Reading Spatial Data in R

HES 505 Fall 2023: Session 4

Matt Williamson

Objectives

  1. Revisit the components of spatial data

  2. Describe some of the key considerations for thinking about spatial data

  3. Introduce the two primary R packages for spatial workflows

  4. Learn to read and explore spatial objects in R

Describing Absolute Locations

  • Coordinates: 2 or more measurements that specify location relative to a reference system

  • Cartesian coordinate system

  • origin (O) = the point at which both measurement systems intersect

  • Adaptable to multiple dimensions (e.g. z for altitude)

Cartesian Coordinate System

Locations on a Globe

  • The earth is not flat…

Latitude and Longitude

Locations on a Globe

  • The earth is not flat…

  • Global Reference Systems (GRS)

  • Graticule: the grid formed by the intersection of longitude and latitude

  • The graticule is based on an ellipsoid model of earth’s surface and contained in the datum

Global Reference Systems

The datum describes which ellipsoid to use and the precise relations between locations on earth’s surface and Cartesian coordinates

  • Geodetic datums (e.g., WGS84): distance from earth’s center of gravity

  • Local data (e.g., NAD83): better models for local variation in earth’s surface

Describing location: extent

  • How much of the world does the data cover?

  • For rasters, these are the corners of the lattice

  • For vectors, we call this the bounding box

Describing location: resolution

  • Resolution: the accuracy that the location and shape of a map’s features can be depicted

  • Minimum Mapping Unit: The minimum size and dimensions that can be reliably represented at a given map scale.

  • Map scale vs. scale of analysis

The earth is not flat…

Projections

  • But maps, screens, and publications are…

  • Projections describe how the data should be translated to a flat surface

  • Rely on ‘developable surfaces’

  • Described by the Coordinate Reference System (CRS)

Developable Surfaces

Projection necessarily induces some form of distortion (tearing, compression, or shearing)

Coordinate Reference Systems

  • Some projections minimize distortion of angle, area, or distance

  • Others attempt to avoid extreme distortion of any kind

  • Includes: Datum, ellipsoid, units, and other information (e.g., False Easting, Central Meridian) to further map the projection to the GCS

  • Not all projections have/require all of the parameters

Choosing Projections

  • Equal-area for thematic maps

  • Conformal for presentations

  • Mercator or equidistant for navigation and distance

Geometries, support, and spatial messiness

Geometries

  • Vectors store aggregate the locations of a feature into a geometry
  • Most vector operations require simple, valid geometries

Image Source: Colin Williams (NEON)

Valid Geometries

  • A linestring is simple if it does not intersect
  • Valid polygons
  • Are closed (i.e., the last vertex equals the first)
  • Have holes (inner rings) that inside the the exterior boundary
  • Have holes that touch the exterior at no more than one vertex (they don’t extend across a line) - For multipolygons, adjacent polygons touch only at points
  • Do not repeat their own path

Empty Geometries

  • Empty geometries arise when an operation produces NULL outcomes (like looking for the intersection between two non-intersecting polygons)

  • sf allows empty geometries to make sure that information about the data type is retained

  • Similar to a data.frame with no rows or a list with NULL values

  • Most vector operations require simple, valid geometries

Support

  • Support is the area to which an attribute applies.

  • For vectors, the attribute-geometry-relationship can be:

  • constant = applies to every point in the geometry (lines and polygons are just lots of points)

  • identity = a value unique to a geometry

  • aggregate = a single value that integrates data across the geometry

  • Rasters can have point (attribute refers to the cell center) or cell (attribute refers to an area similar to the pixel) support

Spatial Messiness

  • Quantitative geography requires that our data are aligned

  • Achieving alignment is part of reproducible workflows

  • Making principled decisions about projections, resolution, extent, etc

Mapping Location in R

Data Types and R Packages

Data Types

  • Vector Data
    • Point features
    • Line features
    • Area features (polygons)
  • Raster Data
    • Spatially continuous field
    • Based on pixels (not points)

Reading in Spatial Data: spreadsheets

  • Most basic form of spatial data

  • Need x (longitude) and y (latitude) as columns

  • Need to know your CRS

  • read_*** necessary to bring in the data

library(tidyverse)
library(sf)

file.to.read <- read_csv(file = "path/to/your/file", 
                         col_names = TRUE, col_types = NULL, 
                         na =na = c("", "NA"))

file.as.sf <- st_as_sf(file.to.read, 
                       coords = c("longitude", "latitude"), 
                       crs=4326)

Reading in Spatial Data: shapefiles

  • ALL FILES NEED TO BE IN THE SAME FOLDER
  • .shp is the shapefile itself
  • .prj contains the CRS information
  • .dbf contains the attributes
  • .shx contains the indices for matching attributes to geometries
  • other extensions contain metadata

  • st_read and read_sf in the sf package will read shapefiles into R

  • read_sf leaves character vectors alone (often beneficial)

  • st_read can handle other datatypes (like geodatabases)

  • Returns slightly different classes

Reading in Spatial Data: shapefiles

library(sf)
shapefile.inR <- read_sf(dsn = "path/to/file.shp", 
                         layer=NULL, geometry_column=...)

Reading in Spatial Data: rasters

  • rast will read rasters using the terra package

  • Also used to create rasters from scratch

  • Returns SpatRaster object

library(sf)
raster.inR <- rast(x = "path/to/file.shp", 
                         lyrs=NULL)

Introducing the Data

  • Good idea to get to know your data before manipulating it

  • str, summary, nrow, ncol are good places to start

  • st_crs (for sf class objects) and crs (for SpatRaster objects)

  • We’ll practice a few of these now…

Saving your data

  • write_sf for sf objects; writeRaster for SpatRasters
library(sf)
library(terra)

write_sf(object = object.to.save, dsn = "path/to/save/object", append = FALSE)
writeRaster(x=object, filename = "path/to/save")