# Chapter 8 Geometric operations with vector layers

*Last updated: 2020-09-28 17:46:01 *

## Aims

Our aims in this chapter are:

- Join by location and by attributes
- Make geometric calculations between vector layers

We will use the following R packages:

`sf`

`units`

## 8.1 Join by location

For most of the examples in this Chapter, we are going to use the US counties and three airports layers (Section 7.7.1):

and transform them to the US National Atlas projection (Section 7.10):

Join by spatial location, or **spatial join**, is one of the most common operations in spatial analysis. In a spatial join we are “attaching” attributes from one layer to another based on their spatial relations. In ArcGIS this is done using “Join data from another layer based on spatial location” Figures 8.1–8.2.

For simplicity, we will create a subset of the counties of New Mexico only, since all three airports are located in that state:

Figure 8.3 shows the geometry of the `nm`

and `airports`

layers:

Note that the `pch`

parameter determines point shape (see `?points`

), while `cex`

determines point size.

Given two layers `x`

and `y`

, a function call of the form `st_join(x, y)`

returns the `x`

layer along with *matching attributes* from `y`

. The default join type is by **intersection**, other options can be specified using the `join`

parameter (see `?st_join`

).

For example, the following expression joins the `airports`

layer with the matching `nm`

attributes. The resulting layer contains all three airports with their original geometry and `name`

attribute, plus the `NAME_1`

, `NAME_2`

, `TYPE_2`

and `FIPS`

of the `nm`

feature that each airport intersects with:

```
st_join(airports, nm)
## Simple feature collection with 3 features and 5 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: -619184.5 ymin: -1081605 xmax: -551689.8 ymax: -1022366
## projected CRS: US National Atlas Equal Area
## name NAME_1 NAME_2 TYPE_2 FIPS
## 1 Albuquerque International New Mexico Bernalillo County 35001
## 2 Double Eagle II New Mexico Bernalillo County 35001
## 3 Santa Fe Municipal New Mexico Santa Fe County 35049
## geometry
## 1 POINT (-603868.2 -1081605)
## 2 POINT (-619184.5 -1068431)
## 3 POINT (-551689.8 -1022366)
```

What do you think will happen if we reverse the order of the arguments, as in

`st_join(nm, airports)`

? How many features is the result going to have, and why?

## 8.2 Subsetting by location

We can create subsets based on intersection with another layer using the `[`

operator. An expression of the form `x[y, ]`

—where `x`

and `y`

are vector layers—returns a subset of `x`

features that *intersect* with `y`

.

For example, the following expression returns only those `nm`

counties that intersect with the `airports`

layer:

The subset is shown in light blue in Figure 8.4:

```
plot(st_geometry(nm))
plot(st_geometry(nm1), col = "lightblue", add = TRUE)
plot(st_geometry(airports), col = "red", pch = 16, cex = 2, add = TRUE)
```

What will be the result of

`airports[nm, ]`

?

What will be the result of

`nm[nm[20, ], ]`

?

## 8.3 Geometric calculations

### 8.3.1 Introduction

**Geometric operations** on vector layers can conceptually be divided into three groups, according to their output:

**Numeric**values (Section 8.3.2)—Functions that summarize geometrical properties of:**Logical**values (Section 8.3.3)—Functions that evaluate whether a certain condition holds true, regarding:- A
**single layer**—e.g., geometry is valid - A
**pair of layers**—e.g., feature A intersects feature B (Section 8.3.3)

- A
**Spatial**layers (Section 8.3.4)—Functions that create a new layer based on:

The following three sections elaborate and give examples of numeric (Section 8.3.2), logical (Section 8.3.3) and spatial (Section 8.3.4) geometric calculations on vector layers.

### 8.3.2 Numeric geometric calculations

#### 8.3.2.1 Introduction

There are several functions to calculate numeric geometric properties of vector layers in package `sf`

:

The first four are operation applicable for individual layers, while `st_distance`

is applicable to *pairs* of layers. The next two sections give an example from each category: `st_area`

for individual layers (Section 8.3.2.2) and `st_distance`

for pairs of layers (Section 8.3.2.3).

#### 8.3.2.2 Area

The `st_area`

function returns the **area** of each feature or geometry. For example, we can use `st_area`

to calculate the area of each feature in the `county`

layer (i.e., each county):

```
county$area = st_area(county)
county$area[1:6]
## Units: [m^2]
## [1] 2451875694 1941109814 1077788608 1350475635 16416571545 2626706989
```

The result is an object of class `units`

, which is basically a numeric vector associated with the **units** of measurement:

**CRS units** (e.g., meters) are used by default in length, area and distance calculations. For example, the units of measurement in the US National Atlas CRS are *meters*, which is why the area values are returned in \(m^2\) units.

We can convert `units`

objects to different units with `set_units`

from package `units`

(run `valid_udunits()`

to see the list of available units). For example, here is how we can convert the `area`

column from \(m^2\) to \(km^2\) units:

```
library(units)
county$area = set_units(county$area, "km^2")
county$area[1:6]
## Units: [km^2]
## [1] 2451.876 1941.110 1077.789 1350.476 16416.572 2626.707
```

Dealing with `units`

, rather than `numeric`

values has several advantages:

- We don’t need to remember, or somehow encode, the units of measurement (e.g., name the column
`area_m2`

) since they are already binded with the values. - We don’t need to remember the coefficients for conversion between one unit to another and avoid making mistakes in those conversions.
- We can keep track of the units when making numeric calculations. For example, if we divide a numeric value by \(km^2\), the result is going to be associated with units of density: \(km^{-2}\) (Section 8.5).

If necessary, however, we can always convert `units`

to an ordinary `numeric`

vector with `as.numeric`

:

The calculated county `area`

attribute values are visualized in Figure 8.5:

#### 8.3.2.3 Distance

An example of a numeric operator on a *pair* of geometries is **geographical distance**. Distances can be calculated using function `st_distance`

. For example, the following expression calculates the distance between each feature in `airports`

and each feature in `nm`

:

The result is a `matrix`

of `units`

values:

```
d[, 1:6]
## Units: [m]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 266778.9 140470.5 103328.22 140913.62 175353.34 121934.94
## [2,] 275925.0 120879.9 93753.25 141511.80 177624.40 125327.45
## [3,] 197540.4 145580.9 34956.53 62231.01 96549.17 43630.68
```

In the distance matrix resulting from `st_distance(x, y)`

:

**Rows**refer to features of`x`

, e.g.,`airports`

**Columns**refer to features of`y`

, e.g.,`nm`

Therefore, the matrix dimensions are equal to `c(nrow(x), nrow(y))`

:

Just like areas (Section 8.3.2.2), distances can be converted to different units with `set_units`

. For example, the following expression converts the distance matrix `d`

from \(m\) to \(km\):

```
d = set_units(d, "km")
d[, 1:6]
## Units: [km]
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 266.7789 140.4705 103.32822 140.91362 175.35334 121.93494
## [2,] 275.9250 120.8799 93.75325 141.51180 177.62440 125.32745
## [3,] 197.5404 145.5809 34.95653 62.23101 96.54917 43.63068
```

To work with the distance `matrix`

, it can be convenient to set row and column names. We can use any of the unique attributes of `x`

and `y`

, such as airport names and county names:

```
rownames(d) = airports$name
colnames(d) = nm$NAME_2
d[, 1:6]
## Units: [km]
## Union San Juan Rio Arriba Taos Colfax
## Albuquerque International 266.7789 140.4705 103.32822 140.91362 175.35334
## Double Eagle II 275.9250 120.8799 93.75325 141.51180 177.62440
## Santa Fe Municipal 197.5404 145.5809 34.95653 62.23101 96.54917
## Mora
## Albuquerque International 121.93494
## Double Eagle II 125.32745
## Santa Fe Municipal 43.63068
```

Once row and column names are set, it is more convenient to find out the distance between a *specific* airport and a *specific* county:

### 8.3.3 Logical geometric calculations

Given two layers, `x`

and `y`

, the following logical geometric functions check whether each feature in `x`

maintains the specified **relation** with each feature in `y`

:

`st_intersects`

`st_disjoint`

`st_touches`

`st_crosses`

`st_within`

`st_contains`

`st_overlaps`

`st_covers`

`st_covered_by`

`st_equals`

`st_equals_exact`

The definitions of the relations evaluated by each of those functions can be found in the Spatial Predicates entry in Wikipedia.

When specifying `sparse=FALSE`

the functions return a logical `matrix`

. Each element `i,j`

in the matrix is `TRUE`

when `f(x[i], y[j])`

is `TRUE`

. For example, the following expression creates a matrix of *intersection* relations between counties in `nm`

:

```
int = st_intersects(nm, nm, sparse = FALSE)
int[1:4, 1:4]
## [,1] [,2] [,3] [,4]
## [1,] TRUE FALSE FALSE FALSE
## [2,] FALSE TRUE TRUE FALSE
## [3,] FALSE TRUE TRUE TRUE
## [4,] FALSE FALSE TRUE TRUE
```

Figure 8.6 shows two matrices of spatial relations between counties in New Mexico: `st_intersects`

(left) and `st_touches`

(right).

How can we calculate

`airports`

count per county in`nm`

, using`st_intersects`

(Figure 8.7)?

### 8.3.4 Spatial geometric calculations

#### 8.3.4.1 Introduction

The `sf`

package provides common **geometry-generating** functions applicable to *individual* geometries, such as:

`st_centroid`

—Section 8.3.4.2`st_buffer`

—Section 8.3.4.5`st_convex_hull`

—Section 8.3.4.7`st_voronoi`

`st_sample`

What each function does is illustrated in Figure 8.8.

Additionally, the `st_cast`

function is used to transform geometries from one type to another (Figure 7.8), which can also be considred as geometry generation (Section 8.3.4.4). The `st_union`

and `st_combine`

functions are used to combine multiple geometries into one, possibly dissolving their borders (Section 8.3.4.3).

Finally, the geometry-generating functions operate on *pairs* of geometries (Section 8.3.4.6) include:

`st_intersection`

`st_difference`

`st_sym_difference`

`st_union`

We elaborate on these functions in the following Sections 8.3.4.2–8.3.4.7.

#### 8.3.4.2 Centroids

For example, the following expression uses `st_centroid`

to create a layer of county **centroids**:

The `nm`

layer along with the calculated centroids is shown in Figure 8.9:

#### 8.3.4.3 Combining and dissolving

The `st_combine`

and `st_union`

functions can be used to combine multiple geometries into a single one. The difference is that `st_combine`

only combines the geometries, while `st_union`

also dissolves internal borders.

To understand what each function does, let’s examine the result of applying them on `nm`

:

```
st_combine(nm)
## Geometry set for 1 feature
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -863763.9 ymin: -1478601 xmax: -267074.1 ymax: -845634.3
## projected CRS: US National Atlas Equal Area
## MULTIPOLYGON (((-267074.1 -884175.1, -267309.2 ...
```

```
st_union(nm)
## Geometry set for 1 feature
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -863763.9 ymin: -1478601 xmax: -267074.1 ymax: -845634.3
## projected CRS: US National Atlas Equal Area
## POLYGON ((-852411.9 -1350402, -850603.1 -133127...
```

The results are plotted in Figure 8.10:

What is the class of the resulting objects? Why? What happened to the original

`nm`

attributes?

Why did

`st_combine`

return a`MULTIPOLYGON`

, while`st_union`

returned`POLYGON`

?

Why do you think there are internal “patches” in the result of

`st_union(nm)`

(Figure 8.10)?

Let’s solve a geometric question to practice some of the functions we learned so far. What is the distance between the centroids of California and New Jersey, in kilometers? Using `[`

, `st_union`

, `st_centroid`

and `st_distance`

, we can calculate it as follows:

```
# Subsetting
ca = county[county$NAME_1 == "California", ]
nj = county[county$NAME_1 == "New Jersey", ]
# Calculating centroids
ca_ctr = st_centroid(st_union(ca))
nj_ctr = st_centroid(st_union(nj))
# Calculating distance
d = st_distance(ca_ctr, nj_ctr)
# Converting to kilometers
set_units(d, "km")
## Units: [km]
## [,1]
## [1,] 3846.64
```

Note that we are using `st_union`

to combine the *county* polygons into a single geometry, so that we can calculate the centroid of the *state* polygon, as a whole.

We can use the `c`

function to combine two geometry columns into one (long) geometry column, which is very similar to the way `c`

works on vectors:

```
p = c(ca_ctr, nj_ctr)
p
## Geometry set for 2 features
## geometry type: POINT
## dimension: XY
## bbox: xmin: -1707694 ymin: -667480.7 xmax: 2111204 ymax: -206332.8
## projected CRS: US National Atlas Equal Area
## POINT (-1707694 -667480.7)
## POINT (2111204 -206332.8)
```

The centroids we calculated can be displayed as follows (Figure 8.11):

How can we draw a line line between the centroids, corresponding to the distance we just calculated (Figure 8.12)?

First, we can use `st_combine`

to transform the points into a single `MULTIPOINT`

geometry. Remember, the `st_combine`

function is similar to `st_union`

, but only combines and *does not dissolve*.

```
p = st_combine(p)
p
## Geometry set for 1 feature
## geometry type: MULTIPOINT
## dimension: XY
## bbox: xmin: -1707694 ymin: -667480.7 xmax: 2111204 ymax: -206332.8
## projected CRS: US National Atlas Equal Area
## MULTIPOINT ((-1707694 -667480.7), (2111204 -206...
```

Do you think the results of

`st_union(p)`

and`st_combine(p)`

will be different, and if so in what way?

What is left to be done is to transform the `MULTIPOINT`

geometry to a `LINESTRING`

geometry, which is what we learn next (Section 8.3.4.4).

#### 8.3.4.4 Geometry casting

The `st_cast`

function can be used to convert between different **geometry types**. The `st_cast`

function accepts:

- The
**input layer** - The destination
**geometry type**

For example, we can use `st_cast`

to convert our `MULTIPOINT`

geometry `p`

to a `LINESTRING`

geometry `l`

:

```
l = st_cast(p, "LINESTRING")
l
## Geometry set for 1 feature
## geometry type: LINESTRING
## dimension: XY
## bbox: xmin: -1707694 ymin: -667480.7 xmax: 2111204 ymax: -206332.8
## projected CRS: US National Atlas Equal Area
## LINESTRING (-1707694 -667480.7, 2111204 -206332.8)
```

Each `MULTIPOINT`

geometry is replaced with a `LINESTRING`

geometry, comprising a line that goes through all points from first to last. In this case, `p`

contains just one `MULTIPOINT`

geometry that contains two points. Therefore, the resulting geometry column `l`

contains one `LINESTRING`

geometry with a straight line.

The line we calculated is shown in Figure 8.12:

```
plot(st_geometry(county), border = "grey")
plot(st_geometry(ca_ctr), col = "red", pch = 16, cex = 2, add = TRUE)
plot(st_geometry(nj_ctr), col = "red", pch = 16, cex = 2, add = TRUE)
plot(l, col = "red", add = TRUE)
```

It is important to note that note every conversion operation is possible. Meaningless `st_cast`

operations will fail with an error message. For example, a `LINESTRING`

composed of just two points cannot be converted to a `POLYGON`

:

#### 8.3.4.5 Buffers

Another example of a function that generates now geometries based on individual input geometries is `st_buffer`

, which calculates **buffers**.

For example, here is how we can calculate 100 \(km\) buffers around the `airports`

. Note that the `st_buffer`

requires buffer distance (`dist`

), which can be passed as `numeric`

(in which case CRS units are assumed) or as `units`

:

The buffered layer is shown in Figure 8.13:

#### 8.3.4.6 Geometry generation from pairs

There are four geometry-generating functions that work on *pairs* of input geometries:

`st_intersection`

`st_difference`

`st_sym_difference`

`st_union`

Figure 8.14 shows what each of the four functions does.

For example, suppose that we need to calculate the area that is within 100 \(km\) range of *all* three airports at the same time? We can find the area of intersection of the three airports, using `st_intersection`

:

```
inter1 = st_intersection(airports_100[1, ], airports_100[2, ])
inter2 = st_intersection(inter1, airports_100[3, ])
```

The result is shown in Figure 8.15:

```
plot(st_geometry(nm))
plot(st_geometry(airports_100), add = TRUE)
plot(inter2, col = "lightblue", add = TRUE)
```

How can we calculate the area that is at within 100 \(km\) range of *at least one* of the three airports? As shown in Section 8.3.4.3, `st_union`

can be applied on an individual layer, in which case it returns the (dissolved) union of all geometries:

The result is shown in Figure 8.16:

#### 8.3.4.7 Convex hull

The **Convex Hull** of a set \(X\) of points is the smallest convex set that contains \(X\). The convex hull may be visualized as the shape resulting when stretching a rubber band around \(X\), then releasing it until it shrinks to minimal size (Figure 8.17).

For example, the following expression calculates the convex hull (per feature) in `nm1`

:

The resulting layer is shown in red in Figure 8.18:

How can we calculate the convex hull of

allpolygons in`nm1`

(Figure 8.19)?

Here is another question to practice the geometric operations we learned in this chapter. Suppose we build a tunnel, 10 \(km\) wide, between the centroids of `"Harding"`

and `"Sierra"`

counties in New Mexico. Which counties does the tunnel go through? The following code gives the result. Take a moment to go over it and make sure you understand what happens in each step:

```
w = nm[nm$NAME_2 %in% c("Harding", "Sierra"), ]
w_ctr = st_centroid(w)
w_ctr_buf = st_buffer(w_ctr, dist = 5000)
w_ctr_buf_u = st_combine(w_ctr_buf)
w_ctr_buf_u_ch = st_convex_hull(w_ctr_buf_u)
nm_w = nm[w_ctr_buf_u_ch, ]
nm_w$NAME_2
## [1] "Harding" "San Miguel" "Guadalupe" "Torrance" "Socorro"
## [6] "Lincoln" "Sierra"
```

Figure 8.20 shows the calculated tunnel geometry and highlights the counties that the tunnel goes through. We also use the `text`

function to draw text labels. The function accepts the `matrix`

of coordinates where labels should be drawn (e.g., county centroid coordinates) and the corresponding `character`

vector of lables (e.g., county names):

```
plot(st_geometry(nm_w), border = NA, col = "lightblue")
plot(st_geometry(nm), add = TRUE)
plot(st_geometry(w_ctr_buf_u_ch), add = TRUE)
text(st_coordinates(st_centroid(nm_w)), nm_w$NAME_2)
```

How is the area of the “tunnel” divided between the various counties that it crosses? We can use the `st_intersection(x, y)`

to calculate all intersections between the tunnel polygon and the county polygons:

```
int = st_intersection(w_ctr_buf_u_ch, nm_w)
int
## Geometry set for 7 features
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -677075.1 ymin: -1293982 xmax: -340138.8 ymax: -1002634
## projected CRS: US National Atlas Equal Area
## First 5 geometries:
## POLYGON ((-359510.4 -1013406, -348484.4 -100391...
## POLYGON ((-430575.6 -1074562, -359510.4 -101340...
## POLYGON ((-484643 -1121091, -430575.6 -1074562,...
## POLYGON ((-545452.3 -1173421, -484643 -1121091,...
## POLYGON ((-638440.3 -1253443, -546193.4 -117405...
```

Then, we can divide the area of each tunnel *part* by the total area of the tunnel to get the proportions:

```
area = st_area(int)
area
## Units: [m^2]
## [1] 199064581 872145769 813577715 702562404 1119738156 108077571 575538789
prop = area / sum(area)
prop
## Units: [1]
## [1] 0.04533773 0.19863456 0.18529546 0.16001130 0.25502469 0.02461508 0.13108118
```

The proportions can be displayed as text labels using the `text`

function (Figure 8.21):

```
plot(st_geometry(nm_w), border = "darkgrey")
plot(int, col = hcl.colors(7, "Set3"), border = "grey", add = TRUE)
text(st_coordinates(st_centroid(int)), paste0(round(prop, 2)*100, "%"), cex = 1.5)
```

## 8.4 Vector layer aggregation

We don’t always have to dissolve *all* features—we can also dissolve by *attribute* or by *location*. To demonstrate dissolving by attribute, also known as aggregation, let’s take a subset with all counties of Arizona and Utah:

The selected counties are shown in Figure 8.22:

As shown previously (Section 8.3.4.3), we can dissolve all features into a single geometry with `st_union`

:

```
s1 = st_union(s)
s1
## Geometry set for 1 feature
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -1389076 ymin: -1470784 xmax: -757960.3 ymax: -235145.7
## projected CRS: US National Atlas Equal Area
## POLYGON ((-852411.9 -1350402, -852611 -1352512,...
```

The dissolved geometry is shown in Figure 8.23:

Aggregating/dissolving *by attributes* can be done with `aggregate`

^{41}, which accepts the following arguments:

`x`

—The`sf`

layer being dissolved`by`

—A corresponding`data.frame`

determining the groups`FUN`

—The function to be applied on each attribute in`x`

For example, the following expression dissolves the layer `s`

, summing the `area`

attribute by state:

```
s2 = aggregate(x = s[, "area"], by = st_drop_geometry(s[, "NAME_1"]), FUN = sum)
s2
## Simple feature collection with 2 features and 2 fields
## Attribute-geometry relationship: 0 constant, 1 aggregate, 1 identity
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -1389076 ymin: -1470784 xmax: -757960.3 ymax: -235145.7
## projected CRS: US National Atlas Equal Area
## NAME_1 area geometry
## 1 Arizona 295423.1 [km^2] POLYGON ((-852411.9 -135040...
## 2 Utah 219634.4 [km^2] POLYGON ((-1011317 -819521....
```

The unit of measurement is lost, so we need to reconstruct it:

```
s2$area = set_units(as.numeric(s2$area), "km^2")
s2
## Simple feature collection with 2 features and 2 fields
## Attribute-geometry relationship: 0 constant, 0 aggregate, 1 identity, 1 NA's
## geometry type: POLYGON
## dimension: XY
## bbox: xmin: -1389076 ymin: -1470784 xmax: -757960.3 ymax: -235145.7
## projected CRS: US National Atlas Equal Area
## NAME_1 area geometry
## 1 Arizona 295423.1 [km^2] POLYGON ((-852411.9 -135040...
## 2 Utah 219634.4 [km^2] POLYGON ((-1011317 -819521....
```

The result is shown in Figure 8.24:

## 8.5 Join by attributes

We can join a vector layer and a table exactly the same way as two tables, e.g., using the `merge`

function (Section 4.6). This is analogous to the “Join attributes from a table” workflow in ArcGIS (Figure 8.25).

In the next example we will join county-level demographic data, from `CO-EST2012-Alldata.csv`

, with the `county`

layer. The join will be based on the common **Federal Information Processing Standards** (FIPS) code of each county.

First, let’s read the `CO-EST2012-Alldata.csv`

file into a `data.frame`

named `dat`

:

And subset the columns we are interested in:

`STATE`

—State code`COUNTY`

—County code`CENSUS2010POP`

—Population size in 2010

```
dat = dat[, c("STATE", "COUNTY", "CENSUS2010POP")]
head(dat)
## STATE COUNTY CENSUS2010POP
## 1 1 0 4779736
## 2 1 1 54571
## 3 1 3 182265
## 4 1 5 27457
## 5 1 7 22915
## 6 1 9 57322
```

Records where `COUNTY`

code is `0`

are state sums. We need to remove the state sums and keep just the county-level records:

```
dat = dat[dat$COUNTY != 0, ]
head(dat)
## STATE COUNTY CENSUS2010POP
## 2 1 1 54571
## 3 1 3 182265
## 4 1 5 27457
## 5 1 7 22915
## 6 1 9 57322
## 7 1 11 10914
```

The FIPS code is a character value with five digits, where the first two digits are the state code and the last three digits are the county code:

To get the corresponding county FIPS codes in `dat`

, we need to:

- Standardize the state code to a
**two-digit**code - Standardize the county code to
**three-digit**code - Paste the two into a
**five-digit**FIPS code

The `formatC`

function can be used to format numeric values into different *formats*, using different “scenarios”. The *“add leading zeros”* scenario is specified using `width=n`

, where `n`

is the required number of digits, and `flag="0"`

:

```
dat$STATE = formatC(dat$STATE, width = 2, flag = "0")
dat$COUNTY = formatC(dat$COUNTY, width = 3, flag = "0")
dat$FIPS = paste0(dat$STATE, dat$COUNTY)
```

Now we have a column named `FIPS`

with exactly the same format as in the `county`

layer:

```
head(dat)
## STATE COUNTY CENSUS2010POP FIPS
## 2 01 001 54571 01001
## 3 01 003 182265 01003
## 4 01 005 27457 01005
## 5 01 007 22915 01007
## 6 01 009 57322 01009
## 7 01 011 10914 01011
```

This means we can join the `county`

layer with the `dat`

table, based on the common column named `FIPS`

:

```
county = merge(county, dat[, c("FIPS", "CENSUS2010POP")], by = "FIPS", all.x = TRUE)
county
## Simple feature collection with 3103 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -2031903 ymin: -2116922 xmax: 2516534 ymax: 732304.6
## projected CRS: US National Atlas Equal Area
## First 10 features:
## FIPS NAME_1 NAME_2 TYPE_2 area CENSUS2010POP
## 1 01001 Alabama Autauga County 1562.805 [km^2] 54571
## 2 01003 Alabama Baldwin County 4247.589 [km^2] 182265
## 3 01005 Alabama Barbour County 2330.135 [km^2] 27457
## 4 01007 Alabama Bibb County 1621.356 [km^2] 22915
## 5 01009 Alabama Blount County 1692.125 [km^2] 57322
## 6 01011 Alabama Bullock County 1623.924 [km^2] 10914
## 7 01013 Alabama Butler County 2013.066 [km^2] 20947
## 8 01015 Alabama Calhoun County 1591.091 [km^2] 118572
## 9 01017 Alabama Chambers County 1557.054 [km^2] 34215
## 10 01019 Alabama Cherokee County 1549.770 [km^2] 25989
## geometry
## 1 MULTIPOLYGON (((1225972 -12...
## 2 MULTIPOLYGON (((1200268 -15...
## 3 MULTIPOLYGON (((1408158 -13...
## 4 MULTIPOLYGON (((1207082 -12...
## 5 MULTIPOLYGON (((1260171 -11...
## 6 MULTIPOLYGON (((1370531 -12...
## 7 MULTIPOLYGON (((1280245 -13...
## 8 MULTIPOLYGON (((1313695 -11...
## 9 MULTIPOLYGON (((1374433 -12...
## 10 MULTIPOLYGON (((1333380 -10...
```

Note that we are using a left join, therefore the `county`

layer remains as is; features that do not have a matching `FIPS`

value in `dat`

will have `NA`

in the new `CENSUS2010POP`

column.

A map of the new `CENSUS2010POP`

attribute, using the default equal breaks, is shown in Figure 8.26:

Almost all counties fall into the first category, therefore the map is uniformely colored and not very informative. A map using **quantile** breaks (Figure 8.27) reveals the spatial pattern of population sizes more clearly.

Another issue with the map is that county population size also depends on county *area*, which can make the pattern misleading in case when county area sizes are variable (which they are). To safely compare the counties we need to standardize population size by county area. In other words, we need to calculate population **density**:

Note how the measurement units (\(km^{-2}\), i.e., count per \(km^2\)) for the new column are automatically determined based on the inputs:

The map of population densities per county is shown in Figure 8.28:

How many features in

`county`

did not have a matching FIPS code in`dat`

? What are their names?

How many features in

`dat`

do not have a matching FIPS code in`county`

?

We can also calculate the *average* population density in the US, by dividing the total population by the total area:

This is close to the population density in 2010 according to Wikipedia, which is `40.015`

.

Simply averaging the `density`

column gives an overestimation, because all counties get equal weight while in reality the smaller counties are more dense:

## 8.6 Recap: main data structures

Table 8.1 summarizes the main data structures we learn in this book.

Category | Class | Chapter |
---|---|---|

Vector | `numeric` , `character` , `logical` |
2 |

Date | `Date` |
3 |

Table | `data.frame` |
4 |

Matrix | `matrix` |
5 |

Array | `array` |
5 |

Raster | `stars` |
5 |

Vector layer | `sfg` , `sfc` , `sf` |
7 |

Units | `units` |
8 |

List | `list` |
11 |

Geostatistical model | `gstat` |
12 |

Variogram model | `variogramModel` |
12 |

Alternatively, the

`group_by`

and`summarize`

functions from package`dplyr`

can also be used for aggregation:`s2 = s %>% group_by(NAME_1) %>% summarize(area = sum(area))`

.↩