Example 2: Raster Data Analysis

Example 1: Importing and Plotting Spatial Data (Vector)

Example 3 using rgdal (Reading and Plotting Spatial Data)

R

# Load required packages
library(raster)
 
# Read a raster dataset (elevation data)
elevation <- raster(system.file("external/test.grd", package="raster"))
 
# Compute statistics on the elevation data
elev_summary <- summary(elevation)
 
# Display the summary statistics
print(elev_summary)

Output:

             test
Min.     138.7071
1st Qu.  293.9575
Median   371.9001
3rd Qu.  501.0102
Max.    1736.0580
NA's    6022.0000

In this example, we use the raster package to read a raster dataset containing elevation data. We then compute summary statistics, such as minimum, maximum, and mean elevation, using the summary() function. The result, elev_summary, provides information about the elevation dataset.

Plot the graph for Geospatial Data in R

R

# Install and load necessary packages if you haven't already
install.packages("ggplot2")
install.packages("maps")
install.packages("plotly")
 
library(ggplot2)
library(maps)
library(plotly)
 
# Load earthquake data from the 'quakes' dataset
data(quakes)
 
# Create a basic map of earthquake occurrences
world_map <- map_data("world")
ggplot() +
  geom_polygon(data = world_map, aes(x = long, y = lat, group = group), 
               fill = "white", color = "black") +
  geom_point(data = quakes, aes(x = long, y = lat, size = mag, 
                                color = depth), alpha = 0.7) +
  scale_size_continuous(range = c(1, 10)) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(
    title = "Global Earthquake Occurrences",
    subtitle = "Magnitude and Depth",
    x = "",
    y = ""
  ) +
  theme_void() +
  theme(plot.title = element_text(hjust = 0.5, size = 18),
        plot.subtitle = element_text(hjust = 0.5, size = 14))
 
# Make the plot interactive using plotly
earthquake_plot <- ggplotly()
 
# Display the interactive plot
earthquake_plot

Output:

Introduction to Geospatial Data Analysis with R

ggplot(): We initialize the plot.
geom_polygon(): We add a layer for drawing the world map with white fill and black borders.
geom_point(): We add a layer for plotting earthquake occurrences as points. We map the longitude (long) to the x-axis, latitude (lat) to the y-axis, magnitude (mag) to the size of points, and depth (depth) to the color of points. The alpha parameter controls the transparency of points.
scale_size_continuous(): We customize the size range of the points.
scale_color_gradient(): We customize the color scale of the points from blue (low depth) to red (high depth).
labs(): We set the plot title, subtitle, and axis labels.
theme_void(): We use a minimal theme with no background.
theme(plot.title): We further customize the appearance of the plot by adjusting the title’s size and position.

Geospatial Data Analysis with R

Geospatial data analysis involves working with data that has a geographic or spatial component. It allows us to analyze and visualize data in the context of its location on the Earth’s surface. R Programming Language is a popular open-source programming language, that offers a wide range of packages and tools for geospatial data analysis.

Example 2: Raster Data Analysis

R

Plot the graph for Geospatial Data in R

R

Geospatial Data Analysis with R

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