Data Carpentry for Biologists

Spatial Data Introduction

Notes

Remember to download and put into data subdirectory:

Load the following into browser window:

Set-up R Console:

library(raster)
library(ggplot2)

raster import and structure

  • Spatial data is often organized in a raster.
    • gridded format (like coordinates)
    • each pixel is a value
    • most remote sensing and environmental data

  • raster() import

  • DSM is Digital Surface Model: elevation of top physical point
  • See diagram
dsm_harv <- raster("data/neon-airborne/harv_dsmCrop.tif")
  • Metadata is important to describe the context of spatial data.
    • bands
    • projection
    • units
    • min, max, mean

Plotting spatial data with ggplot

  • Continue using what we’ve been learning
  • Useful for making nice maps combined with other figures

Three steps (write on board):

  1. Do Spatial Work (just importing so far)
  2. Convert to Data Frame (this is what ggplot works with)
  3. Make Plots
  • Convert using as.data.frame (overloaded by raster to convert spatial data)
dsm_harv_df = as.data.frame(dsm_harv, xy = TRUE)
head(dsm_harv_df)
  • Can then plot using geom_raster
ggplot() +
  geom_raster(data = dsm_harv_df, 
              aes(x = x, y = y, fill = HARV_dsmCrop))
  • Because this is a data frame we can treat raster values like they are part of a normal table
ggplot() +
  geom_histogram(data = dsm_harv_df, 
                 aes(x = HARV_dsmCrop))

Do Task 1 of Canopy Height from Space.

raster math

Show > * Canopy Height Model picture

  • The DSM data is a Digital Surface Model: elevation of top physical point
  • DTM is Digital Terrain Model: elevation of the ground
  • We can create a Canopy Height Model (CHM) by taking the difference between them
dtm_harv <- raster("data/neon-airborne/harv_dtmcrop.tif")
chm_harv <- dsm_harv - dtm_harv
  • Math happens on a cell by cell (elementwise) basis
  • Can then graph this new raster by following the three rules
    1. Already did spatial work
    1. Convert to data frame
chm_harv_df = as.data.frame(chm_harv, xy = TRUE)
    1. Make plot
ggplot() +
  geom_raster(data = _harv_df, 
              aes(x = x, y = y, fill = layer))

Do Task 2 of Canopy Height from Space.

Import and reproject shapefiles

  • vector data includes points, lines, and polygons
  • shapefiles are one main format
    • set of multiple files
      • same name, different extensions
    • readOGR("directory", "file_name_without_extensions")
      • stores data in a single data.frame
      • access ‘attributes’ similar to GIS software using $
        • file_name$site_id
library(rgdal)

plots_harv <- readOGR("data/neon-airborne/plot_locations", 
                      "harv_plots")
  • Plot vector on top of raster
chm_harv_df = as.data.frame(chm_harv, xy = TRUE)
plots_harv_df = as.data.frame(plots_harv)

ggplot() +
  geom_raster(data = chm_harv_df, 
              aes(x = x, y = y, fill = layer)) +
  geom_point(data = plots_harv_df, 
             aes(x = coords.x1, y = coords.x2), color = "yellow")

  • Uh oh, nothing happened.

  • Coordinate Reference System (crs or projection) is different from raster.

crs(chm_harv)
crs(plots_harv)
  • Change projection:
    • reproject raster with projectRaster()
    • reproject vector with spTransform()
plots_harv_utm <- spTransform(plots_harv, crs(chm_harv))
plots_harv_utm_df = as.data.frame(plots_harv_utm)

ggplot() +
  geom_raster(data = chm_harv_df, 
              aes(x = x, y = y, fill = layer)) +
  geom_point(data = plots_harv_utm_df, 
             aes(x = coords.x1, y = coords.x2), color = "yellow")

Do Task 3 of Canopy Height from Space.

Extract raster data

  • Use vector to extract() values from raster
  • These are canopy heights from chm_harv at the coordinates from plots_harv_utm.
plots_chm <- extract(chm_harv, plots_harv_utm)
  • Order of values lines up with plots_harv_utm$plot_id.
plots_harv_utm$plot_id
plots_chm <- data.frame(plot_num = plots_harv_utm$plot_id, plot_value = plots_chm)
  • Often want values surrounding a point, not just the single pixel that the point lands in
  • Do this using buffer to get the values for all pixels within some buffer distance from the point
extract(chm_harv, plots_harv_utm, buffer = 10)
  • This returns one value for each pixel within the buffer region
  • Also what output is like for line and polygon data

  • One value for each cell intersected by a line or each cell inside a polygon

  • Could keep all of this data, or calculate a value from it
  • Often want an average
extract(chm_harv, plots_harv_utm, buffer = 10, fun = mean)

Do Tasks 4-5 of Canopy Height from Space.

Map of point data

  • Maps are available in the maps package
  • Maps are typically vector data
library(maps)
us_map = map_data("usa")
  • Open map
  • Polygons, but already stored as data frames (already done as.data.frame)
  • Coordinates + Group + Order
  • Group identifies unique polygons
  • Order identifies how the points in each polygon are connected
  • Draw a polygon illustrating points, edges, & order
ggplot() +
  geom_polygon(data = us_map, 
               aes(x = long, y = lat, group = group), 
               fill = "grey")
  • coord_quickmap gives us a reasonable projection
ggplot() +
  geom_polygon(data = us_map, 
               aes(x = long, y = lat, group = group), 
               fill = "grey") +
  coord_quickmap()
  • Add other data on top
  • Data on species occurances using the spocc package
  • Gets data from multiple sources include GBIF
library(spocc)

do_gbif = occ(query = "Dipodomys ordii", 
              from = "gbif", 
              limit = 1000, 
              has_coords = TRUE)
do_data = data.frame(dipo_df$gbif$data)

ggplot() +
  geom_polygon(data = us_map, 
               aes(x = long, y = lat, group = group), 
               fill = "grey") +
  geom_point(data = do_data, 
             aes(x = Dipodomys_ordii.longitude,
                 y = Dipodomys_ordii.latitude)) +
  coord_quickmap()

Do Species Occurrences Map.

Namespacing

library(dplyr)
library(raster)
  • raster’s select function overwrites dplyr’s select function
  • Demo error
select(do_data, Dipodomys_ordii.latitude)
  • To use dplyr’s function
dply::select(do_data, Dipodomys_ordii.latitude)

Making your own vector data

  • Make spatial data from from non-spatial data with latitudes and longitudes
  • Do to combine with other spatial data
  • Need to know the proj4string for standard latitude/longitude data
  • "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"
points_crs <- crs("+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0")
do_data_spat <- SpatialPointsDataFrame(
	do_data[c('Dipodomys_ordii.longitude', 'Dipodomys_ordii.latitude')], 
	do_data, 
	proj4string = points_crs)
str(do_data_spat)
  • do_data was a regular data frame, so do the same thing with your down data after loading it using read.csv
  • Now you can do things like reproject and extract values from rasters

Do Species Occurrences Elevation Histogram