GormAnalysisSpatial Data Analysis in R
In this tutorial you will learn how to
- define geometries (points, lines, polygons)
- plot those geometries
- execute spatial joins (which points are contained in a polygon?)
- get the distance between a set of points
- do all of the above within the context of geospatial data (e.g. cities, roads, counties)
Important This tutorial is based on sf version 0.5-3 and ggplot2 version 2.2.1.900.
The Basics
To get started we need to learn how to define and operate on abstract geometries without a coordinate reference system (CRS). In other words, we need to learn how to deal with points, lines and polygons on your vanilla XY plane as opposed to longitude and latitude coordinates on a globe. To do this, we will make heavy use of the Simple Feature (sf) package from Edzer Pebesma.
Simple Feature Geometries (sfg)
We can define a point with the coordinates (x,y) = (1,1) as follows
pt1 <- st_point(c(1,1))
pt1
We could also include a Z coordinate by doing
st_point(c(1,1,1))
and/or an M coordinate by doing
st_point(c(1,1,1,20))
st_point(c(1,1,20), dim = "XYM")
Here X, Y, and Z represent spatial coordinates while M represents an associated measurement such as time.
Inspecting the class of pt1 we see
class(pt1)
## [1] "XY" "POINT" "sfg"
Our point object inherits from multiple classes – “XY”, “POINT”, and “sfg”. Here “sfg” stands for Simple Feature Geometry. We can build other Simple Feature Geometries such as
LINESTRING
line1 <- st_linestring(x = rbind(c(4,1), c(5,2), c(6,2)))
line1
class(line1)
## [1] "XY" "LINESTRING" "sfg"
plot(line1)
POLYGON
poly1 <- st_polygon(x = list(rbind(c(1,1), c(3,1), c(3,2), c(1,2), c(.5,.5), c(1,1))))
poly1
class(poly1)
## [1] "XY" "POLYGON" "sfg"
plot(poly1)
POLYGON (with holes)
poly2 <- st_polygon(x = list(
# outer polygon
rbind(c(1,3), c(3,3), c(3,4), c(1,4), c(.5,2.5), c(1,3)),
# hole1
rbind(c(1.5, 3.5), c(1.7, 3.5), c(1.7, 3.7), c(1.5, 3.7), c(1.5, 3.5)),
# hole2
rbind(c(2.5, 3.5), c(2.7, 3.5), c(2.7, 3.7), c(2.5, 3.5))
))
poly2
class(poly2)
## [1] "XY" "POLYGON" "sfg"
plot(poly2)
You can also define a Simple Feature Geometry that includes multiple points, linestrings, polygons, or a mixture of all three.
MULTIPOINT
multipt <- st_multipoint(rbind(c(1,1), c(2,2), c(1.5,1.5), c(2,1)))
multipt
class(multipt)
## [1] "XY" "MULTIPOINT" "sfg"
plot(multipt)
GEOMETRYCOLLECTION (a point, line, and polygon)
geomcoll <- st_geometrycollection(x = list(pt1, line1, poly2))
class(geomcoll)
## [1] "XY" "GEOMETRYCOLLECTION" "sfg"
plot(geomcoll)
Notice that printing the objects returns a description of its coordinates. This textual description is called Well Known Text (WKT).
We can do lots of cool things with geometries like
create new point which is pt1 shifted one unit up
pt1 + c(0, 1)
get the area of polygon1
st_area(poly1)
## [1] 2.25
determine if pt1 lies inside poly2
st_within(pt1, poly1, sparse = F)
## [,1]
## [1,] FALSE
determine the distance between two points
st_distance(st_point(c(1,1)), st_point(c(2,2)))
## [,1]
## [1,] 1.414214
Simple Feature List-Column (sfc)
Suppose we shoot bullets at a target. We’ll want to analyze where each bullet hit the target. For this we can use a Simple Feature List-Column (sfc). For example
shot1 <- st_point(c(0.4, 0.3))
shot2 <- st_point(c(0.8, 0.1))
shot3 <- st_point(c(0.6, 0.55))
shotsSFC <- st_sfc(shot1, shot2, shot3)
shotsSFC
## Geometry set for 3 features
## geometry type: POINT
## dimension: XY
## bbox: xmin: 0.4 ymin: 0.1 xmax: 0.8 ymax: 0.55
## CRS: NA
class(shotsSFC)
## [1] "sfc_POINT" "sfc"
plot(shotsSFC)
Now we can do cool stuff like,
get the distance between every pair of bullets
st_distance(shotsSFC)
## [,1] [,2] [,3]
## [1,] 0.0000000 0.4472136 0.3201562
## [2,] 0.4472136 0.0000000 0.4924429
## [3,] 0.3201562 0.4924429 0.0000000
determine the minimum bounding box that contains all points
st_bbox(shotsSFC)
## xmin ymin xmax ymax
## 0.40 0.10 0.80 0.55
You might be wondering why we created a Simple Feature List-Column instead of a MULTIPOINT object. The reason is because MULTIPOINT is meant to represent one “thing” which happens to have multiple points. In this case, we can consider each shot to be a separate “thing”, so it make sense to represent them as multiple POINT objects within a Simple Feature List-Column. To make this more clear, suppose we use a shotgun (which sprays many beads for each shot) instead of a rifle (one bead per shot). Here we’d represent each shot as a MULTIPOINT, but still organize the collection of shots together as a Simple Feature List-Column.
shot1 <- st_multipoint(rbind(c(0.4, 0.3), c(0.45, 0.32), c(0.42, 0.35)))
shot2 <- st_multipoint(rbind(c(0.8, 0.1)))
shot3 <- st_multipoint(rbind(c(0.93, 0.55), c(0.97, 0.52), c(0.95, 0.48), c(1.05, 0.29)))
shotsSFC <- st_sfc(shot1, shot2, shot3)
shotsSFC
## Geometry set for 3 features
## geometry type: MULTIPOINT
## dimension: XY
## bbox: xmin: 0.4 ymin: 0.1 xmax: 1.05 ymax: 0.55
## CRS: NA
class(shotsSFC)
## [1] "sfc_MULTIPOINT" "sfc"
plot(shotsSFC)
Simple Feature (sf)
The next layer up from Simple Feature List-Column is the Simple Feature class. Consider our example of shooting a gun at target. With each shot there is associated data we’d like to retain. For example, the shot number and the type of gun used. ShotNumber and GunType are the features associated with the points representing the bullet locations. This concept of having a geometry associated with a set of features is the underlying concept behind the Simple Feature class type.
shotsDF <- data.frame(
ShotNumber = c(1, 2, 3, 4, 5),
GunType = c("rifle", "rifle", "shotgun", "shotgun", "shotgun")
)
shot1 <- st_point(c(0.2, 0.75))
shot2 <- st_point(c(1.1, 0.9))
shot3 <- st_multipoint(rbind(c(0.4, 0.3), c(0.45, 0.32), c(0.42, 0.35)))
shot4 <- st_multipoint(rbind(c(0.8, 0.1)))
shot5 <- st_multipoint(rbind(c(0.93, 0.55), c(0.97, 0.52), c(0.95, 0.48), c(1.05, 0.48)))
shotsSFG <- st_sfc(shot1, shot2, shot3, shot4, shot5)
shotsSF <- st_sf(shotsDF, shotsSFG)
shotsSF
## Simple feature collection with 5 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 0.2 ymin: 0.1 xmax: 1.1 ymax: 0.9
## CRS: NA
## ShotNumber GunType shotsSFG
## 1 1 rifle POINT (0.2 0.75)
## 2 2 rifle POINT (1.1 0.9)
## 3 3 shotgun MULTIPOINT ((0.4 0.3), (0.4...
## 4 4 shotgun MULTIPOINT ((0.8 0.1))
## 5 5 shotgun MULTIPOINT ((0.93 0.55), (0...
class(shotsSF)
## [1] "sf" "data.frame"
plot(shotsSF)
Notice that when we print shotsSF it looks much like a data.frame. In fact, class(shotsSF) returns “sf”, “data.frame” meaning shotsSF is an extension of a data.frame with additional properties defined for Simple Feature objects. The upshot of this is that we can use it much like we use a normal data.frame
subset rows
shotsSF[1:3,]
## Simple feature collection with 3 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 0.2 ymin: 0.3 xmax: 1.1 ymax: 0.9
## CRS: NA
## ShotNumber GunType shotsSFG
## 1 1 rifle POINT (0.2 0.75)
## 2 2 rifle POINT (1.1 0.9)
## 3 3 shotgun MULTIPOINT ((0.4 0.3), (0.4...
combine rows
rbind(shotsSF, shotsSF)
## Simple feature collection with 10 features and 2 fields
## geometry type: GEOMETRY
## dimension: XY
## bbox: xmin: 0.2 ymin: 0.1 xmax: 1.1 ymax: 0.9
## CRS: NA
## ShotNumber GunType shotsSFG
## 1 1 rifle POINT (0.2 0.75)
## 2 2 rifle POINT (1.1 0.9)
## 3 3 shotgun MULTIPOINT ((0.4 0.3), (0.4...
## 4 4 shotgun MULTIPOINT ((0.8 0.1))
## 5 5 shotgun MULTIPOINT ((0.93 0.55), (0...
## 6 1 rifle POINT (0.2 0.75)
## 7 2 rifle POINT (1.1 0.9)
## 8 3 shotgun MULTIPOINT ((0.4 0.3), (0.4...
## 9 4 shotgun MULTIPOINT ((0.8 0.1))
## 10 5 shotgun MULTIPOINT ((0.93 0.55), (0...
reference columns
shotsSF$GunType
## [1] "rifle" "rifle" "shotgun" "shotgun" "shotgun"
Simple Feature is the class type you’ll use most frequently when working with spatial data. A major benefit to this is that ggplot2 has a native geom_sf() method for plotting your Simple Feature objects. For example
library(ggplot2)
mypoints <- data.frame(x = c(1,2,3), y = c(1,2,3), foo = c("a", "b", "a"))
mypoints <- st_as_sf(mypoints, coords = c("x", "y"))
ggplot()+
geom_sf(data = mypoints, aes(color = foo))+
labs(title = "My simple plot")+
theme_bw()
Here we define a square target to illustrate some of the cool things you can do with sf objects.
target <- st_polygon(list(rbind(c(0,0), c(1,0), c(1,1), c(0,1), c(0,0))))
plot(target)
plot(shotsSF, add = T, col = shotsSF$ShotNumber)
Which shots hit the target at least a little bit?
st_intersects(target, shotsSF, sparse = F)
## [,1] [,2] [,3] [,4] [,5]
## [1,] TRUE FALSE TRUE TRUE TRUE
Which shots landed entirely within the target?
st_contains(target, shotsSF, sparse = F)
## [,1] [,2] [,3] [,4] [,5]
## [1,] TRUE FALSE TRUE TRUE FALSE
For each shot, how close was the center of the target to the nearest bead?
st_distance(shotsSF, st_centroid(target))
## [,1]
## [1,] 0.3905125
## [2,] 0.7211103
## [3,] 0.1700000
## [4,] 0.5000000
## [5,] 0.4328972
For each shot, how close was the center of the target to the center of the beads?
st_distance(st_centroid(shotsSF), st_centroid(target))
## [,1]
## [1,] 0.3905125
## [2,] 0.7211103
## [3,] 0.1925848
## [4,] 0.5000000
## [5,] 0.4750592
Data with a Coordinate Reference System (CRS)
For geospatial data, we can mimmick the concepts above but we’ll need to include something extra – a Coordinate Reference System (CRS). The CRS will inform sf to treat x and y coordinates as longitude and latitude, and help with operations like plotting and measuring the distance between points. CRS is a deep, technical topic and I won’t pretend to be an expert on it. If you’re interested in understanding it better, I’ll defer you to the Wikipedia article for Spatial Reference Systems.
Geospatial data is typically stored as a “shape file” which is really a collection of files that store information about the data. As an example, the sf package comes with a shape file representing the counties in North Carolina. You can see them by navigating to
system.file("shape", package = "sf") # For me, /Library/Frameworks/R.framework/Versions/3.4/Resources/library/sf/shape
## [1] "/usr/local/Cellar/r/4.0.0/lib/R/library/sf/shape"
list.files(system.file("shape", package = "sf"))
## [1] "nc.dbf" "nc.prj"
## [3] "nc.shp" "nc.shx"
## [5] "olinda1.dbf" "olinda1.prj"
## [7] "olinda1.shp" "olinda1.shx"
## [9] "storms_xyz_feature.dbf" "storms_xyz_feature.shp"
## [11] "storms_xyz_feature.shx" "storms_xyz.dbf"
## [13] "storms_xyz.shp" "storms_xyz.shx"
## [15] "storms_xyzm_feature.dbf" "storms_xyzm_feature.shp"
## [17] "storms_xyzm_feature.shx" "storms_xyzm.dbf"
## [19] "storms_xyzm.shp" "storms_xyzm.shx"
To load a shape file, we can do
nc <- st_read(system.file("shape/nc.shp", package = "sf"))
## Reading layer `nc' from data source `/usr/local/Cellar/r/4.0.0/lib/R/library/sf/shape/nc.shp' using driver `ESRI Shapefile'
## Simple feature collection with 100 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## CRS: 4267
nc
## Simple feature collection with 100 features and 14 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
## CRS: 4267
## First 10 features:
## AREA PERIMETER CNTY_ CNTY_ID NAME FIPS FIPSNO CRESS_ID BIR74 SID74
## 1 0.114 1.442 1825 1825 Ashe 37009 37009 5 1091 1
## 2 0.061 1.231 1827 1827 Alleghany 37005 37005 3 487 0
## 3 0.143 1.630 1828 1828 Surry 37171 37171 86 3188 5
## 4 0.070 2.968 1831 1831 Currituck 37053 37053 27 508 1
## 5 0.153 2.206 1832 1832 Northampton 37131 37131 66 1421 9
## 6 0.097 1.670 1833 1833 Hertford 37091 37091 46 1452 7
## 7 0.062 1.547 1834 1834 Camden 37029 37029 15 286 0
## 8 0.091 1.284 1835 1835 Gates 37073 37073 37 420 0
## 9 0.118 1.421 1836 1836 Warren 37185 37185 93 968 4
## 10 0.124 1.428 1837 1837 Stokes 37169 37169 85 1612 1
## NWBIR74 BIR79 SID79 NWBIR79 geometry
## 1 10 1364 0 19 MULTIPOLYGON (((-81.47276 3...
## 2 10 542 3 12 MULTIPOLYGON (((-81.23989 3...
## 3 208 3616 6 260 MULTIPOLYGON (((-80.45634 3...
## 4 123 830 2 145 MULTIPOLYGON (((-76.00897 3...
## 5 1066 1606 3 1197 MULTIPOLYGON (((-77.21767 3...
## 6 954 1838 5 1237 MULTIPOLYGON (((-76.74506 3...
## 7 115 350 2 139 MULTIPOLYGON (((-76.00897 3...
## 8 254 594 2 371 MULTIPOLYGON (((-76.56251 3...
## 9 748 1190 2 844 MULTIPOLYGON (((-78.30876 3...
## 10 160 2038 5 176 MULTIPOLYGON (((-80.02567 3...
Notice that the resulting object is a Simple Feature where each row in the data.frame represents a county, and the geometry used to represent counties is a MULTIPOLYGON. This is because some counties in NC have disjoint sections broken apart by water.
Heres a plot of the data
ggplot()+geom_sf(data = nc)+theme_bw()
Also take note of the CRS (SRID 4267).
st_crs(nc)
## Coordinate Reference System:
## User input: 4267
## wkt:
## GEOGCS["NAD27",
## DATUM["North_American_Datum_1927",
## SPHEROID["Clarke 1866",6378206.4,294.9786982138982,
## AUTHORITY["EPSG","7008"]],
## AUTHORITY["EPSG","6267"]],
## PRIMEM["Greenwich",0,
## AUTHORITY["EPSG","8901"]],
## UNIT["degree",0.0174532925199433,
## AUTHORITY["EPSG","9122"]],
## AUTHORITY["EPSG","4267"]]
Suppose we want to plot a few cities on the map. First we can build a table of some major cities in NC
cities <- data.frame(
City = c("Charlotte", "Raleigh", "Greensboro"),
Population = c(842051, 458880, 287027),
lon = c(-80.8431, -78.6382, -79.7920),
lat = c(35.2271, 35.7796, 36.0726)
)
Convert to sf object
citiesSF <- st_as_sf(cities, coords = c("lon", "lat"), crs = 4267)
Plot North Carolina counties with major cities overlaid
ggplot()+
geom_sf(data = nc)+
geom_sf(data = citiesSF, aes(size = Population), color = "red")+
theme_bw()
For more details, be sure to check out the sf vignettes, starting with this one.