Understanding Spatial Data Visualization with ggplot2

Spatial data visualization is a crucial aspect of data analysis, especially when dealing with geospatial data. In this article, we will explore the nuances of spatial data visualization using the popular R package ggplot2, specifically focusing on sf objects and their relationship with legends.

Introduction to sf Objects

sf (Simple Features) objects are a type of geometry object used in R for storing and manipulating geographic data. They provide an efficient way to work with spatial data by allowing you to perform operations like clipping, intersecting, and buffering with ease. In this context, we will focus on using sf objects as layers in ggplot2 plots.

Creating Spatial Layers

When creating a plot that includes multiple layers of spatial data, it’s essential to understand how these layers interact with each other. The geom_sf() function is used to add spatial layers to a ggplot2 plot. Here’s an example:

ggplot() +
  geom_sf(data = track, color = "orange", lwd = 1.4) +          # line
  geom_sf(data = SZ_cropped, color = "grey40", lwd = 0.5) +     # line
  geom_sf(aes(size = total), data = x, color = "black", alpha = 0.7, shape = 21, fill = "purple") +      # dots

In this example, we have three spatial layers: two line layers (track and SZ_cropped) and one dot layer (x). Each layer is represented by a separate call to geom_sf().

Proportional Area Dots

When creating the dot layer, we use the size aesthetic to represent the data values. This allows ggplot2 to display the dots in proportion to their corresponding values. The scale_size_area() function is used to create a scale that maps area sizes to numerical values:

scale_size_area(name = "Count")

This creates a legend that displays square symbols of a single size, which can be misleading when dealing with proportional data.

Customizing the Legend

To match the aesthetics in the plot, we need to customize the legend for the dot layer. One approach is to override the default shape and fill colors used by ggplot2:

guides(size = guide_legend(override.aes = list(shape = 21, fill = "purple", color = "black")))

This code tells ggplot2 to use a custom legend for the dot layer, with square symbols of purple color.

Using max_size in scale_size_area()

Another approach is to use the max_size argument in scale_size_area() to limit the maximum size of the dots. This can help when dealing with large datasets that may produce very large dots:

scale_size_area(name = "Count", max_size = 5)

This creates a legend where the largest dot has a size of 5, making it easier to interpret.

Conclusion

When working with sf objects in ggplot2, it’s essential to understand how spatial layers interact with each other. By customizing legends and using scale_size_area() with max_size, we can create plots that accurately represent proportional data. Remember to experiment with different approaches to find the best fit for your specific use case.

Additional Tips

• When working with large datasets, consider using the geom_sf cache to improve performance.
• Use the coord_cartesian() function to limit the extent of the plot to a specific region.
• Experiment with different shapes and colors in the legend to find the best representation for your data.

Example Use Case: Visualizing Urban Heat Island Effect

Suppose we have three datasets:

1. A city boundary dataset (track)
2. A temperature dataset (temp)
3. An urban heat island index dataset (heat_index)

We want to create a plot that shows the relationship between urban heat island indices and temperature values, with the city boundary as a reference.

Here’s an example code snippet:

ggplot() +
  geom_sf(data = track, color = "orange", lwd = 1.4) +          # city boundary
  geom_sf(data = temp, color = "grey40", lwd = 0.5) +     # temperature data
  geom_sf(aes(size = heat_index), data = heat_index, color = "black", alpha = 0.7, shape = 21, fill = "purple") +      # urban heat island index
  scale_size_area(name = "Heat Index")

This plot shows the relationship between urban heat island indices and temperature values, with the city boundary as a reference.

Note: This is just an example code snippet to illustrate how sf objects can be used in ggplot2 plots.

Last modified on 2024-07-05