Understanding Reverse Coordinates for Multi-Polygon Data in R

Working with shapefiles can be a daunting task, especially when dealing with geospatial data. In this article, we’ll explore the concept of reverse coordinates and how to apply them to multi-polygon data in R using the sf package.

Introduction to Geodetic Coordinate Systems

Before diving into the solution, it’s essential to understand the basics of geodetic coordinate systems. A geodetic coordinate system is a reference framework for mapping the Earth’s surface. It consists of two fundamental components: the ellipsoid and the datum. The ellipsoid represents the shape of the Earth, while the datum defines the reference point for latitude and longitude coordinates.

In this article, we’ll focus on the International Standard Normal Mercator (ISN) 2016 coordinate system, which is used to represent Iceland’s subdivisions. This system uses a projection called the Islands Net 2016 (IN2016), which transforms the ellipsoid into a two-dimensional plane.

The Problem with Reverse Coordinates

The question presents an interesting scenario where the X and Y coordinates appear to be swapped around in the shapefile for Iceland’s subdivisions. This suggests that the data is not properly transformed from the ISN 2016 coordinate system, resulting in incorrect latitudes and longitudes.

To resolve this issue, we need to transform the data back to a more suitable coordinate system, such as the Web Mercator (WGS84) or the Pseudo-Mercator projection. This transformation will ensure that the coordinates are correctly oriented and represented.

Solution: Using sf Package in R

Fortunately, the sf package in R provides an efficient way to handle geospatial data transformations. We can leverage this package to swap the geometry lat/lon columns without modifying the sf attributes.

Here’s an example code snippet that demonstrates how to achieve this:

## Load necessary libraries
library(sf)
library(tidyverse)

## Read shapefile data using st_read()
ic_2003 <- st_read("mork_kjordaemiPolygon.shp")

## Apply transformation using modify() function
ic_2003 <- ic_2003 %>% 
  mutate(geometry = modify(geometry, modify, ~list(.[[1]][,c(2,1)])))

## Plot transformed data using ggplot()
ggplot(ic_2003) +
  geom_sf() +
  coord_sf()

In this example, we first load the necessary libraries, including sf and tidyverse. We then read the shapefile data using st_read().

Next, we apply the transformation using the modify() function from the tidyverse package. This function allows us to swap the geometry lat/lon columns without modifying the sf attributes. The expression within modify() specifies that we want to reverse the order of the coordinates in each geometry element (i.e., [1][,c(2,1)]).

Finally, we plot the transformed data using ggplot(), which should display the correct orientation and representation of the coordinates.

Alternative Solution: Using st_transform()

Another approach is to use st_transform() from the sf package. This function allows us to transform the coordinate system without modifying the geometry attributes.

Here’s an example code snippet that demonstrates how to achieve this:

## Load necessary libraries
library(sf)
library(tidyverse)

## Read shapefile data using st_read()
ic_2003 <- st_read("mork_kjordaemiPolygon.shp")

## Transform coordinate system using st_transform()
ic_2003 <- st_transform(ic_2003, 4326) # Web Mercator (WGS84)

## Plot transformed data using ggplot()
ggplot(ic_2003) +
  geom_sf() +
  coord_sf()

In this example, we first load the necessary libraries and read the shapefile data.

Next, we transform the coordinate system using st_transform() to convert it from ISN 2016 to Web Mercator (WGS84). The second argument specifies the target CRS (coordinate reference system) code, which is 4326 for WGS84.

Finally, we plot the transformed data using ggplot(), which should display the correct orientation and representation of the coordinates.

Conclusion

In this article, we’ve explored the concept of reverse coordinates for multi-polygon data in R. We’ve discussed the importance of understanding geodetic coordinate systems and how to apply transformations to achieve accurate representations.

We’ve presented two approaches to solving the problem: using modify() from the tidyverse package and using st_transform() from the sf package. Both methods allow us to transform coordinates without modifying the geometry attributes, ensuring correct orientation and representation.

By leveraging these techniques, you can efficiently handle geospatial data transformations in R and ensure accurate representations of your spatial data.

Last modified on 2023-09-04