Vector and Raster Geospatial Data
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- Meet the key R packages for geospatial analysis
- Learn basic wrangling of vector and raster data
- Distinguish “scriptable” tasks from desktop GIS needs
- Create, load and plot vector data types, or “features”
- Filter features based on associated data or on space
- Perform geometric operations on polygons
- Load, manipulate and extract raster data
The key R packages for this lesson are
In addition to the common case of depending on other R packages, these two have dependencies on system libraries.
- GDAL for read/write in geospatial data formats
- GEOS for geometry operations
- PROJ.4 for cartographic projections
System libraries cannot be installed by R’s
install.packages(), but can be
bundled with these packages and for private use by them. Either way, the
necessary libraries are maintained by the good people at the Open Source
Geospatial Foundation for free and easy distribution.
library(sf) shp <- 'data/cb_2016_us_county_5m' counties <- st_read(shp, stringsAsFactors = FALSE)
counties object is a
data.frame that includes a
sfc, which stands for
“simple feature column”. This special column is usually called “geometry” or
Simple feature collection with 6 features and 9 fields geometry type: MULTIPOLYGON dimension: XY bbox: xmin: -114.7556 ymin: 29.26116 xmax: -81.10192 ymax: 38.77443 epsg (SRID): 4269 proj4string: +proj=longlat +datum=NAD83 +no_defs STATEFP COUNTYFP COUNTYNS AFFGEOID GEOID NAME LSAD 1 04 015 00025445 0500000US04015 04015 Mohave 06 2 12 035 00308547 0500000US12035 12035 Flagler 06 3 20 129 00485135 0500000US20129 20129 Morton 06 4 28 093 00695770 0500000US28093 28093 Marshall 06 5 29 510 00767557 0500000US29510 29510 St. Louis 25 6 35 031 00929107 0500000US35031 35031 McKinley 06 ALAND AWATER geometry 1 34475567011 387344307 MULTIPOLYGON (((-114.7556 3... 2 1257365642 221047161 MULTIPOLYGON (((-81.52366 2... 3 1889993251 507796 MULTIPOLYGON (((-102.042 37... 4 1828989833 9195190 MULTIPOLYGON (((-89.72432 3... 5 160458044 10670040 MULTIPOLYGON (((-90.31821 3... 6 14116799068 14078537 MULTIPOLYGON (((-109.0465 3...
data.frame column, the
geometry column is comprised of a single
data type. The “MULTIPOLYGON” is just one of several standard geometric data
|POINT||zero-dimensional geometry containing a single point|
|LINESTRING||sequence of points connected by straight, non-self intersecting line pieces; one-dimensional geometry|
|POLYGON||sequence of points in closed, non-intersecting rings; the first denotes the exterior ring, any subsequent rings denote holes|
|MULTI*||set of * (POINT, LINESTRING, or POLYGON)|
The spatial data types are built upon eachother in a logical way: lines are built from points, polygons are built from lines, and so on.
We can create any of these spatial objects from coordinates.
sfc object with a single “POINT”, corresponding to SESYNC’s postition
in WGS84 degrees lat and degrees lon.
sesync <- st_sfc(st_point( c(-76.503394, 38.976546)), crs = st_crs(counties))
Coordinate Reference Systems
A key feature of a geospatial data type is its associated CRS, stored as an EPSG ID and an equivalent PROJ.4 string.
Coordinate Reference System: EPSG: 4269 proj4string: "+proj=longlat +datum=NAD83 +no_defs"
A bounding box for all featurs in a
sf data frame is generated by
xmin ymin xmax ymax -179.14734 -14.55255 179.77847 71.35256
The bounding box is not a static attribute—it is determined on-the-fly for the entire table or any subset of features.
library(dplyr) counties_md <- filter( counties, STATEFP == '24')
xmin ymin xmax ymax -79.48765 37.91172 -75.04894 39.72312
A bounding box summarizes the limits, but is not itself a geometry (not a POINT or POLYGON), even though it has a CRS attribute.
Coordinate Reference System: EPSG: 4269 proj4string: "+proj=longlat +datum=NAD83 +no_defs"
A rectangular grid made over a
sf object is a geometry—by default, a POLYGON.
grid_md <- st_make_grid(counties_md, n = 4)
Geometry set for 16 features geometry type: POLYGON dimension: XY bbox: xmin: -79.48765 ymin: 37.91172 xmax: -75.04894 ymax: 39.72312 epsg (SRID): 4269 proj4string: +proj=longlat +datum=NAD83 +no_defs First 5 geometries:
POLYGON ((-79.48765 37.91172, -78.37797 37.9117...
POLYGON ((-78.37797 37.91172, -77.26829 37.9117...
POLYGON ((-77.26829 37.91172, -76.15862 37.9117...
POLYGON ((-76.15862 37.91172, -75.04894 37.9117...
POLYGON ((-79.48765 38.36457, -78.37797 38.3645...
Spatial objects defined by sf are compatible with the
function. Setting the
add = TRUE allows an existing plot to
serve as a layer underneath the new one, so long as the CRS lines up.
plot(grid_md) plot(counties_md['ALAND'], add = TRUE) plot(sesync, col = "green", pch = 20, add = TRUE)
But note that the
plot function won’t prevent you from layering up geometries
with different coordinate systems: you must safegaurd your own plots from this
mistake. The arguments
pch, by the way, are graphical parameters
used in base R, see
An object created with
st_read is a
data.frame, which is why the
filter used above on the non-geospatial column named “STATEFP”
worked normally. The equivalent of a filtering operation on the “geometry”
column is called a spatial “overlay”.
> st_within(sesync, counties_md)
Sparse geometry binary predicate list of length 1, where the predicate was `within' 1: 5
It can be seen as a type of subsetting based on spatial (rather than numeric or
string) matching. Matching is implemented with functions like
The output implies that the 1^st^ (and only) point in
sesync is within the 5th
- What was the message issued by the last command all about?
- It is a reminder that all geometric calculations are performed as if the coordinates (in this case longitutde and latitude) are Cartesian x,y coordinates.
The overlay functions in the sf package follow the pattern
st_predicate(x, y) and perform the test “x [is] predicate y”. Some key
|st_intersects||boundary or interior of x intersects boundary or interior of y|
|st_within||interior and boundary of x do not intersect exterior of y|
|st_contains||y is within x|
|st_overlaps||interior of x intersects interior of y|
|st_equals||x has the same interior and boundary as y|
For the next part of this lesson, we import a new polygon layer corresponding to the 1:250k map of US hydrological units (HUC) downloaded from the United States Geological Survey.
shp <- 'data/huc250k' huc <- st_read(shp, stringsAsFactors = FALSE)
Compare the coordinate reference systems of
huc, as given by
their Proj4 strings.
 "+proj=longlat +datum=NAD83 +no_defs"
 "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +datum=NAD27 +units=m +no_defs"
The Census data uses unprojected (longitude, latitude) coordinates, but
in an Albers equal-area projection (indicated as “+proj=aea”).
st_transform() converts a
sfc between coordinate reference
systems, specified with the parameter
crs = x. A numeric
x must be a valid
EPSG code; a character
x is interpretted as a PROJ.4 string.
prj <- '+proj=aea +lat_1=29.5 +lat_2=45.5 \ +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 \ +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 \ +units=m +no_defs'
Proj4 strings contain a reference to the type of projection, this one is another Albers Equal Area, along with numeric parameters associated with that projection. An additional important parameter that may differ between two coordinate systems is the “datum”, which indicates the standard by which the irregular surface of the Earth is approximated by an ellipsoid in the coordinates themselves.
st_transform() to assign the two layers to a common projection string
prj). This takes a few moments, as it requires re-calculating coordinates for
every vertex of every polygon in the
counties_md <- st_transform( counties_md, crs = prj) huc <- st_transform( huc, crs = prj) sesync <- st_transform( sesync, crs = prj)
plot(counties_md$geometry) plot(huc$geometry, border = 'blue', add = TRUE) plot(sesync, col = 'green', pch = 20, add = TRUE)
The data for a map of watershed boundaries within the state of MD is all here;
in the country-wide
huc and in the state boundary “surrounding” all of
counties_md. To get just the huc in a MD outline:
- remove the internal county boundaries within the state
- clip the hydrological areas to their intersection with the state
The first step is a spatial union operation: we want the resulting object to
combine the area covered by all the multipolygons in
state_md <- st_union(counties_md) plot(state_md)
To perform a union of all sub-geometries in a single
sfc, we use the
st_union() function with a single argument. The output,
state_md, is a new
sfc that is no longer a column of a data frame. Tabular data can’t safely
survive a spatial union and is discarded.
The second step is a spatial intersection, since we want to limit the
polygons to areas covered by both
huc_md <- st_intersection( huc, state_md)
Warning: attribute variables are assumed to be spatially constant throughout all geometries
plot(state_md) plot(huc_md, border = 'blue', col = NA, add = TRUE)
st_intersection() function intersects its first argument with the second.
The individual hydrological units are preserved but any part of them (or any
whole polygon) lying outside the
state_md polygon is cut from the output. The
attribute data remains in the corresponding records of the
data.frame, but (as
warned) has not been updated. For example, the “AREA” attribute of any clipped
HUC does not reflect the new polygon.
The GEOS library provides many functions dealing with distances and areas. Many of these are accessible through the sf package, including:
st_buffer: to create a buffer of specific width around a geometry
st_distance: to calculate the shortest distance between geometries
st_area: to calculate the area of polygons
Keep in mind that all these functions use planar geometry equations and thus become less precise over larger distances, where the Earth’s curvature is noticeable. To calculate geodesic distances that account for that curvature, checkout the geosphere package.
Raster data is a matrix or cube with additional spatial metadata (e.g. extent,
resolution, and projection) that allow its values to be mapped onto geographical
space. The raster package provides the eponymous
for reading the many formats of such data.
The National Land Cover Database is ‘.GRD’ format data, a lot of it. The file provided in this lesson is cropped and reduced to a lower resolution in order to speed processing.
library(raster) nlcd <- raster("data/nlcd_agg.grd")
By default, raster data is not loaded into working memory, as you can confirm
by checking the R object size with
object.size(nlcd). This means that unlike
most analyses in R, you can actually process raster datasets larger than the RAM
available on your computer; the raster package automatically loads pieces of the
data and computes on each of them in sequence.
The default print method for a raster object is a summary of metadata contained in the raster file.
class : RasterLayer dimensions : 2514, 3004, 7552056 (nrow, ncol, ncell) resolution : 150, 150 (x, y) extent : 1394535, 1845135, 1724415, 2101515 (xmin, xmax, ymin, ymax) coord. ref. : +proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs data source : /nfs/public-data/training/nlcd_agg.grd names : nlcd_2011_landcover_2011_edition_2014_03_31 values : 0, 95 (min, max) attributes : ID COUNT Red Green Blue Land.Cover.Class Opacity from: 0 7854240512 0 0 0 Unclassified 1 to : 255 0 0 0 0 0
The plot method interprets the pixel values of the raster matrix according to a pre-defined color scheme.
crop() function trims a raster object to a given spatial “extent” (or
range of x and y values).
extent <- matrix(st_bbox(huc_md), nrow=2) nlcd <- crop(nlcd, extent) plot(nlcd) plot(huc_md, col = NA, add = TRUE)
The extent can be extracted from sp package objects with
but must be created “from scratch” for an
sfc. Here, we crop the
to the extent of the
huc_md polygon, then display both layers on the same map.
Also note that the transformed raster is now loaded in R memory, as indicated by
the size of
nlcd. We could have also saved the output to disk by specifying an
filename argument to
crop; the same is true for many other
functions in the raster package.
A raster is fundamentally a data matrix, and individual pixel values can be extracted by regular matrix subscripting. For example, the value of the bottom-left corner pixel:
> nlcd[1, 1]
The meaning of this number is not immediately clear. For this particular dataset, the mapping of values to land cover classes is described in the data attributes:
ID COUNT Red Green Blue Land.Cover.Class Opacity 1 0 7854240512 0 0.0000000 0 Unclassified 1 2 1 0 0 0.9764706 0 1 3 2 0 0 0.0000000 0 1 4 3 0 0 0.0000000 0 1 5 4 0 0 0.0000000 0 1 6 5 0 0 0.0000000 0 1
Land.Cover.Class vector gives string names for the land cover type
corresponding to the matrix values. Note that we need to add 1 to the raster
value, since these go from 0 to 255, whereas the indexing in R begins at 1.
nlcd_attr <- nlcd@data@attributes lc_types <- nlcd_attr[]$Land.Cover.Class
 "" "Barren Land"  "Cultivated Crops" "Deciduous Forest"  "Developed, High Intensity" "Developed, Low Intensity"  "Developed, Medium Intensity" "Developed, Open Space"  "Emergent Herbaceuous Wetlands" "Evergreen Forest"  "Hay/Pasture" "Herbaceuous"  "Mixed Forest" "Open Water"  "Perennial Snow/Ice" "Shrub/Scrub"  "Unclassified" "Woody Wetlands"
Mathematical functions called on a raster gets applied to each pixel. For a
r, the function
log(r) returns a new raster where each pixel’s
value is the log of the corresponding pixel in
Likewise, addition with
r1 + r2 creates a raster where each pixel is the sum of the
r2, and so on. Naturally, spatial attributes of rasters
(e.g. extent, resolution, and projection) must match for functions that operate
pixel-wise on multiple rasters.
Logical operations work too:
r1 > 5 returns a raster with pixel values
FALSE and is often used in combination with the
pasture <- mask(nlcd, nlcd == 81, maskvalue = FALSE) plot(pasture)
A pasture raster results from unsetting pixel values where the mask (
nlcd == 81)
is false (
maskvalue = FALSE).
To further reduce the resolution of the
nlcd raster, the
function combines values in a block of a given size using a given function.
nlcd_agg <- aggregate(nlcd, fact = 25, fun = modal) nlcd_agg@legend <- nlcd@legend plot(nlcd_agg)
fact = 25 means that we are aggregating blocks 25 x 25 pixels and
modal indicates that the aggregate value is the mode of the original pixels
(averaging would not work since land cover is a categorical variable).
Crossing Rasters with Vectors: Prelude
Presently, to mix raster and vectors, we must convert needed
to their counterpart
sesync <- as(sesync, "Spatial") huc_md <- as(huc_md, "Spatial") counties_md <- as(counties_md, "Spatial")
The creation of geospatial tools in R has been a community effort, and not necessarilly a well-organized one. One current stumbling block is that the raster package, which is tightly integrated with the sp package, has not caught up to the sf package. The still-under-development stars package aims to remedy this problem and others.
Crossing Rasters with Vectors
extract function allows subsetting and aggregation of raster values based
on a vector spatial object.
plot(nlcd) plot(sesync, col = 'green', pch = 16, cex = 2, add = TRUE)
When extracting by point locations (i.e. a SpatialPoints object), the result is a vector of values corresponding to each point.
sesync_lc <- extract(nlcd, sesync)
> lc_types[sesync_lc + 1]
 Developed, Medium Intensity 18 Levels: Barren Land Cultivated Crops ... Woody Wetlands
When extracting with a polygon, the output is a vector of all raster values for pixels falling within that polygon.
county_nlcd <- extract(nlcd_agg, counties_md[1,])
county_nlcd 11 21 22 23 24 41 3 1 4 5 2 1
To get a summary of raster values for each polygon in a
object, add an aggregation function to
extract via the
fun argument. For
fun = modal gives the most common land cover type for each polygon in
modal_lc <- extract(nlcd_agg, huc_md, fun = modal) huc_md$modal_lc <- lc_types[modal_lc + 1]
AREA PERIMETER HUC250K_ HUC250K_ID HUC_CODE 903 6413577966 454290.2 904 916 02050306 915 1982478663 292729.7 916 927 02040205 937 5910074657 503796.5 938 948 02070004 956 3159193443 506765.4 957 968 02060002 966 4580816836 433034.1 967 978 05020006 975 2502118608 252945.8 976 987 02070009 HUC_NAME REG SUB ACC CAT modal_lc 903 Lower Susquehanna 02 0205 020503 02050306 Deciduous Forest 915 Brandywine-Christina 02 0204 020402 02040205 Hay/Pasture 937 Conococheague-Opequon 02 0207 020700 02070004 Hay/Pasture 956 Chester-Sassafras 02 0206 020600 02060002 Cultivated Crops 966 Youghiogheny 05 0502 050200 05020006 Deciduous Forest 975 Monocacy 02 0207 020700 02070009 Cultivated Crops
- raster package vignette.
- R.S. Bivand, E.J. Pebesma and V. Gómez-Rubio (2013) Applied Spatial Data Analysis with R. UseR! Series, Springer.
- R. Lovelace et al., Geocomputation with R
- F. Rodriguez-Sanchez. Spatial data in R: Using R as a GIS.
- CRAN Task View: Analysis of Spatial Data
Produce a map of Maryland counties with the county that contains SESYNC colored in red.
st_buffer to create a 5km buffer around the
state_md border and plot it as a dotted line (
plot(..., lty = 'dotted')) over the true state border. Hint: check the layer’s units with
st_crs() and express any distance in those units.
cellStats aggregates accross an entire raster. Use it to figure out the proportion of
nlcd pixels that are covered by deciduous forest (value = 41).
> plot(counties_md$geometry) > overlay <- st_within(sesync, counties_md) > counties_sesync <- counties_md[overlay[], 'geometry'] > plot(counties_sesync, col = "red", add = TRUE) > plot(sesync, col = 'green', pch = 20, add = TRUE)
> bubble_md <- st_buffer(state_md, 5000) > plot(state_md) > plot(bubble_md, lty = 'dotted', add = TRUE)
> cellStats(nlcd == 41, "mean")
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