom_sf(data = world_map, aes(fill = AREA)) +
  scale_fill_gradient(low = "lightblue", high = "darkblue", name = "Population Density") +
  labs(title = "Population Density in Different Regions")

Simply, by using the packages like sf and ggplot2, R facilitates efficient data handling and creation of insightful visualizations such as maps and choropleths.