04-spatial-operations.Rmd

# Spatial data operations {#spatial-operations}

## Prerequisites {-}

- This chapter requires the same packages used in Chapter \@ref(attr): 

```{r 04-spatial-operations-1, message=FALSE, results='hide'}
library(sf)
library(terra)
library(dplyr)
library(spData)
```

- You also need to read in a couple of datasets as follows for Section \@ref(spatial-ras)

```{r 04-spatial-operations-1-1}
elev = rast(system.file("raster/elev.tif", package = "spData"))
grain = rast(system.file("raster/grain.tif", package = "spData"))
```


## Introduction

Spatial operations, including spatial joins between vector datasets and local and focal operations on raster datasets, are a vital part of geocomputation\index{geocomputation}.
This chapter shows how spatial objects can be modified in a multitude of ways based on their location and shape.
Many spatial operations have a non-spatial (attribute) equivalent, so concepts such as subsetting and joining datasets demonstrated in the previous chapter are applicable here.
This is especially true for *vector* operations: Section \@ref(vector-attribute-manipulation) on vector attribute manipulation provides the basis for understanding its spatial counterpart, namely spatial subsetting (covered in Section \@ref(spatial-subsetting)).
Spatial joining (Section \@ref(spatial-joining)) and aggregation (Section \@ref(spatial-aggr)) also have non-spatial counterparts, covered in the previous chapter.

Spatial operations differ from non-spatial operations in in a number of ways, however:
Spatial joins, for example, can be done in a number of ways --- including matching entities that intersect with or are within a certain distance of the target dataset --- while the attribution joins discussed in Section \@ref(vector-attribute-joining) in the previous chapter can only be done in one way (except when using fuzzy joins, as described in the documentation of the [**fuzzyjoin**](https://cran.r-project.org/package=fuzzyjoin) package).
The *type* of spatial relationship between objects must be considered when undertaking spatial operations, as described in Section \@ref(topological-relations), on topological relations between vector features.
\index{spatial operations}.
Another unique aspect of spatial objects is distance: all spatial objects are related through space, and distance calculations can be used to explore the strength of this relationship, as described in the context of vector data in Section \@ref(distance-relations).

Spatial operations on raster objects include subsetting --- covered in Section \@ref(spatial-raster-subsetting) --- and merging several raster 'tiles' into a single object, as demonstrated in Section \@ref(merging-rasters).
*Map algebra* covers a range of operations that modify raster cell values, with or without reference to surrounding cell values, is vital for many applications.
The concept of map algebra is introduced in Section \@ref(map-algebra); local, focal and zonal map algebra operations are covered in sections \@ref(local-operations), \@ref(focal-operations), and \@ref(zonal-operations), respectively. Global map algebra operations, which generate summary statistics representing an entire raster dataset, and distance calculations on rasters, are discussed in Section \@ref(global-operations-and-distances).
In the final section before the exercises (\@ref(merging-rasets)) the process of merging two raster datasets is discussed and demonstrated with reference to a reproducible example.

```{block2 04-spatial-operations-2, type='rmdnote'}
It is important to note that spatial operations that use two spatial objects rely on both objects having the same coordinate reference system, a topic that was introduced in Section \@ref(crs-intro) and which will be covered in more depth in Chapter \@ref(reproj-geo-data).
```

## Spatial operations on vector data {#spatial-vec}

This section provides an overview of spatial operations on vector geographic data represented as simple features in the **sf** package.
Section \@ref(spatial-ras) presents spatial operations on raster datasets using classes and functions from the **terra** package.

### Spatial subsetting

Spatial subsetting is the process of taking a spatial object and returning a new object containing only features that *relate* in space to another object.
Analogous to *attribute subsetting* (covered in Section \@ref(vector-attribute-subsetting)), subsets of `sf` data frames can be created with square bracket (`[`) operator using the syntax `x[y, , op = st_intersects]`, where `x` is an `sf` object from which a subset of rows will be returned, `y` is the 'subsetting object' and `, op = st_intersects` is an optional argument that specifies the topological relation (also known as the binary predicate) used to do the subsetting.
The default topological relation used when an `op` argument is not provided is `st_intersects()`: the command `x[y, ]` is identical to `x[y, , op = st_intersects]` shown above but not `x[y, , op = st_disjoint]` (the meaning of these and other topological relations is described in the next section).
The `filter()` function from the **tidyverse**\index{tidyverse (package)} can also be used but this approach more verbose, as we will see in the examples below.
\index{vector!subsetting}
\index{spatial!subsetting}

To demonstrate spatial subsetting, we will use the `nz` and `nz_height` datasets in the **spData** package, which contain geographic data on the 16 main regions and 101 highest points in New Zealand, respectively (Figure \@ref(fig:nz-subset)), in a projected coordinate system.
The following code chunk creates an object representing Canterbury, then uses spatial subsetting to return all high points in the region:

```{r 04-spatial-operations-3}
canterbury = nz %>% filter(Name == "Canterbury")
canterbury_height = nz_height[canterbury, ]
```

```{r nz-subset, echo=FALSE, warning=FALSE, fig.cap="Illustration of spatial subsetting with red triangles representing 101 high points in New Zealand, clustered near the central Canterbuy region (left). The points in Canterbury were created with the `[` subsetting operator (highlighted in gray, right).", fig.scap="Illustration of spatial subsetting.", message=FALSE}
library(tmap)
p_hpnz1 = tm_shape(nz) + tm_polygons(col = "white") +
  tm_shape(nz_height) + tm_symbols(shape = 2, col = "red", size = 0.25) +
  tm_layout(main.title = "High points in New Zealand", main.title.size = 1,
            bg.color = "lightblue")
p_hpnz2 = tm_shape(nz) + tm_polygons(col = "white") +
  tm_shape(canterbury) + tm_fill(col = "gray") + 
  tm_shape(canterbury_height) + tm_symbols(shape = 2, col = "red", size = 0.25) +
  tm_layout(main.title = "High points in Canterbury", main.title.size = 1,
            bg.color = "lightblue")
tmap_arrange(p_hpnz1, p_hpnz2, ncol = 2)
```

Like attribute subsetting, the command `x[y, ]` (equivalent to `nz_height[canterbury, ]`) subsets features of a *target* `x` using the contents of a *source* object `y`.
Instead of `y` being a vector of class `logical` or `integer`, however, for spatial subsetting both `x` and `y` must be geographic objects.
Specifically, objects used for spatial subsetting in this way must have the class `sf` or `sfc`: both `nz` and `nz_height` are geographic vector data frames and have the class `sf`, and the result of the operation returns another `sf` object representing the features in the target `nz_height` object that intersect with (in this case high points that are located within) the `canterbury` region. 

Various *topological relations* can be used for spatial subsetting which determine the type of spatial relationship that features in the target object must have with the subsetting object to be selected.
These include *touches*, *crosses* or *within*, as we will see shortly in Section \@ref(topological-relations). 
The default setting `st_intersects` is a 'catch all' topological relation that will return features in the target that *touch*, *cross* or are *within* the source 'subsetting' object.
As indicated above, alternative spatial operators can be specified with the `op =` argument, as demonstrated in the following command which returns the opposite of `st_intersects()`, points that do not intersect with Canterbury (see Section \@ref(topological-relations)):

```{r 04-spatial-operations-4, eval=FALSE}
nz_height[canterbury, , op = st_disjoint]
```

```{block2 04-spatial-operations-5, type='rmdnote'}
Note the empty argument --- denoted with `, ,` --- in the preceding code chunk is included to highlight `op`, the third argument in `[` for `sf` objects.
One can use this to change the subsetting operation in many ways.
`nz_height[canterbury, 2, op = st_disjoint]`, for example, returns the same rows but only includes the second attribute column (see `` sf:::`[.sf` `` and the `?sf` for details).
```

For many applications, this is all you'll need to know about spatial subsetting for vector data: it just works.
If you are impatient to learn about more topological relations, beyond `st_intersects()` and `st_disjoint()`, skip to the next section (\@ref(topological-relations)).
If you're interested in the details, including other ways of subsetting, read on.

Another way of doing spatial subsetting uses objects returned by topological operators.
These objects can be useful in their own right, for example when exploring the graph network of relationships between contiguous regions, but they can also be used for subsetting, as demonstrated in the code chunk below:

```{r 04-spatial-operations-6}
sel_sgbp = st_intersects(x = nz_height, y = canterbury)
class(sel_sgbp)
sel_sgbp
sel_logical = lengths(sel_sgbp) > 0
canterbury_height2 = nz_height[sel_logical, ]
```

The above code chunk creates an object of class `sgbp` (a sparse geometry binary predicate, a list of length `x` in the spatial operation) and then converts it into a logical vector `sel_logical` (containing only `TRUE` and `FALSE` values, something that can also be used by **dplyr**'s filter function).
\index{binary predicate|seealso {topological relations}}
The function `lengths()` identifies which features in `nz_height` intersect with *any* objects in `y`.
In this case 1 is the greatest possible value but for more complex operations one could use the method to subset only features that intersect with, for example, 2 or more features from the source object.

```{block2 04-spatial-operations-7, type='rmdnote'}
Note: another way to return a logical output is by setting `sparse = FALSE` (meaning 'return a dense matrix not a sparse one') in operators such as `st_intersects()`. The command `st_intersects(x = nz_height, y = canterbury, sparse = FALSE)[, 1]`, for example, would return an output identical to `sel_logical`.
Note: the solution involving `sgbp` objects is more generalisable though, as it works for many-to-many operations and has lower memory requirements.
```

The same result can be achieved with the **sf** function `st_filter()` which was [created](https://github.com/r-spatial/sf/issues/1148) to increase compatibility between `sf` objects and **dplyr** data manipulation code:

```{r}
canterbury_height3 = nz_height %>%
  st_filter(y = canterbury, .predicate = st_intersects)
```


```{r 04-spatial-operations-7b-old, eval=FALSE, echo=FALSE}
# Additional tests of subsetting
canterbury_height4 = nz_height %>%
  filter(st_intersects(x = ., y = canterbury, sparse = FALSE))
canterbury_height5 = nz_height %>%
  filter(sel_logical)
identical(canterbury_height3, canterbury_height4)
identical(canterbury_height3, canterbury_height5)
identical(canterbury_height2, canterbury_height4)
identical(canterbury_height, canterbury_height4)
waldo::compare(canterbury_height2, canterbury_height4)
```

At this point, there are three identical (in all but row names) versions of `canterbury_height`, one created using the `[` operator, one created via an intermediary selection object, and another using **sf**'s convenience function `st_filter()`.
<!-- RL: commented out for now as old. Todo: if we ever update that vignette uncomment the next line. -->
<!-- To explore spatial subsetting in more detail, see the supplementary vignettes on `subsetting` and [`tidyverse-pitfalls`](https://geocompr.github.io/geocompkg/articles/) on the [geocompkg website](https://geocompr.github.io/geocompkg/articles/). -->
The next section explores different types of spatial relation, also known as binary predicates, that can be used to identify whether or not two features are spatially related or not.

### Topological relations

Topological relations describe the spatial relationships between objects.
"Binary topological relationships", to give them their full name, are logical statements (in that the answer can only be `TRUE` or `FALSE`) about the spatial relationships between two objects defined by ordered sets of points (typically forming points, lines and polygons) in two or more dimensions [@egenhofer_mathematical_1990].
That may sound rather abstract and, indeed, the definition and classification of topological relations is based on earlier mathematical foundations first published as a book in 1966 [@spanier_algebraic_1995].^[
See @dieck_algebraic_2008 for an updated textbook teaching algebraic topology.
]

Despite their mathematical origins, topological relations can be understood intuitively with reference to visualizations of commonly used functions that test for common types of spatial relationships.
Figure \@ref(fig:relations) shows a variety of geometry pairs and their associated relations.
The third and fourth pairs in Figure \@ref(fig:relations) (from left to right and then down) demonstrate that, for some relations, order is important: while the relations *equals*, *intersects*, *crosses*, *touches* and *overlaps* are symmetrical, meaning that if `function(x, y)` is true, `function(y, x)` will also by true, relations in which the order of the geomtries are important such as *contains* and *within* are not.
\index{topological relations}

```{r relations, echo=FALSE, fig.cap="Topological relations between vector geometries, inspired by Figures 1 and 2 in Egenhofer and Herring (1990). The relations for which the function(x, y) is true are printed for each geometry pair, with x represented in pink and y represented in blue.", fig.show='hold', message=FALSE, fig.asp=0.66}
# source("https://github.com/Robinlovelace/geocompr/raw/c4-v2-updates-rl/code/de_9im.R")
source("code/de_9im.R")
library(sf)
p1 = st_sfc(st_polygon(list(rbind(c(0, 0), c(0, 1), c(1, 1), c(1, 0.5), c(0, 0)))))
p2 = st_sfc(st_polygon(list(rbind(c(0, 0), c(1, 0), c(1, 0.5), c(0, 0)))))
p3 = st_sfc(st_polygon(list(rbind(c(0, 0), c(1, 0), c(1, 0.7), c(0, 0)))))
p4 = st_sfc(st_polygon(list(rbind(c(0.7, 0.8), c(0.7, 0.5), c(0.9, 0.5), c(0.7, 0.8)))))
p5 = st_sfc(st_polygon(list(rbind(c(0.6, 0.7), c(0.7, 0.5), c(1, 0.5), c(0.6, 0.7)))))
p6 = st_sfc(st_polygon(list(rbind(c(0.1, 0), c(1, 0), c(1, 0.3), c(0.1, 0)))))
p7 = st_sfc(st_polygon(list(rbind(c(0.05, 0.4), c(0.05, 0.97), c(0.6, 0.97), c(0.5, 0.4), c(0.05, 0.4)))))

# todo: add 3 more with line/point relations?
tmap::tmap_arrange(de_9im(p1, p2), de_9im(p1, p3), de_9im(p1, p4),
                   de_9im(p7, p1), de_9im(p1, p5), de_9im(p1, p6))
```

In `sf`, functions testing for different types of topological relations are called 'binary predicates', as described in the vignette *Manipulating Simple Feature Geometries*, which can be viewed with the command [`vignette("sf3")`](https://r-spatial.github.io/sf/articles/sf3.html), and in the help page [`?geos_binary_pred`](https://r-spatial.github.io/sf/reference/geos_binary_ops.html) [@pebesma_simple_2018].
To see how topological relations work in practice, let's create a simple reproducible example, building on the relations illustrated in Figure \@ref(fig:relations) and consolidating knowledge of how vector geometries are represented from a previous chapter (Section \@ref(geometry)).
Note that to create tabular data representing coordinates (x and y) of the polygon vertices, we use the base R function `read.csv()` and then convert the result into a matrix, a `POLYGON` and finally an `sfc` object:

```{r, echo=FALSE}
# alternative way of getting reader to data, less clear...
# polygon_matrix = matrix(c(
#   0, 0.0,
#   0, 1.0,
#   1, 1.0,
#   1, 0.5,
#   0,  0.0
#   ),
#   ncol = 2,
#   byrow = TRUE
# )
```


```{r}
polygon_df = read.csv(text = "x, y
0, 0.0
0, 1.0
1, 1.0
1, 0.5
0, 0.0")
polygon_matrix = as.matrix(polygon_df)
polygon = st_polygon(list(polygon_matrix))
polygon_sfc = st_sfc(polygon)
```

We will create additional geometries to demonstrate spatial relations with the following commands which, when plotted on top of the polygon created above, relate in space to one another, as shown in Figure \@ref(fig:relation-objects).
Note the use of the function `st_as_sf()` and the argument `coords` to efficiently convert from a data frame containing columns representing coordinates to an `sf` object containing points:

```{r}
line_df = read.csv(text = "x,y
0.1, 0
1, 0.1")
line = st_linestring(x = as.matrix(line_df))
line_sfc = st_sfc(line)
# create points
point_df = read.csv(text = "x,y
0.1,0
0.7,0.2
0.4,0.8")
point_sf = st_as_sf(point_df, coords = c("x", "y"))
```

```{r relation-objects, echo=FALSE, fig.cap="Points (`point_df` 1 to 3), line and polygon objects arranged to illustrate topological relations.", fig.asp=1, out.width="50%", fig.scap="Demonstration of topological relations."}
par(pty = "s")
plot(polygon_sfc, border = "red", col = "gray", axes = TRUE)
plot(line_sfc, add = TRUE)
plot(point_sf, add = TRUE, lab = 1:4)
text(point_df[, 1] + 0.02, point_df[, 2] + 0.03, 1:3, cex = 1.3)
```

A simple query is: which of the points in `point_sf` intersect in some way with polygon `polygon_sfc`?
The question can be answered by inspection (points 1 and 2 are over or touch the triangle).
It can also be answered by using a *spatial predicate* such as *do the objects intersect*?
This is implemented in **sf** as follows:

```{r 04-spatial-operations-9, eval=FALSE}
st_intersects(point_sf, polygon_sfc)
#> Sparse geometry binary ..., where the predicate was `intersects'
#> 1: (empty)
#> 2: (empty)
#> 3: 1
```

The contents of the result should be as you expected:
the function returns a positive (`1`) for the third point, and a negative result (represented by an empty vector) for the first two which are outside the polygon's border.
What may be unexpected is that the result comes in the form of a list of vectors.
This *sparse matrix* output only registers a relation if one exists, reducing the memory requirements of topological operations on multi-feature objects.
As we saw in the previous section, a *dense matrix* consisting of `TRUE` or `FALSE` values for each combination of features can also be returned when `sparse = FALSE`:

```{r 04-spatial-operations-10}
st_intersects(point_sf, polygon_sfc, sparse = FALSE)
```

The output is a matrix in which each row represents a feature in the target object and each column represents a feature in the selecting object.
In this case, only the third feature in `point_sf` intersects with `polygon_sfc`.
There is only one feature in `polygon_sfc` so the result has only one column.
The result can be used for subsetting as we saw in Section \@ref(spatial-subsetting).

Note: `st_intersects()` returns `TRUE` even in cases where the features just touch: *intersects* is a 'catch-all' topological operation which identifies many types of spatial relation, as illustrated in Figure \@ref(fig:relate).
The opposite of `st_intersects()` is `st_disjoint()`, which returns only objects that do not spatially relate in any way to the selecting object (note `[, 1]` converts the result into a vector):

```{r 04-spatial-operations-11}
st_disjoint(point_sf, polygon_sfc, sparse = FALSE)[, 1]
```

`st_within()` returns `TRUE` only for objects that are completely within the selecting object.
This also only applies to the third point, which is inside the polygon, as illustrated below:

```{r 04-spatial-operations-12}
st_within(point_sf, polygon_sfc, sparse = FALSE)[, 1]
```

Note that although the first point is *within* the triangle, it does not *touch* any part of its border.
For this reason `st_touches()` only returns `FALSE` for all points:

```{r 04-spatial-operations-13}
st_touches(point_sf, polygon_sfc, sparse = FALSE)[, 1]
```

What about features that do not touch, but *almost touch* the selection object?
These can be selected using `st_is_within_distance()`, which has an additional `dist` argument.
It can be used to set how close target objects need to be before they are selected.
Note that although point 4 is one unit of distance from the nearest node of `polygon_sfc` (at point 2 in Figure \@ref(fig:relation-objects)), it is still selected when the distance is set to 0.9.
This is illustrated in the code chunk below, the second line of which uses the function `lengths()` to convert the lengthy list output into a `logical` object:

```{r 04-spatial-operations-14}
# can only return a sparse matrix
sel = st_is_within_distance(point_sf, polygon_sfc, dist = 0.1) 
lengths(sel) > 0
```


```{block2 04-spatial-operations-15, type='rmdnote'}
Functions for calculating topological relations use spatial indices to largely speed up spatial query performance.
They achieve that using the Sort-Tile-Recursive (STR) algorithm.
The `st_join` function, mentioned in the next section, also uses the spatial indexing. 
You can learn more at https://www.r-spatial.org/r/2017/06/22/spatial-index.html.
```

```{r 04-spatial-operations-16, eval=FALSE, echo=FALSE}
# other tests
st_overlaps(point_sf, polygon_sfc, sparse = FALSE)
st_covers(point_sf, polygon_sfc, sparse = FALSE)
st_covered_by(point_sf, polygon_sfc, sparse = FALSE)
```

```{r 04-spatial-operations-17, eval=FALSE, echo=FALSE}
st_contains(a, p[2, ], sparse = TRUE)
```

```{r 04-spatial-operations-18, eval=FALSE, echo=FALSE}
# starting simpler so commented
a1 = st_polygon(list(rbind(c(-1, -1), c(1, -1), c(1, 1), c(-1, -1))))
a2 = st_polygon(list(rbind(c(2, 0), c(2, 2), c(3, 2), c(3, 0), c(2, 0))))
a = st_sfc(a1, a2)

b1 = a1 * 0.5
b2 = a2 * 0.4 + c(1, 0.5)
b = st_sfc(b1, b2)

l1 = st_linestring(x = matrix(c(0, 3, -1, 1), , 2))
l2 = st_linestring(x = matrix(c(-1, -1, -0.5, 1), , 2))
l = st_sfc(l1, l2)

p = st_multipoint(x = matrix(c(0.5, 1, -1, 0, 1, 0.5), , 2))

plot(a, border = "red", axes = TRUE)
plot(b, border = "green", add = TRUE)
plot(l, add = TRUE)
plot(p, add = TRUE)
```

<!-- Todo:reinstate this section (RL 2021-11) -->
<!-- ### DE-9IM strings -->

<!-- Underlying the binary predicates demonstrated in the previous section is the Dimensionally Extended 9-Intersection Model (DE-9IM). -->
<!-- The model was originally labelled "DE + 9IM" by its inventors, referring to the "dimension of the intersections of' boundaries, interiors, and exteriors of two features" [@clementini_comparison_1995] -->
<!-- , but is now generally referred to as DE-9IM [@shen_2018_classification] -->
<!-- The way the model works can be demonstrated with reference to two intersecting polygons, as illustrated in Figure \@ref(fig:de-9im). -->

```{r, echo=FALSE, eval=FALSE}
b = st_sfc(st_point(c(0, 1)), st_point(c(1, 1))) # create 2 points
b = st_buffer(b, dist = 1) # convert points to circles
bsf = sf::st_sf(data.frame(Object = c("a", "b")), geometry = b)
b9 = replicate(bsf, n = 9, simplify = FALSE)
b9sf = do.call(rbind, b9)
domains = c("Interior", "Boundary", "Exterior")
b9sf$domain_a = rep(rep(domains, 3), each = 2)
b9sf$domain_b = rep(rep(domains, each = 3), each = 2)
library(ggplot2)
ggplot(b9sf) +
  geom_sf() +
  facet_grid(domain_a ~ domain_b)

plot(b9sf)
tmap_arrange(
  tm_shape(b) + tm_polygons(alpha = 0.5) + tm_layout(title = "Interior-Interior"),
  tm_shape(b) + tm_polygons(alpha = 0.5) + tm_layout(title = "Interior-Boundary"),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  tm_shape(b) + tm_polygons(alpha = 0.5),
  nrow = 3
)

plot(b)
text(x = c(-0.5, 1.5), y = 1, labels = c("x", "y")) # add text
```

### Spatial joining 

Joining two non-spatial datasets relies on a shared 'key' variable, as described in Section \@ref(vector-attribute-joining).
Spatial data joining applies the same concept, but instead relies on spatial relations, described in the previous section.
As with attribute data, joining adds new columns to the target object (the argument `x` in joining functions), from a source object (`y`).
\index{join!spatial}
\index{spatial!join}

The process is illustrated by the following example: imagine you have ten points randomly distributed across the Earth's surface and you ask, for the points that are on land, which countries are they in?
Implementing this idea in a [reproducible example](https://github.com/Robinlovelace/geocompr/blob/main/code/04-spatial-join.R) will build your geographic data handling skills and show how spatial joins work.
The starting point is to create points that are randomly scattered over the Earth's surface:

```{r 04-spatial-operations-19}
set.seed(2018) # set seed for reproducibility
(bb = st_bbox(world)) # the world's bounds
random_df = tibble(
  x = runif(n = 10, min = bb[1], max = bb[3]),
  y = runif(n = 10, min = bb[2], max = bb[4])
)
random_points = random_df %>% 
  st_as_sf(coords = c("x", "y")) %>% # set coordinates
  st_set_crs("EPSG:4326") # set geographic CRS
```

The scenario illustrated in Figure \@ref(fig:spatial-join) shows that the `random_points` object (top left) lacks attribute data, while the `world` (top right) has attributes, including country names shown for a sample of countries in the legend.
Spatial joins are implemented with `st_join()`, as illustrated in the code chunk below.
The output is the `random_joined` object which is illustrated in Figure \@ref(fig:spatial-join) (bottom left).
Before creating the joined dataset, we use spatial subsetting to create `world_random`, which contains only countries that contain random points, to verify the number of country names returned in the joined dataset should be four (see the top right panel of Figure \@ref(fig:spatial-join)).

```{r 04-spatial-operations-20, message=FALSE}
world_random = world[random_points, ]
nrow(world_random)
random_joined = st_join(random_points, world["name_long"])
```

```{r spatial-join, echo=FALSE, fig.cap="Illustration of a spatial join. A new attribute variable is added to random points (top left) from source world object (top right) resulting in the data represented in the final panel.", fig.asp=0.5, warning=FALSE, message=FALSE, out.width="100%", fig.scap="Illustration of a spatial join."}
# source("https://github.com/Robinlovelace/geocompr/raw/main/code/04-spatial-join.R")
source("code/04-spatial-join.R")
tmap_arrange(jm1, jm2, jm3, jm4, nrow = 2, ncol = 2)
```

By default, `st_join()` performs a left join, meaning that the result is an object containing all rows from `x` including rows with no match in `y` (see Section \@ref(vector-attribute-joining)), but it can also do inner joins by setting the argument `left = FALSE`.
Like spatial subsetting, the default topological operator used by `st_join()` is `st_intersects()`, which can be changed by setting the `join` argument (see `?st_join` for details).
The example above demonstrates the addition of a column from a polygon layer to a point layer, but same approach works regardless of geometry types.
In such cases, for example when `x` contains polygons, each of which match multiple objects in `y`, spatial joins will result in duplicate features, creates a new row for each match in `y`.

<!-- Idea: demonstrate what happens when there are multiple matches with reprex (low priority, RL: 2021-12) -->

### Non-overlapping joins

Sometimes two geographic datasets do not touch but still have a strong geographic relationship.
The datasets `cycle_hire` and `cycle_hire_osm`, already attached in the **spData** package, provide a good example.
Plotting them shows that they are often closely related but they do not touch, as shown in Figure \@ref(fig:cycle-hire), a base version of which is created with the following code below:
\index{join!non-overlapping}

```{r 04-spatial-operations-21, eval=FALSE}
plot(st_geometry(cycle_hire), col = "blue")
plot(st_geometry(cycle_hire_osm), add = TRUE, pch = 3, col = "red")
```

We can check if any points are the same `st_intersects()` as shown below:

```{r 04-spatial-operations-22, message=FALSE}
any(st_touches(cycle_hire, cycle_hire_osm, sparse = FALSE))
```

```{r 04-spatial-operations-23, echo=FALSE, eval=FALSE}
# included to show alternative ways of showing there's no overlap
sum(st_geometry(cycle_hire) %in% st_geometry(cycle_hire_osm))
sum(st_coordinates(cycle_hire)[, 1] %in% st_coordinates(cycle_hire_osm)[, 1])
```

```{r cycle-hire, fig.cap="The spatial distribution of cycle hire points in London based on official data (blue) and OpenStreetMap data (red).", echo=FALSE, warning=FALSE, fig.scap="The spatial distribution of cycle hire points in London."}
if (knitr::is_latex_output()){
  knitr::include_graphics("figures/cycle-hire-1.png")
} else if (knitr::is_html_output()){
  # library(tmap)
  # osm_tiles = tmaptools::read_osm(tmaptools::bb(cycle_hire, ext = 1.3), type =   "https://korona.geog.uni-heidelberg.de/tiles/roadsg/x={x}&y={y}&z={z}")
  # qtm(osm_tiles) +
    # tm_shape(cycle_hire) +
    # tm_bubbles(col = "blue", alpha = 0.5, size = 0.2) +
    # tm_shape(cycle_hire_osm) +
    # tm_bubbles(col = "red", alpha = 0.5, size = 0.2) +
    # tm_scale_bar()
  library(leaflet)
  leaflet() %>%
    # addProviderTiles(providers$OpenStreetMap.BlackAndWhite) %>%
    addCircles(data = cycle_hire) %>%
    addCircles(data = cycle_hire_osm, col = "red")  
}
```

Imagine that we need to join the `capacity` variable in `cycle_hire_osm` onto the official 'target' data contained in `cycle_hire`.
This is when a non-overlapping join is needed.
The simplest method is to use the topological operator `st_is_within_distance()`, as demonstrated below using a threshold distance of 20 m (note that this works with projected and unprojected data).

```{r 04-spatial-operations-24}
sel = st_is_within_distance(cycle_hire, cycle_hire_osm, dist = 20)
summary(lengths(sel) > 0)
```

```{r 04-spatial-operations-24-without-s2-test, eval=FALSE, echo=FALSE}
sf::sf_use_s2(FALSE)
sel = st_is_within_distance(cycle_hire, cycle_hire_osm, dist = 20)
summary(lengths(sel) > 0)
# still works: must be lwgeom or some other magic!
```


```{r 04-spatial-operations-24-projected, eval=FALSE, echo=FALSE}
# This chunk contains the non-overlapping join on projected data, a step that is no longer needed:
# Note that, before performing the relation, both objects are transformed into a projected CRS.
# These projected objects are created below (note the affix `_P`, short for projected):
cycle_hire_P = st_transform(cycle_hire, 27700)
cycle_hire_osm_P = st_transform(cycle_hire_osm, 27700)
sel = st_is_within_distance(cycle_hire_P, cycle_hire_osm_P, dist = 20)
summary(lengths(sel) > 0)
```

This shows that there are `r sum(lengths(sel) > 0)` points in the target object `cycle_hire` within the threshold distance of `cycle_hire_osm`.
How to retrieve the *values* associated with the respective `cycle_hire_osm` points?
The solution is again with `st_join()`, but with an addition `dist` argument (set to 20 m below):

```{r 04-spatial-operations-25}
z = st_join(cycle_hire, cycle_hire_osm, st_is_within_distance, dist = 20)
nrow(cycle_hire)
nrow(z)
```

Note that the number of rows in the joined result is greater than the target.
This is because some cycle hire stations in `cycle_hire` have multiple matches in `cycle_hire_osm`.
To aggregate the values for the overlapping points and return the mean, we can use the aggregation methods learned in Chapter \@ref(attr), resulting in an object with the same number of rows as the target:

```{r 04-spatial-operations-26}
z = z %>% 
  group_by(id) %>% 
  summarize(capacity = mean(capacity))
nrow(z) == nrow(cycle_hire)
```

The capacity of nearby stations can be verified by comparing a plot of the capacity of the source `cycle_hire_osm` data with the results in this new object (plots not shown):

```{r 04-spatial-operations-27, eval=FALSE}
plot(cycle_hire_osm["capacity"])
plot(z["capacity"])
```

The result of this join has used a spatial operation to change the attribute data associated with simple features; the geometry associated with each feature has remained unchanged.

### Spatial aggregation {#spatial-aggr}

As with attribute data aggregation, spatial data aggregation *condenses* data: aggregated outputs have fewer rows than non-aggregated inputs.
Statistical *aggregating functions*, such as mean average or sum, summarise multiple values \index{statistics} of a variable, and return a single value per *grouping variable*.
Section \@ref(vector-attribute-aggregation) demonstrated how `aggregate()` and `group_by() %>% summarize()` condense data based on attribute variables, this section shows how the same functions work with spatial objects.
\index{aggregation!spatial}

Returning to the example of New Zealand, imagine you want to find out the average height of high points in each region: it is the geometry of the source (`y` or `nz` in this case) that defines how values in the target object (`x` or `nz_height`) are grouped.
This can be done in a single line of code with base R's `aggregate()` method:

```{r 04-spatial-operations-28}
nz_agg = aggregate(x = nz_height, by = nz, FUN = mean)
```

The result of the previous command is an `sf` object with the same geometry as the (spatial) aggregating object (`nz`), which you can verify with the command `identical(st_geometry(nz), st_geometry(nz_agg))`.
The result of the previous operation is illustrated in Figure \@ref(fig:spatial-aggregation), which shows the average value of features in `nz_height` within each of New Zealand's 16 regions.
The same result can also be generated by piping the output from `st_join()` into the 'tidy' functions `group_by()` and `summarize()` as follows:

```{r spatial-aggregation, echo=FALSE, fig.cap="Average height of the top 101 high points across the regions of New Zealand.", fig.asp=1, message=FALSE, out.width="50%"}
library(tmap)
tm_shape(nz_agg) +
  tm_fill("elevation", breaks = seq(27, 30, by = 0.5) * 1e2) +
  tm_borders()
```

```{r 04-spatial-operations-29}
nz_agg2 = st_join(x = nz, y = nz_height) %>%
  group_by(Name) %>%
  summarize(elevation = mean(elevation, na.rm = TRUE))
```

```{r test-tidy-spatial-join, eval=FALSE, echo=FALSE}
plot(nz_agg)
plot(nz_agg2)
# aggregate looses the name of aggregating objects
```

The resulting `nz_agg` objects have the same geometry as the aggregating object `nz` but with a new column summarising the values of `x` in each region using the function `mean()` (which cold, of course, be replaced by `median()`, `sd()` and other functions that return a single value).
Note: one difference between the `aggregate()` and `group_by() %>% summarize()` approaches is that the former results in `NA` values for unmatching region names, while the latter preserves region names and is more flexible in terms of aggregating functions and the column names of the results.
For aggregating operations which also create new geometries, see Section \@ref(geometry-unions).

### Joining incongruent layers {#incongruent}

Spatial congruence\index{spatial congruence} is an important concept related to spatial aggregation.
An *aggregating object* (which we will refer to as `y`) is *congruent* with the target object (`x`) if the two objects have shared borders.
Often this is the case for administrative boundary data, whereby larger units --- such as Middle Layer Super Output Areas ([MSOAs](https://www.ons.gov.uk/methodology/geography/ukgeographies/censusgeography)) in the UK or districts in many other European countries --- are composed of many smaller units.

*Incongruent* aggregating objects, by contrast, do not share common borders with the target [@qiu_development_2012].
This is problematic for spatial aggregation (and other spatial operations) illustrated in Figure \@ref(fig:areal-example): aggregating the centroid of each sub-zone will not return accurate results.
Areal interpolation overcomes this issue by transferring values from one set of areal units to another, using a range of algorithms including simple area weighted approaches and more sophisticated approaches such as 'pycnophylactic' methods [@tobler_smooth_1979].

```{r areal-example, echo=FALSE, fig.cap="Illustration of congruent (left) and incongruent (right) areal units with respect to larger aggregating zones (translucent blue borders).", fig.asp=0.2, fig.scap="Illustration of congruent and incongruent areal units."}
source("https://github.com/Robinlovelace/geocompr/raw/main/code/04-areal-example.R", print.eval = TRUE)
```

The **spData** package contains a dataset named `incongruent` (colored polygons with black borders in the right panel of Figure \@ref(fig:areal-example)) and a dataset named `aggregating_zones` (the two polygons with the translucent blue border in the right panel of Figure \@ref(fig:areal-example)).
Let us assume that the `value` column of `incongruent` refers to the total regional income in million Euros.
How can we transfer the values of the underlying nine spatial polygons into the two polygons of `aggregating_zones`?

The simplest useful method for this is *area weighted* spatial interpolation, which transfers values from the `incongruent` object to a new column in `aggregating_zones` in proportion with the area of overlap: the larger the spatial intersection between input and output features, the larger the corresponding value.
This is implemented in `st_interpolate_aw()`, as demonstrated in the code chunk below.

```{r 04-spatial-operations-30}
iv = incongruent["value"] # keep only the values to be transferred
agg_aw = st_interpolate_aw(iv, aggregating_zones, ext = TRUE)
agg_aw$value
```

In our case it is meaningful to sum up the values of the intersections falling into the aggregating zones since total income is a so-called spatially extensive variable (which increases with area), assuming income is evenly distributed across the smaller zones (hence the warning message above).
This would be different for spatially [intensive](http://ibis.geog.ubc.ca/courses/geob370/notes/intensive_extensive.htm) variables such as *average* income or percentages, which do not increase as the area increases.
`st_interpolate_aw()` works equally with spatially intensive variables: set the `extensive` parameter to `FALSE` and it will use an average rather than a sum function when doing the aggregation.

### Distance relations 

While topological relations are binary --- a feature either intersects with another or does not --- distance relations are continuous.
The distance between two objects is calculated with the `st_distance()` function.
This is illustrated in the code chunk below, which finds the distance between the highest point in New Zealand and the geographic centroid of the Canterbury region, created in Section \@ref(spatial-subsetting):
\index{sf!distance relations}

```{r 04-spatial-operations-31, warning=FALSE}
nz_heighest = nz_height %>% top_n(n = 1, wt = elevation)
canterbury_centroid = st_centroid(canterbury)
st_distance(nz_heighest, canterbury_centroid)
```

There are two potentially surprising things about the result:

- It has `units`, telling us the distance is 100,000 meters, not 100,000 inches, or any other measure of distance
- It is returned as a matrix, even though the result only contains a single value

This second feature hints at another useful feature of `st_distance()`, its ability to return *distance matrices* between all combinations of features in objects `x` and `y`.
This is illustrated in the command below, which finds the distances between the first three features in `nz_height` and the Otago and Canterbury regions of New Zealand represented by the object `co`.

```{r 04-spatial-operations-32}
co = filter(nz, grepl("Canter|Otag", Name))
st_distance(nz_height[1:3, ], co)
```

Note that the distance between the second and third features in `nz_height` and the second feature in `co` is zero.
This demonstrates the fact that distances between points and polygons refer to the distance to *any part of the polygon*:
The second and third points in `nz_height` are *in* Otago, which can be verified by plotting them (result not shown):

```{r 04-spatial-operations-33, eval=FALSE}
plot(st_geometry(co)[2])
plot(st_geometry(nz_height)[2:3], add = TRUE)
```

## Spatial operations on raster data {#spatial-ras}

This section builds on Section \@ref(manipulating-raster-objects), which highlights various basic methods for manipulating raster datasets, to demonstrate more advanced and explicitly spatial raster operations, and uses the objects `elev` and `grain` manually created in Section \@ref(manipulating-raster-objects).
For the reader's convenience, these datasets can be also found in the **spData** package.

### Spatial subsetting {#spatial-raster-subsetting}

The previous chapter (Section \@ref(manipulating-raster-objects)) demonstrated how to retrieve values associated with specific cell IDs or row and column combinations.
Raster objects can also be extracted by location (coordinates) and other spatial objects.
To use coordinates for subsetting, one can 'translate' the coordinates into a cell ID with the **terra** function `cellFromXY()`.
An alternative is to use `terra::extract()` (be careful, there is also a function called `extract()` in the **tidyverse**\index{tidyverse (package)}) to extract values.
Both methods are demonstrated below to find the value of the cell that covers a point located at coordinates of 0.1, 0.1.
\index{raster!subsetting}
\index{spatial!subsetting}

```{r 04-spatial-operations-34, eval = FALSE}
id = cellFromXY(elev, xy = matrix(c(0.1, 0.1), ncol = 2))
elev[id]
# the same as
terra::extract(elev, matrix(c(0.1, 0.1), ncol = 2))
```

<!--jn:toDo-->
<!-- to update? -->
<!-- It is convenient that both functions also accept objects of class `Spatial* Objects`. -->
Raster objects can also be subset with another raster object, as demonstrated in the code chunk below:

```{r 04-spatial-operations-35, eval=FALSE}
clip = rast(xmin = 0.9, xmax = 1.8, ymin = -0.45, ymax = 0.45,
            resolution = 0.3, vals = rep(1, 9))
elev[clip]
# we can also use extract
# terra::extract(elev, ext(clip))
```

This amounts to retrieving the values of the first raster object (in this case `elev`) that fall within the extent of a second raster (here: `clip`), as illustrated in Figure \@ref(fig:raster-subset).

```{r raster-subset, echo = FALSE, fig.cap = "Original raster (left). Raster mask (middle). Output of masking a raster (right).", fig.scap="Subsetting raster values."}
knitr::include_graphics("figures/04_raster_subset.png")
```

The example above returned the values of specific cells, but in many cases spatial outputs from subsetting operations on raster datasets are needed.
This can be done using the `[` operator, with `drop = FALSE`, as outlined in Section \@ref(manipulating-raster-objects), which also shows how raster objects can be subsetted by various objects.
This is demonstrated in the code below, which returns the first two cells of `elev` as a raster object the first two cells on the top row (only the first 2 lines of the output is shown):

```{r 04-spatial-operations-36, eval=FALSE}
elev[1:2, drop = FALSE]    # spatial subsetting with cell IDs
#> class       : SpatRaster 
#> dimensions  : 1, 2, 1  (nrow, ncol, nlyr)
#> ...
```

```{r 04-spatial-operations-37, echo=FALSE, eval=FALSE}
# aim: illustrate the result of previous spatial subsetting example
x = elev[1, 1:2, drop = FALSE]
plot(x)
```

Another common use case of spatial subsetting is when a raster with `logical` (or `NA`) values is used to mask another raster with the same extent and resolution, as illustrated in Figure \@ref(fig:raster-subset).
In this case, the `[` and `mask()` functions can be used (results not shown):

```{r 04-spatial-operations-38, eval=FALSE}
# create raster mask
rmask = elev
values(rmask) = sample(c(NA, TRUE), 36, replace = TRUE)
```

In the code chunk above, we have created a mask object called `rmask` with values randomly assigned to `NA` and `TRUE`.
Next, we want to keep those values of `elev` which are `TRUE` in `rmask`.
In other words, we want to mask `elev` with `rmask`.

```{r 04-spatial-operations-38b, eval=FALSE}
# spatial subsetting
elev[rmask, drop = FALSE]           # with [ operator
mask(elev, rmask)                   # with mask()
```

The above approach can be also used to replace some values (e.g., expected to be wrong) with NA. 

```{r 04-spatial-operations-38c, eval=FALSE}
elev[elev < 20] = NA
```

These operations are in fact Boolean local operations since we compare cell-wise two rasters.
The next subsection explores these and related operations in more detail.

### Map algebra

\index{map algebra}
The term 'map algebra' was coined in the late 1970s to describe a "set of conventions, capabilities, and techniques" for the analysis of geographic raster *and* (although less prominently) vector data [@tomlin_map_1994].
<!-- Although the concept never became widely adopted, the term usefully encapsulates and helps classify the range operations that can be undertaken on raster datasets. -->
In this context, we define map algebra more narrowly, as operations that modify or summarise raster cell values, with reference to surrounding cells, zones, or statistical functions that apply to every cell.

Map algebra operations tend to be fast, because raster datasets only implicitly store coordinates (hence the oversimplifying phrase "raster is faster but vector is corrector").
The location of cells in raster datasets can be calculated it using its matrix position and the resolution and origin of the dataset (stored in the header).
For the processing, however, the geographic position of a cell is barely relevant as long as we make sure that the cell position is still the same after the processing.
Additionally, if two or more raster datasets share the same extent, projection and resolution, one could treat them as matrices for the processing.

This is the way that map algebra works with the **terra** package.
First, the headers of the raster datasets are queried and (in cases where map algebra operations work on more than one dataset) checked to ensure the datasets are compatible.
Second, map algebra retains the so-called one-to-one locational correspondence, meaning that cells cannot move.
This differs from matrix algebra, in which values change position, for example when multiplying or dividing matrices.

Map algebra (or cartographic modeling with raster data) divides raster operations into four subclasses [@tomlin_geographic_1990], with each working on one or several grids simultaneously:

1. *Local* or per-cell operations
2. *Focal* or neighborhood operations.
Most often the output cell value is the result of a 3 x 3 input cell block
3. *Zonal* operations are similar to focal operations, but the surrounding pixel grid on which new values are computed can have irregular sizes and shapes
4. *Global* or per-raster operations; that means the output cell derives its value potentially from one or several entire rasters

This typology classifies map algebra operations by the number of cells used for each pixel processing step and the type of the output.
For the sake of completeness, we should mention that raster operations can also be classified by discipline such as terrain, hydrological analysis, or image classification.
The following sections explain how each type of map algebra operations can be used, with reference to worked examples.

### Local operations

\index{map algebra!local operations}
**Local** operations comprise all cell-by-cell operations in one or several layers.
Raster algebra is a classical use case of local operations -- this includes adding or subtracting values from a raster, squaring and multipling rasters.
Raster algebra also allows logical operations such as finding all raster cells that are greater than a specific value (5 in our example below).
The **terra** package supports all these operations and more, as demonstrated below (Figure \@ref(fig:04-local-operations)):

```{r 04-spatial-operations-41, eval = FALSE}
elev + elev
elev^2
log(elev)
elev > 5
```

```{r 04-local-operations, echo=FALSE, fig.cap="Examples of different local operations of the elev raster object: adding two rasters, squaring, applying logarithmic transformation, and performing a logical operation."}
knitr::include_graphics("figures/04-local-operations.png")
```

Another good example of local operations is the classification of intervals of numeric values into groups such as grouping a digital elevation model into low (class 1), middle (class 2) and high elevations (class 3).
Using the `classify()` command, we need first to construct a reclassification matrix, where the first column corresponds to the lower and the second column to the upper end of the class.
The third column represents the new value for the specified ranges in column one and two.

```{r 04-spatial-operations-40}
rcl = matrix(c(0, 12, 1, 12, 24, 2, 24, 36, 3), ncol = 3, byrow = TRUE)
rcl
```

Here, we assign the raster values in the ranges 0--12, 12--24 and 24--36 are *reclassified* to take values 1, 2 and 3, respectively.

```{r 04-spatial-operations-40b, eval = FALSE}
recl = classify(elev, rcl = rcl)
```

The `classify()` function can be also used when we want to reduce the number of classes in our categorical rasters.
We will perform several additional reclassifications in Chapter \@ref(location).

Apart of arithmetic operators, one can also use the `app()`, `tapp()` and `lapp()` functions.
They are more efficient, hence, they are preferable in the presence of large raster datasets. 
Additionally, they allow you to save an output file directly.
The `app()` function applies a function to each cell of a raster and is used to summarize (e.g., calculating the sum) the values of multiple layers into one layer.
`tapp()` is an extension of `app()`, allowing us to select a subset of layers (see the `index` argument) for which we want to perform a certain operation.
Finally, the `lapp()` function allows to apply a function to each cell using layers as arguments -- an application of `lapp()` is presented below.

The calculation of the normalized difference vegetation index (NDVI) is a well-known local (pixel-by-pixel) raster operation.
It returns a raster with values between -1 and 1; positive values indicate the presence of living plants (mostly > 0.2).
NDVI is calculated from red and near-infrared (NIR) bands of remotely sensed imagery, typically from satellite systems such as Landsat or Sentinel.
Vegetation absorbs light heavily in the visible light spectrum, and especially in the red channel, while reflecting NIR light, explaining the NVDI formula:

$$
\begin{split}
NDVI&= \frac{\text{NIR} - \text{Red}}{\text{NIR} + \text{Red}}\\
\end{split}
$$

Let's calculate NDVI for the multispectral satellite file of the Zion National Park.

```{r}
multi_raster_file = system.file("raster/landsat.tif", package = "spDataLarge")
multi_rast = rast(multi_raster_file)
```

The raster object has four satellite bands - blue, green, red, and near-infrared (NIR).
Our next step should be to implement the NDVI formula into an R function:

```{r}
ndvi_fun = function(nir, red){
  (nir - red) / (nir + red)
}
```

This function accepts two numerical arguments, `nir` and `red`, and returns a numerical vector with NDVI values.
It can be used as the `fun` argument of `lapp()`.
We just need to remember that our function just needs two bands (not four from the original raster), and they need to be in the NIR, red order.
That is why we subset the input raster with `multi_rast[[c(4, 3)]]` before doing any calculations.

```{r}
ndvi_rast = lapp(multi_rast[[c(4, 3)]], fun = ndvi_fun)
```

The result, shown on the right panel in Figure \@ref(fig:04-ndvi), can be compared to the RGB image of the same area (left panel of the same Figure).
It allows us to see that the largest NDVI values are connected to areas of dense forest in the northern parts of the area, while the lowest values are related to the lake in the north and snowy mountain ridges.

```{r 04-ndvi, echo=FALSE, fig.cap="RGB image (left) and NDVI values (right) calculated for the example satellite file of the Zion National Park"}
knitr::include_graphics("figures/04-ndvi.png")
```

Predictive mapping is another interesting application of local raster operations.
The response variable corresponds to measured or observed points in space, for example, species richness, the presence of landslides, tree disease or crop yield.
Consequently, we can easily retrieve space- or airborne predictor variables from various rasters (elevation, pH, precipitation, temperature, landcover, soil class, etc.).
Subsequently, we model our response as a function of our predictors using `lm()`, `glm()`, `gam()` or a machine-learning technique. 
Spatial predictions on raster objects can therefore be made by applying estimated coefficients to the predictor raster values, and summing the output raster values (see Chapter \@ref(eco)).

### Focal operations

\index{map algebra!focal operations}
While local functions operate on one cell, though possibly from multiple layers, **focal** operations take into account a central (focal) cell and its neighbors.
The neighborhood (also named kernel, filter or moving window) under consideration is typically of size 3-by-3 cells (that is the central cell and its eight surrounding neighbors), but can take on any other (not necessarily rectangular) shape as defined by the user.
A focal operation applies an aggregation function to all cells within the specified neighborhood, uses the corresponding output as the new value for the the central cell, and moves on to the next central cell (Figure \@ref(fig:focal-example)).
Other names for this operation are spatial filtering and convolution [@burrough_principles_2015].

In R, we can use the `focal()` function to perform spatial filtering. 
We define the shape of the moving window with a `matrix` whose values correspond to weights (see `w` parameter in the code chunk below).
Secondly, the `fun` parameter lets us specify the function we wish to apply to this neighborhood.
Here, we choose the minimum, but any other summary function, including `sum()`, `mean()`, or `var()` can be used.

```{r 04-spatial-operations-42, eval = FALSE}
r_focal = focal(elev, w = matrix(1, nrow = 3, ncol = 3), fun = min)
```

This function also accepts additional arguments, for example, should it remove NAs in the process (`na.rm = TRUE`) or not (`na.rm = FALSE`).

```{r focal-example, echo = FALSE, fig.cap = "Input raster (left) and resulting output raster (right) due to a focal operation - finding the minimum value in 3-by-3 moving windows.", fig.scap="Illustration of a focal operation."}
knitr::include_graphics("figures/04_focal_example.png")
```

We can quickly check if the output meets our expectations.
In our example, the minimum value has to be always the upper left corner of the moving window (remember we have created the input raster by row-wise incrementing the cell values by one starting at the upper left corner).
In this example, the weighting matrix consists only of 1s, meaning each cell has the same weight on the output, but this can be changed.

Focal functions or filters play a dominant role in image processing.
Low-pass or smoothing filters use the mean function to remove extremes.
In the case of categorical data, we can replace the mean with the mode, which is the most common value.
By contrast, high-pass filters accentuate features.
The line detection Laplace and Sobel filters might serve as an example here.
Check the `focal()` help page for how to use them in R (this will also be used in the exercises at the end of this chapter).

Terrain processing, the calculation of topographic characteristics such as slope, aspect and flow directions, relies on focal functions.
`terrain()` can be used to calculate these metrics, although some terrain algorithms, including the Zevenbergen and Thorne method to compute slope, are not implemented in this **terra** function.
Many other algorithms --- including curvatures, contributing areas and wetness indices --- are implemented in open source desktop geographic information system (GIS) software.
Chapter \@ref(gis) shows how to access such GIS functionality from within R.

### Zonal operations

\index{map algebra!zonal operations}
Just like focal operations, *zonal* operations apply an aggregation function to multiple raster cells.
However, a second raster, usually with categorical values, defines the *zonal filters* (or 'zones') in the case of zonal operations, as opposed to a predefined neighborhood window in the case of focal operation presented in the previous section.
Consequently, raster cells defining the zonal filter do not necessarily have to be neighbors.
Our grain size raster is a good example, as illustrated in the right panel of Figure \@ref(fig:cont-raster)): different grain sizes are spread irregularly throughout the raster.
Finally, the result of a zonal operation is a summary table grouped by zone which is why this operation is also known as *zonal statistics* in the GIS world\index{GIS}. 
This is in contrast to focal operations which return a raster object.

The following code chunk uses the `zonal()` function to calculate the mean elevation associated with each grain size class, for example.
The output is shown in Figure \@ref(fig:cont-raster)).

```{r 04-spatial-operations-43}
z = zonal(elev, grain, fun = "mean")
z
```

This returns the statistics\index{statistics} for each category, here the mean altitude for each grain size class.
Note: it is also possible to get a raster with calculated statistics for each zone by setting the `as.raster` argument to `TRUE`.

### Global operations and distances

*Global* operations are a special case of zonal operations with the entire raster dataset representing a single zone.
The most common global operations are descriptive statistics\index{statistics} for the entire raster dataset such as the minimum or maximum -- we already discussed those in Section \@ref(summarizing-raster-objects).

Aside from that, global operations are also useful for the computation of distance and weight rasters.
In the first case, one can calculate the distance from each cell to a specific target cell.
For example, one might want to compute the distance to the nearest coast (see also `terra::distance()`).
We might also want to consider topography, that means, we are not only interested in the pure distance but would like also to avoid the crossing of mountain ranges when going to the coast.
To do so, we can weight the distance with elevation so that each additional altitudinal meter 'prolongs' the Euclidean distance.
Visibility and viewshed computations also belong to the family of global operations (in the exercises of Chapter \@ref(gis), you will compute a viewshed raster).

### Map algebra counterparts in vector processing

Many map algebra operations have a counterpart in vector processing [@liu_essential_2009].
Computing a distance raster (global operation) while only considering a maximum distance (logical focal operation) is the equivalent to a vector buffer operation (Section \@ref(clipping)).
Reclassifying raster data (either local or zonal function depending on the input) is equivalent to dissolving vector data (Section \@ref(spatial-joining)). 
Overlaying two rasters (local operation), where one contains `NULL` or `NA` values representing a mask, is similar to vector clipping (Section \@ref(clipping)).
Quite similar to spatial clipping is intersecting two layers (Section \@ref(spatial-subsetting)). 
The difference is that these two layers (vector or raster) simply share an overlapping area (see Figure \@ref(fig:venn-clip) for an example).
However, be careful with the wording.
Sometimes the same words have slightly different meanings for raster and vector data models.
Aggregating in the case of vector data refers to dissolving polygons, while it means increasing the resolution in the case of raster data.
In fact, one could see dissolving or aggregating polygons as decreasing the resolution. 
However, zonal operations might be the better raster equivalent compared to changing the cell resolution. 
Zonal operations can dissolve the cells of one raster in accordance with the zones (categories) of another raster using an aggregation function (see above).

### Merging rasters

\index{raster!merge}
Suppose we would like to compute the NDVI (see Section \@ref(local-operations)), and additionally want to compute terrain attributes from elevation data for observations within a study area.
Such computations rely on remotely sensed information. 
The corresponding imagery is often divided into scenes covering a specific spatial extent, and frequently, a study area covers more than one scene.
Then, we would need to merge the scenes covered by our study area. 
In the easiest case, we can just merge these scenes, that is put them side by side.
This is possible, for example, with digital elevation data (SRTM, ASTER).
In the following code chunk we first download the SRTM elevation data for Austria and Switzerland (for the country codes, see the **geodata** function `country_codes()`).
In a second step, we merge the two rasters into one.

```{r 04-spatial-operations-44, eval = FALSE}
aut = geodata::elevation_30s(country = "AUT", path = tempdir())
ch = geodata::elevation_30s(country = "CHE", path = tempdir())
aut_ch = merge(aut, ch)
```

**terra**'s `merge()` command combines two images, and in case they overlap, it uses the value of the first raster.
<!--jn:toDo-->
<!-- gdalUtils is slower (for this files): -->
<!-- two_rast = c(terra::sources(aut)$source, terra::sources(ch)$source) -->
<!-- tf = tempfile(fileext = ".tif") -->
<!-- bench::mark({gdalUtils::mosaic_rasters(two_rast, tf)}) -->
<!-- You can do exactly the same with `gdalUtils::mosaic_rasters()` which is faster, and therefore recommended if you have to merge a multitude of large rasters stored on disk. -->

The merging approach is of little use when the overlapping values do not correspond to each other.
This is frequently the case when you want to combine spectral imagery from scenes that were taken on different dates.
The `merge()` command will still work but you will see a clear border in the resulting image.
On the other hand, the `mosaic()` command lets you define a function for the overlapping area. 
For instance, we could compute the mean value -- this might smooth the clear border in the merged result but it will most likely not make it disappear.
To do so, we need a more advanced approach. 
Remote sensing scientists frequently apply histogram matching or use regression techniques to align the values of the first image with those of the second image.
The packages **landsat** (`histmatch()`, `relnorm()`, `PIF()`), **satellite** (`calcHistMatch()`) and **RStoolbox** (`histMatch()`, `pifMatch()`) provide the corresponding functions for the **raster**'s package objects.
For a more detailed introduction on how to use R for remote sensing, we refer the reader to @wegmann_remote_2016.
<!--jn:toDo-->
<!--update the above reference to the 2nd edition-->

## Exercises


```{r, echo=FALSE, results='asis'}
res = knitr::knit_child('_04-ex.Rmd', quiet = TRUE, options = list(include = FALSE, eval = FALSE))
cat(res, sep = '\n')
```