Tutorial

Vector 03: When Vector Data Don't Line Up - Handling Spatial Projection & CRS in R

Authors: Joseph Stachelek, Leah Wasser, Megan A. Jones

Last Updated: Apr 8, 2021

In this tutorial, we will create a base map of our study site using a United States state and country boundary accessed from the . We will learn how to map vector data that are in different CRS and thus don't line up on a map.

Learning Objectives

After completing this tutorial, you will be able to:

  • Identify the CRS of a spatial dataset.
  • Differentiate between with geographic vs. projected coordinate reference systems.
  • Use the proj4 string format which is one format used used to store & reference the CRS of a spatial object.

Things You鈥檒l Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.

Install R Packages

  • raster: install.packages("raster")

  • rgdal: install.packages("rgdal")

  • sp: install.packages("sp")

  • More on Packages in R 鈥� Adapted from Software Carpentry.

Data to Download

These vector data provide information on the site characterization and infrastructure at the National Ecological Observatory Network's Harvard Forest field site. The Harvard Forest shapefiles are from the archives. US Country and State Boundary layers are from the .

The LiDAR and imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites and processed at NEON headquarters. The entire dataset can be accessed by request from the .

Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.

An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

Working With Spatial Data From Different Sources

To support a project, we often need to gather spatial datasets for from different sources and/or data that cover different spatial extents. Spatial data from different sources and that cover different extents are often in different Coordinate Reference Systems (CRS).

Some reasons for data being in different CRS include:

  1. The data are stored in a particular CRS convention used by the data provider; perhaps a federal agency or a state planning office.
  2. The data are stored in a particular CRS that is customized to a region. For instance, many states prefer to use a State Plane projection customized for that state.
Examples of how different projections will alter the shape of a map in different ways.
Maps of the United States using data in different projections. Notice the differences in shape associated with each different projection. These differences are a direct result of the calculations used to "flatten" the data onto a 2-dimensional map. Often data are stored purposefully in a particular projection that optimizes the relative shape and size of surrounding geographic boundaries (states, counties, countries, etc). Source: M. Corey,

Check out this short video from highlighting how map projections can make continents seems proportionally larger or smaller than they actually are!

In this tutorial we will learn how to identify and manage spatial data in different projections. We will learn how to reproject the data so that they are in the same projection to support plotting / mapping. Note that these skills are also required for any geoprocessing / spatial analysis, as data need to be in the same CRS to ensure accurate results.

We will use the rgdal and raster libraries in this tutorial.

# load packages
library(rgdal)  # for vector work; sp package should always load with rgdal. 
library (raster)   # for metadata/attributes- vectors or rasters

# set working directory to data folder
# setwd("pathToDirHere")

Import US Boundaries - Census Data

There are many good sources of boundary base layers that we can use to create a basemap. Some R packages even have these base layers built in to support quick and efficient mapping. In this tutorial, we will use boundary layers for the United States, provided by the

It is useful to have shapefiles to work with because we can add additional attributes to them if need be - for project specific mapping.

Read US Boundary File

We will use the readOGR() function to import the /US-Boundary-Layers/US-State-Boundaries-Census-2014 layer into R. This layer contains the boundaries of all continental states in the U.S.. Please note that these data have been modified and reprojected from the original data downloaded from the Census website to support the learning goals of this tutorial.

# Read the .csv file
State.Boundary.US <- readOGR("NEON-DS-Site-Layout-Files/US-Boundary-Layers",
          "US-State-Boundaries-Census-2014")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/olearyd/Git/data/NEON-DS-Site-Layout-Files/US-Boundary-Layers", layer: "US-State-Boundaries-Census-2014"
## with 58 features
## It has 10 fields
## Integer64 fields read as strings:  ALAND AWATER

## Warning in readOGR("NEON-DS-Site-Layout-Files/US-Boundary-Layers", "US-
## State-Boundaries-Census-2014"): Z-dimension discarded

# look at the data structure
class(State.Boundary.US)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

Note: the Z-dimension warning is normal. The readOGR() function doesn't import z (vertical dimension or height) data by default. This is because not all shapefiles contain z dimension data.

Now, let's plot the U.S. states data.

# view column names
plot(State.Boundary.US, 
     main="Map of Continental US State Boundaries\n US Census Bureau Data")

Continental U.S. state boundaries.

U.S. Boundary Layer

We can add a boundary layer of the United States to our map to make it look nicer. We will import NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-Boundary-Dissolved-States. If we specify a thicker line width using lwd=4 for the border layer, it will make our map pop!

# Read the .csv file
Country.Boundary.US <- readOGR("NEON-DS-Site-Layout-Files/US-Boundary-Layers",
          "US-Boundary-Dissolved-States")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/olearyd/Git/data/NEON-DS-Site-Layout-Files/US-Boundary-Layers", layer: "US-Boundary-Dissolved-States"
## with 1 features
## It has 9 fields
## Integer64 fields read as strings:  ALAND AWATER

## Warning in readOGR("NEON-DS-Site-Layout-Files/US-Boundary-Layers", "US-
## Boundary-Dissolved-States"): Z-dimension discarded

# look at the data structure
class(Country.Boundary.US)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

# view column names
plot(State.Boundary.US, 
     main="Map of Continental US State Boundaries\n US Census Bureau Data",
     border="gray40")

# view column names
plot(Country.Boundary.US, 
     lwd=4, 
     border="gray18",
     add=TRUE)

Continental U.S. state boundaries with the U.S. boundary emphasized with a thicker border.

Next, let's add the location of a flux tower where our study area is. As we are adding these layers, take note of the class of each object.

# Import a point shapefile 
point_HARV <- readOGR("NEON-DS-Site-Layout-Files/HARV/",
                      "HARVtower_UTM18N")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/olearyd/Git/data/NEON-DS-Site-Layout-Files/HARV", layer: "HARVtower_UTM18N"
## with 1 features
## It has 14 fields

class(point_HARV)

## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"

# plot point - looks ok? 
plot(point_HARV, 
     pch = 19, 
     col = "purple",
     main="Harvard Fisher Tower Location")

Fisher Tower location represented by a point.

The plot above demonstrates that the tower point location data are readable and will plot! Let's next add it as a layer on top of the U.S. states and boundary layers in our basemap plot.

# plot state boundaries  
plot(State.Boundary.US, 
     main="Map of Continental US State Boundaries \n with Tower Location",
     border="gray40")

# add US border outline 
plot(Country.Boundary.US, 
     lwd=4, 
     border="gray18",
     add=TRUE)

# add point tower location
plot(point_HARV, 
     pch = 19, 
     col = "purple",
     add=TRUE)

Continental U.S. state boundaries with the U.S. boundary emphasized with a thicker border; note that the Fisher Tower point is not currently visible.

What do you notice about the resultant plot? Do you see the tower location in purple in the Massachusetts area? No! So what went wrong?

Let's check out the CRS (crs()) of both datasets to see if we can identify any issues that might cause the point location to not plot properly on top of our U.S. boundary layers.

# view CRS of our site data
crs(point_HARV)

## CRS arguments:
##  +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs

# view crs of census data
crs(State.Boundary.US)

## CRS arguments: +proj=longlat +datum=WGS84 +no_defs

crs(Country.Boundary.US)

## CRS arguments: +proj=longlat +datum=WGS84 +no_defs

It looks like our data are in different CRS. We can tell this by looking at the CRS strings in proj4 format.

Understanding CRS in Proj4 Format

The CRS for our data are given to us by R in proj4 format. Let's break down the pieces of proj4 string. The string contains all of the individual CRS elements that R or another GIS might need. Each element is specified with a + sign, similar to how a .csv file is delimited or broken up by a ,. After each + we see the CRS element being defined. For example projection (proj=) and datum (datum=).

UTM Proj4 String

Our project string for point_HARV specifies the UTM projection as follows:

+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0

  • proj=utm: the projection is UTM
  • zone=18: the zone is 18
  • datum=WGS84: the datum WGS84 (the datum refers to the 0,0 reference for the coordinate system used in the projection)
  • units=m: the units for the coordinates are in METERS
  • ellps=WGS84: the ellipsoid (how the earth's roundness is calculated) for the data is WGS84

Note that the zone is unique to the UTM projection. Not all CRS will have a zone.

Geographic (lat / long) Proj4 String

Our project string for State.boundary.US and Country.boundary.US specifies the lat/long projection as follows:

+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

  • proj=longlat: the data are in a geographic (latitude and longitude) coordinate system
  • datum=WGS84: the datum is WGS84
  • ellps=WGS84: the ellipsoid is WGS84

Note that there are no specified units above. This is because this geographic coordinate reference system is in latitude and longitude which is most often recorded in Decimal Degrees.

**Data Tip:** the last portion of each `proj4` string is `+towgs84=0,0,0 `. This is a conversion factor that is used if a datum conversion is required. We will not deal with datums in this tutorial series.

CRS Units - View Object Extent

Next, let's view the extent or spatial coverage for the point_HARV spatial object compared to the State.Boundary.US object.

# extent for HARV in UTM
extent(point_HARV)

## class      : Extent 
## xmin       : 732183.2 
## xmax       : 732183.2 
## ymin       : 4713265 
## ymax       : 4713265

# extent for object in geographic
extent(State.Boundary.US)

## class      : Extent 
## xmin       : -124.7258 
## xmax       : -66.94989 
## ymin       : 24.49813 
## ymax       : 49.38436

Note the difference in the units for each object. The extent for State.Boundary.US is in latitude and longitude, which yields smaller numbers representing decimal degree units; however, our tower location point is in UTM, which is represented in meters.

Proj4 & CRS Resources

  • To view a list of datum conversion factors, type projInfo(type = "datum") into the R console.

Reproject Vector Data

Now we know our data are in different CRS. To address this, we have to modify or reproject the data so they are all in the same CRS. We can use spTransform() function to reproject our data. When we reproject the data, we specify the CRS that we wish to transform our data to. This CRS contains the datum, units and other information that R needs to reproject our data.

The spTransform() function requires two inputs:

  1. The name of the object that you wish to transform
  2. The CRS that you wish to transform that object too. In this case we can use the crs() of the State.Boundary.US object as follows: crs(State.Boundary.US)
**Data Tip:** `spTransform()` will only work if your original spatial object has a CRS assigned to it AND if that CRS is the correct CRS!

Next, let's reproject our point layer into the geographic latitude and longitude WGS84 coordinate reference system (CRS).

# reproject data
point_HARV_WGS84 <- spTransform(point_HARV,
                                crs(State.Boundary.US))

# what is the CRS of the new object
crs(point_HARV_WGS84)

## CRS arguments: +proj=longlat +datum=WGS84 +no_defs

# does the extent look like decimal degrees?
extent(point_HARV_WGS84)

## class      : Extent 
## xmin       : -72.17266 
## xmax       : -72.17266 
## ymin       : 42.5369 
## ymax       : 42.5369

Once our data are reprojected, we can try to plot again.

# plot state boundaries  
plot(State.Boundary.US, 
     main="Map of Continental US State Boundaries\n With Fisher Tower Location",
     border="gray40")

# add US border outline 
plot(Country.Boundary.US, 
     lwd=4, 
     border="gray18",
     add=TRUE)

# add point tower location
plot(point_HARV_WGS84, 
     pch = 19, 
     col = "purple",
     add=TRUE)

Continental U.S. state boundaries with the U.S. country border emphasized with a thicker border and with the Fisher Tower represented as a point.

Reprojecting our data ensured that things line up on our map! It will also allow us to perform any required geoprocessing (spatial calculations / transformations) on our data.

## Challenge - Reproject Spatial Data

Create a map of the North Eastern United States as follows:

  1. Import and plot Boundary-US-State-NEast.shp. Adjust line width as necessary.
  2. Reproject the layer into UTM zone 18 north.
  3. Layer the Fisher Tower point location point_HARV on top of the above plot.
  4. Add a title to your plot.
  5. Add a legend to your plot that shows both the state boundary (line) and the Tower location point.

A close-up view of the northeastern U.S. with the Fisher Tower location as a point symbol.

Get Lesson Code

03-when-vector-data-dont-line-up-CRS.R

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