Geospatial data analysis in R

Vector data II

Josh Merfeld

KDI School

October 1, 2024

Getting started with geospatial data, part II

What are we doing today?
- Shapefiles
  - The packages terra and tidyterra
  - More practice on maps
Operations on shapefiles
- Spatial merging
Areas and distances
More shapefile operations
- Continued next week, too

The package `terra`

A new package

Last week, we used the sf package
- This is a really nice, introductory way to ease into shapefiles
The terra package, though, is much more powerful
- It is a complete replacement for the sf package
- It is also a complete replacement for the raster package
- It is also a complete replacement for the sp package
- It is also a complete replacement for the rgdal package
- Etc. etc. etc.

Loading a shapefile

Code

library(terra)

# load the mw3 shapefile
mw3 <- vect("week3files/mw3.shp")

mw3

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 76, 2  (geometries, attributes)
 extent      : 493675.9, 691460, 8591761, 8964834  (xmin, xmax, ymin, ymax)
 source      : mw3.shp
 coord. ref. : Arc 1950 / UTM zone 36S 
 names       : TA_CODE DIST_CODE
 type        :   <chr>     <chr>
 values      :   10120       101
                 10110       101
                 10102       101

Saving a shapefile

Code

# with terra
writeVector(mw3, "locationoffile.shp")
# with sf
write_sf(mw3, "locationoffile.shp")

Plotting the shapefile

Code

# terra uses base R plots by default!
plot(mw3)

I don’t use base R… so ggplot!

Code

# we need this library to use ggplot with terra objects
library(tidyterra)
ggplot() + 
  geom_spatvector(data = mw3) +
  theme_bw()

Find the name of the district variable

Code

ggplot() + 
  geom_spatvector(data = mw3, aes(fill = DIST_CODE)) +
  theme_bw()

Note that you can extract variables with `$`

Code

mw3$DIST_CODE

 [1] "101" "101" "101" "101" "101" "101" "101" "101" "101" "101" "101" "102"
[13] "102" "102" "102" "102" "102" "102" "106" "106" "105" "105" "105" "105"
[25] "105" "105" "105" "105" "105" "105" "105" "105" "105" "107" "107" "107"
[37] "107" "107" "107" "107" "107" "107" "107" "107" "107" "107" "107" "107"
[49] "103" "103" "103" "103" "103" "103" "103" "103" "103" "103" "103" "103"
[61] "103" "103" "104" "104" "104" "104" "104" "104" "104" "104" "104" "104"
[73] "104" "104" "104" "104"

What if we want to aggregate to districts?

i.e. we want to “aggregate” or “dissolve” polygons

What if we want to create a new shapefile with districts?

Code

mw2 <- aggregate(mw3, by = "DIST_CODE")
nrow(mw2)

[1] 7

Code

# check if mw3 has same number of districts
length(unique(mw3$DIST_CODE))

[1] 7

What if we want to create a new shapefile with districts?

What if we want to do something like this?

Let’s start simpler!

A bunch of steps!

Load the shapefile

Create bounding boxes and convert to vect

Intersect the shapefile with the bounding boxes

Plot the shapefile with the bounding boxes
- This last one takes a lot of experimentation

Step 1: Load the shapefile

Please download the mw3allcountry.zip file and extract to your computer
- Then load it using vect()

Code

mw3all <- vect("week3files/mw3allcountry.shp")
# we'll use this variable
mw3all$surveyobs

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[2971]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  32   0
[2989]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16
[3007]   0   0   0   0  16   0   0   0   0   0   0   0   0  16  16   0   0   0
[3025]   0  16   0   0  16   0   0   0   0   0   0   0   0   0   0   0   0  16
[3043]   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  16   0
[3061]   0   0   0   0   0   0   0  16   0   0  16  16   0   0   0   0   0   0
[3079]   0   0  16   0   0   0   0   0   0   0   0   0   0  16   0   0  16   0
[3097]   0   0   0   0   0   0   0   0   0   0  16   0   0   0   0   0   0  16
[3115]   0   0   0   0   0  16   0   0   0   0   0 316

Step 2: Create bounding boxes (extent)

Now we have to find the extent of Lilongwe and Blantyre
- Look through the data
- What variables might help us with this?

Code

mw3all

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 3126, 16  (geometries, attributes)
 extent      : 32.67152, 35.91505, -17.12721, -9.363796  (xmin, xmax, ymin, ymax)
 source      : mw3allcountry.shp
 coord. ref. : lon/lat WGS 84 (EPSG:4326) 
 names       :  ID_0   ISO NAME_0  ID_1 NAME_1  ID_2      NAME_2  ID_3
 type        : <int> <chr>  <chr> <int>  <chr> <int>       <chr> <int>
 values      :   135   MWI Malawi     1 Balaka     1 Balaka Town     1
                 135   MWI Malawi     1 Balaka     1 Balaka Town     2
                 135   MWI Malawi     1 Balaka     1 Balaka Town     3
      NAME_3 CCN_3 (and 6 more)
       <chr> <int>             
 n.a. (1951)     0             
 n.a. (1953)     0             
 n.a. (1954)     0

Step 2: Create bounding boxes (extent)

Let’s look at the NAME_1 variable

Code

unique(mw3all$NAME_1)

 [1] "Balaka"     "Blantyre"   "Chikwawa"   "Chiradzulu" "Chitipa"   
 [6] "Dedza"      "Dowa"       "Karonga"    "Kasungu"    "Likoma"    
[11] "Lilongwe"   "Machinga"   "Mangochi"   "Mchinji"    "Mulanje"   
[16] "Mwanza"     "Mzimba"     "Neno"       "Nkhata Bay" "Nkhotakota"
[21] "Nsanje"     "Ntcheu"     "Ntchisi"    "Phalombe"   "Rumphi"    
[26] "Salima"     "Thyolo"     "Zomba"

Step 2: Create bounding boxes (extent)

Let’s create new shapefiles for Lilongwe and Blantyre
- How?

Code

lilongwe <- mw3all[mw3all$NAME_1=="Lilongwe",]
blantyre <- mw3all[mw3all$NAME_1=="Blantyre",]

Now create extents

Code

lilongwebox <- ext(lilongwe)
blantyrebox <- ext(blantyre)

Step 2: Create bounding boxes (extent)

What is an extent?
- We’ve covered this before!

Code

lilongwebox

SpatExtent : 33.2449913024903, 34.2784042358398, -14.6049509048462, -13.4817895889282 (xmin, xmax, ymin, ymax)

Code

blantyrebox

SpatExtent : 34.7182960510255, 35.1323623657227, -16.0146331787109, -15.3487529754639 (xmin, xmax, ymin, ymax)

Step 2: Create bounding boxes (extent)

You can actually plot those as is!
- But let’s explicitly give them the correct CRS (and turn them into vect objects)

Code

lilongwebox <- vect(lilongwebox, crs = crs(mw3all))

Try it for Blantyre

Code

blantyrebox <- vect(blantyrebox, crs = crs(mw3all))

Let’s plot them with the original shapefile

Code

ggplot() + 
  geom_spatvector(data = mw3all, 
    aes(fill = surveyobs), color = NA) +
  geom_spatvector(data = lilongwebox, 
    fill = NA, color = "black") +
  geom_spatvector(data = blantyrebox, 
    fill = NA, color = "black") +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw()

Step 3: Intersect the shapefile with the bounding boxes

Now we need to intersect the shapefile with the bounding boxes
- This will give us the shapefiles for Lilongwe and Blantyre

Code

lilongwe <- intersect(mw3all, lilongwebox)
ggplot() + 
  geom_spatvector(data = lilongwe, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw()

Go ahead and do the same for Blantyre

(and plot it!)

Go ahead and do the same for Blantyre

Code

blantyre <- intersect(mw3all, blantyrebox)
ggplot() + 
  geom_spatvector(data = blantyre, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw()

Note the legend scale changed!
- We’ll correct that later

Step 4. Plot the shapefile with the bounding boxes

This is the hard part!
- A couple hard things:
Grid size:
- For Malawi, the grid is quite small
- We want to expand it a bit
Finding the right locations:
- It takes experimentation to figure out where to put the insets

Expanding the grid - can you spot the difference?

We do this with `expand_limits`

If we provide expand_limits with a single x coordinate, it will expand the grid to include that coordinate
What does this code do?

Code

ggplot() + 
  geom_spatvector(data = mw3all, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  theme(legend.position = "bottom") +
  expand_limits(x = ext(mw3all)[2] + 2.5)

Adding insets

We can use cowplot to add insets to our plot
- This is a bit tricky, but it’s a nice way to show multiple levels of detail
Step 1:
- Create main plot and save (I’ll save as g1)
Step 2:
- Create the insets and save them
- I’ll save them as g2 and g3
Step 3:
- Use ggdraw to add insets

Step 1: Create the main plot

Code

g1 <- ggplot() + 
  geom_spatvector(data = mw3all, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  theme(legend.position = "bottom") +
  expand_limits(x = ext(mw3all)[2] + 2.5)

Step 1: Create the main plot

Step 2: Create the insets

Code

g2 <- ggplot() + 
  geom_spatvector(data = lilongwe, 
    aes(fill = surveyobs), color = NA,
    show.legend = FALSE) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  # this part is important!
  theme(plot.background = element_rect(fill = "transparent", color = "transparent")) +
  theme(axis.title.x = element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank())

Step 2: Create the insets

Go ahead and do it for Blantyre

Code

g3 <- ggplot() + 
  geom_spatvector(data = blantyre, 
    aes(fill = surveyobs), color = NA,
    show.legend = FALSE) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  theme(axis.title.x = element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank()) +
  theme(plot.background = element_rect(fill = "transparent", color = "transparent"))

Step 2: Create the insets

Step 3: Add insets with `ggdraw`

Code

ggdraw(g1) +
  draw_plot(g2 + theme(legend.position = "none"), 
    x = ??, y = ??, height = ??, width = ??) +
  draw_plot(g3 + theme(legend.position = "none"), 
    x = ??, y = ??, height = ??, width = ??)

This is where the experimentation comes in!
- All the ?? values need to be between 0 and 1
- They represent proportions of the plot
- The legend.position = "none" is just to make sure it’s not there!
Give it a try. See if you can come up with a good combination!
- I’ll show you what I did in a minute

Step 3: Add insets with `ggdraw`

Code

ggdraw(g1) +
  draw_plot(g2, x = 0.425, y = 0.6, height = 0.275, width = 0.275) +
  draw_plot(g3, x = 0.485, y = 0.23, height = 0.275, width = 0.2) +

Step 3: Add insets with `ggdraw`

This still isn’t quite right

There are still some changes to make
- Can you spot them?

The legend for Blantyre is wrong!
Let’s delete the “x” axis label
Let’s put the legend title on top
Add subtitles for Blantyre and Lilongwe

The Blantyre legend - what’s the difference?

Using `limits` in `scale_fill_distiller`

Code

g3 <- ggplot() + 
  geom_spatvector(data = blantyre, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral", 
    limits = c(min(mw3all$surveyobs), max(mw3all$surveyobs))) +
  theme_bw()

This also works with all the other scale_ options
- scale_fill_brewer, scale_color_brewer, etc.

Delete the x-axis label

See if you can find an answer on Google!
- There are a couple possible ways to do it

Code

g1 <- ggplot() + 
  geom_spatvector(data = mw3all, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  theme(legend.position = "bottom") +
  expand_limits(x = ext(mw3all)[2] + 2.5) +
  theme(axis.title.x = element_blank())

Finally, move legend title to top

Code

g1 <- ggplot() + 
  geom_spatvector(data = mw3all, 
    aes(fill = surveyobs), color = NA) +
  scale_fill_distiller("Survey\nobservations", 
    palette = "Spectral") +
  theme_bw() +
  theme(legend.position = "bottom") +
  expand_limits(x = ext(mw3all)[2] + 2.5) +
  theme(axis.title.x = element_blank()) +
  guides(fill = guide_colorbar(title.position="top", title.hjust = 0.5))

Note that if it’s a color instead of a fill, should be color = instead of fill =

Adding titles for the insets

How do you add a title to Blantyre and Lilongwe?
- Note that I use subtitle here because it’s a bit smaller (you could also change the text size)

labs(subtitle = "Lilongwe")

labs(subtitle = "Blantyre")

One more small change: the width of the legend

theme(legend.key.width = unit(0.5, "inches"))

Putting it all together

Intersections and spatial joins

Let’s move on to a new topic

Here’s our goal:
- We want to count the number of points in each polygon
- We want to count points in each ADMIN2 (not admin3)
We have two files: one is data with lon/lat coordinates and one is admin3 polygons
- We need to create a shapefile from the points
- We need to aggregate the admin3 to admin2
- We need to extract the admin2 identifier into points
- Then we need to join the two together

What we need

Points: points.csv
- This is a list of health facilities in Malawi
- Note that this data is NOT complete (it comes from OpenStreetMap)
Polygons: mw3allcountry.shp
- Let’s use the entire country

Step 1: Load the points

Code

# note that this function is in tidyverse!
points <- read_csv("week3files/points.csv")

Find the name of the columns that represent the coordinates
- We’ll need these to create the spatial object

Code

# Let's turn it into a `terra` object
points <- vect(points, geom = c("x", "y"), crs = "EPSG:4326") # these are lon/lat
points

 class       : SpatVector 
 geometry    : points 
 dimensions  : 155, 1  (geometries, attributes)
 extent      : 32.88771, 35.71544, -16.52659, -9.496221  (xmin, xmax, ymin, ymax)
 coord. ref. : lon/lat WGS 84 (EPSG:4326) 
 names       :          fclass
 type        :           <chr>
 values      : health facility
               health facility
               health facility

Let’s just plot the points

Code

ggplot() + 
  geom_spatvector(data = points) +
  theme_bw()

Now let’s load the admin3 and aggregate

Code

adm3 <- vect("week3files/mw3allcountry.shp")

Do you remember how to aggregate to admin2?
- You need to use aggregate and the column name!
- Look at the column names

Code

adm2 <- aggregate(adm3, "ID_2")

Let’s plot it

Code

ggplot() + 
  geom_spatvector(data = adm2) +
  theme_bw()

Time to extract

We are going to use the function relate to find which adm2 feature the point lies within
- This is a spatial join
- We are also going to use this to learn a bit more about R

Code

# let's first make sure it's the same CRS!
points <- project(points, crs(adm2))

join <- relate(adm2, points, "contains")
# check the dimensions
dim(join)

[1] 256 155

The `apply` function

Example data
	col1	col2	col3	col4
Point 1	TRUE	FALSE	FALSE	TRUE
Point 2	FALSE	FALSE	TRUE	FALSE
Point 3	FALSE	TRUE	FALSE	TRUE
Point 4	FALSE	TRUE	TRUE	FALSE
Point 5	FALSE	FALSE	FALSE	TRUE
Point 6	TRUE	TRUE	FALSE	TRUE

Let’s look at an example
An important note:
- If you take the mean of logical statements, it treats TRUE as 1 and FALSE as 0
- Same with sum

The `apply` function

Example data
	col1	col2	col3	col4
Point 1	TRUE	FALSE	FALSE	TRUE
Point 2	FALSE	FALSE	TRUE	FALSE
Point 3	FALSE	TRUE	FALSE	TRUE
Point 4	FALSE	TRUE	TRUE	FALSE
Point 5	FALSE	FALSE	FALSE	TRUE
Point 6	TRUE	TRUE	FALSE	TRUE

In this data, what happens if we:
- Take the sum of each row?
- What values do we get?

The `apply` function

Example data
	col1	col2	col3	col4	sum
Point 1	TRUE	FALSE	FALSE	TRUE	2
Point 2	FALSE	FALSE	TRUE	FALSE	1
Point 3	FALSE	TRUE	FALSE	TRUE	2
Point 4	FALSE	TRUE	TRUE	FALSE	2
Point 5	FALSE	FALSE	FALSE	TRUE	1
Point 6	TRUE	TRUE	FALSE	TRUE	3

We can use the apply function to do this!
- data$sum = apply(data, 1, "sum")
- This will sum each row and put it in a new column called sum
- What happens if we instead use "mean"?
The 1 denotes doing this by row… how do we apply the function by column?

The `apply` function

Example data
	col1	col2	col3	col4	mean
Point 1	TRUE	FALSE	FALSE	TRUE	0.5
Point 2	FALSE	FALSE	TRUE	FALSE	0.25
Point 3	FALSE	TRUE	FALSE	TRUE	0.5
Point 4	FALSE	TRUE	TRUE	FALSE	0.5
Point 5	FALSE	FALSE	FALSE	TRUE	0.25
Point 6	TRUE	TRUE	FALSE	TRUE	0.75

data$mean = apply(data, 1, "mean")

Back to our data

Check the dimensions of the data
- What do rows and what do columns represent?

Code

dim(join)

[1] 256 155

Code

join

        [,1]  [,2]  [,3]  [,4]  [,5]  [,6]  [,7]  [,8]  [,9] [,10] [,11] [,12]
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  [2,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[240,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
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[255,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
       [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[254,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[255,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
       [,25] [,26] [,27] [,28] [,29] [,30] [,31] [,32] [,33] [,34] [,35] [,36]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[255,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
       [,37] [,38] [,39] [,40] [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[214,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[216,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[220,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[223,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[224,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[225,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[226,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[228,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[230,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[231,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
[232,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[233,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[234,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[235,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[244,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[245,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[254,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[255,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
       [,49] [,50] [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[224,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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       [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [,71] [,72]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[203,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE
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[224,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[225,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[226,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[227,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[228,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[229,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[230,]  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[231,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[232,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[233,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[234,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[235,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[236,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[237,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[238,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[239,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[240,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[242,] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE
[243,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[244,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[245,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[246,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[247,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[248,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[249,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[250,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[251,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[252,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[254,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[255,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
       [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [,81] [,82] [,83] [,84]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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  [4,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
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[153,] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[219,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[220,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[222,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[223,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[224,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[225,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[226,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[227,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[230,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[231,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[232,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[233,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[234,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[238,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[239,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[240,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[241,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[242,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[243,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[244,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[245,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[246,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[247,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[248,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[249,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[250,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[251,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[252,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[253,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[254,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[255,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[256,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
       [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [,93] [,94] [,95] [,96]
  [1,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[102,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[105,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[107,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[110,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[111,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[112,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[122,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[123,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[124,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE
[125,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[127,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[128,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[131,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[134,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[135,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[138,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[139,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[140,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[141,] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[142,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[143,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[144,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[145,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[148,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[150,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[151,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[154,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[157,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[158,] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
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[225,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE  FALSE
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[232,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
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[234,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[235,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[236,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[237,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[238,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[239,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[240,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[241,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[242,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[243,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[244,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[245,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[246,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[247,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE   TRUE
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[249,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[250,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
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[255,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[256,] FALSE FALSE FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
       [,107] [,108] [,109] [,110] [,111] [,112] [,113] [,114] [,115] [,116]
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[235,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
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[240,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[241,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[242,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[243,]  FALSE   TRUE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
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[256,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
       [,117] [,118] [,119] [,120] [,121] [,122] [,123] [,124] [,125] [,126]
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       [,147] [,148] [,149] [,150] [,151] [,152] [,153] [,154] [,155]
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[253,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[254,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[255,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE
[256,]  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE  FALSE

How do we use `apply` here?

So the columns are POINTS and the rows are POLYGONS (adm2)
What do we want to do?

We want to find the number of points in each polygon
- It is TRUE/FALSE so we want to sum BY rows!

How do we use `apply` here?

Code

adm2$points = apply(join, 1, "sum")
adm2$points

  [1]  2  2  2 14  0  1  0  0  0  1  0  0  0  0  0  1  1  0  1  2  0  0  0  0  0
 [26]  0  0  0  0  0  1  0  0  0  4  0  0  1  0  0  0  0  1  0  1  0  0  0  1  0
 [51]  0  1  0  0  0  0  1  2  0  0  1  0  0  0  0  0  0  0  1  0  0  0  0  0  0
 [76]  0  0  0  0  0  0 22  1  2  0  0  2  2  3  0  1  2  0  0  1  1  1  0  0  0
[101]  1  0  0  0  0  0  0  0  2  0  0  0  0  1  1  0  0  0  0  0  0  0  1  3  1
[126]  0  0  0  0  0  1  1  0  1  1  0  1  0  0  0  1  0  0  3  7  0  0  0  0  1
[151]  1  0  1  0  0  0  0  3  0  1  2  1  0  0  0  0  1  0  0  0  0  0  1  0  0
[176]  0  0  1  0  0  0  0  0  0  0  0  3  0  0  0  0  1  0  1  0  0  0  0  0  0
[201]  0  0  1  0  0  0  0  0  0  1  0  0  0  1  0  0  0  1  0  0  0  0  0  1  1
[226]  1  0  0  0  1  2  0  0  0  0  0  0  0  0  2  0  6  1  0  0  0  1  0  0  0
[251]  0  0  0  0  1  7

Now we can graph it!

Code

ggplot() + 
  geom_spatvector(data = adm2, aes(fill = points)) +
  scale_fill_distiller("Number of\npoints", palette = "Spectral") +
  theme_bw()

Make it a little nicer

Code

ggplot() + 
  geom_spatvector(data = adm2, aes(fill = points), color = NA) +
  scale_fill_distiller("Number of\npoints", palette = "Spectral") +
  theme_bw()

Now it’s your turn!

I have uploaded a map of Korea
- It is at the shi/gun/gu (시군구) level
- It is called kcities.shp (also a .zip there)
I have also uploaded a list of points
- These are train stations
- It is called krail.csv
Your task: Find how many train stations there are in each feature!
- Then map it
I’ll give you 10-15 minutes
- Then I’ll open a script and we’ll do it together

Calculating areas and lengths

We can also calculate areas and lengths!
perim for length
- For polygons, it returns the length of the perimeter
- For lines, it returns the length of the line
expanse for area
- Returns the area of polygons
- What will it return for lines or points?
Believe it or not, using lon/lat gives the most accurate results!
- This is because of the haversine formula

Area

Let’s use the adm3 shapefile

Code

adm3$area <- expanse(adm3, unit="km", transform = TRUE)

Unit: do you want $km^2$ or $m^2$?
Transform: automatically transform to lon/lat?
- Always do this. terra documentation says this will be more accurate

Area

Perimeter

Code

adm3$perimeter <- perim(adm3 |> project("EPSG:4326"))

This automatically gives length in meters
You can add project with a pipe operator |> to transform to lon/lat

Perimeter

More distances

We have points for households
We have points for health facilities
We can calculate the distance between each household and each health facility
- Finding distances between points is a common GIS task!

Distance matrix

The name of the households data is households.dta
- This is a Stata dataset
- You can read it using the package haven
Please go ahead and try loading the dataset and then turning it into a terra object
- You’ll have to find the names of the columns that represent the coordinates

Distance matrix

Code

# households
households <- read_dta("week3files/households.dta")
households <- vect(households, geom = c("ea_lon_mod", "ea_lat_mod"), crs = "EPSG:4326")
# health facilities
# do it in one line!
health <- vect(read_csv("week3files/points.csv"), geom = c("x", "y"), crs = "EPSG:4326")

Distance matrix

Code

distances <- distance(households, health)
dim(households)

[1] 11290    42

Code

dim(health)

[1] 155   1

Code

dim(distances)

[1] 11290   155

Distance matrix - “Heat map”

Closest health facility by household

How did I create the map?

The distances object is a matrix
- What are the rows and what are the columns?

If we want to find the closest health facility to each household, what do we need to do?
- We need to find the minimum distance for each row
- Do you remember?

Closest health facility by household

We can use the apply function!
- But with "min" instead of "sum"
- The rows are in the same order as the households, so…

Code

distances <- distance(households, health)
closest <- apply(distances, 1, "min")

# they're in the same order!
households$closest <- closest

ggplot() + 
  geom_spatvector(data = households, aes(color = closest/1000), size = 0.5) +
  scale_color_distiller("Distances\n(km)", palette = "Spectral") +
  theme_bw()

More advanced operations

Let’s go over some more advanced operations

This stuff will spillover into next week
First up:
- Spatial overlap

A grid in Korea - `kgrid.zip`

We will discuss how to make a grid after we learn about rasters
For now, the grid is a shapefile
A very common operation:
- We want to know which province/city each grid cell is in
- This isn’t straightforward. Why?

A grid cell can overlap multiple provinces/cities

The data

Here is the data:
- kshape.shp is the shapefile of the provinces/cities
- kgrid.shp is the grid
- I have uploaded .zip files for both

Code

kshape <- vect("week3files/kshape.shp")
kgrid <- vect("week3files/kgrid.shp")
kgrid

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 4906, 1  (geometries, attributes)
 extent      : 746110.3, 1306110, 1458754, 2068754  (xmin, xmax, ymin, ymax)
 source      : kgrid.shp
 coord. ref. : Korea 2000 / Unified CS (EPSG:5179) 
 names       :    id
 type        : <int>
 values      :    65
                  66
                  67

The `intersect` function from `terra`

We are going to use the intersect function
Here’s what it will do:
- It will find the intersection of the grid cell and the province/city
- Except, it will return a new feature for EACH overlap
Let’s look at some maps

The `intersect` function from `terra`

Code

intersection <- intersect(kgrid, kshape)
intersection

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 5546, 3  (geometries, attributes)
 extent      : 746110.3, 1304099, 1458754, 2068444  (xmin, xmax, ymin, ymax)
 coord. ref. : Korea 2000 / Unified CS (EPSG:5179) 
 names       :    id CTPRVN_CD CTP_ENG_NM
 type        : <int>     <chr>      <chr>
 values      :    65        51 Gangwon-do
                  66        51 Gangwon-do
                  67        51 Gangwon-do

Code

kgrid

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 4906, 1  (geometries, attributes)
 extent      : 746110.3, 1306110, 1458754, 2068754  (xmin, xmax, ymin, ymax)
 source      : kgrid.shp
 coord. ref. : Korea 2000 / Unified CS (EPSG:5179) 
 names       :    id
 type        : <int>
 values      :    65
                  66
                  67

One grid cell, multiple overlaps

Area of overlap

So what do we want to do?
- Let’s find the area of overlap for each grid cell and each province/city
- Then let’s take the LARGEST overlap and assign that to the grid cell
- In practice, depending on the context, you could use a weighted mean or something else
  - This won’t work with categorical variables, though

Code

# Get area of all overlaps
intersection$area <- expanse(intersection)
# turn it into a tibble
intersection <- as_tibble(intersection)
# Get the largest overlap
intersection <- intersection |> group_by(id) |> filter(area==max(area)) |> ungroup()

Area of overlap

Code

intersection

# A tibble: 4,906 × 4
      id CTPRVN_CD CTP_ENG_NM      area
   <int> <chr>     <chr>          <dbl>
 1    65 51        Gangwon-do   332498.
 2    66 51        Gangwon-do  3885964.
 3    67 51        Gangwon-do  1659454.
 4   193 51        Gangwon-do   477650.
 5   194 51        Gangwon-do 12783973.
 6   195 51        Gangwon-do 14674738.
 7   321 51        Gangwon-do   289674.
 8   322 51        Gangwon-do 14274204.
 9   323 51        Gangwon-do 22582297.
10   324 51        Gangwon-do   604121.
# ℹ 4,896 more rows

Code

kgrid

 class       : SpatVector 
 geometry    : polygons 
 dimensions  : 4906, 1  (geometries, attributes)
 extent      : 746110.3, 1306110, 1458754, 2068754  (xmin, xmax, ymin, ymax)
 source      : kgrid.shp
 coord. ref. : Korea 2000 / Unified CS (EPSG:5179) 
 names       :    id
 type        : <int>
 values      :    65
                  66
                  67

Geospatial data analysis in R

Getting started with geospatial data, part II

The package terra

A new package

Loading a shapefile

Saving a shapefile

Plotting the shapefile

I don’t use base R… so ggplot!

Find the name of the district variable

Find the name of the district variable

Note that you can extract variables with $

What if we want to aggregate to districts?

What if we want to create a new shapefile with districts?

What if we want to create a new shapefile with districts?

What if we want to create a new shapefile with districts?

What if we want to do something like this?

Let’s start simpler!

A bunch of steps!

Step 1: Load the shapefile

Step 2: Create bounding boxes (extent)

Step 2: Create bounding boxes (extent)

Step 2: Create bounding boxes (extent)

Step 2: Create bounding boxes (extent)

Step 2: Create bounding boxes (extent)

Let’s plot them with the original shapefile

Step 3: Intersect the shapefile with the bounding boxes

Go ahead and do the same for Blantyre

Go ahead and do the same for Blantyre

Step 4. Plot the shapefile with the bounding boxes

Expanding the grid - can you spot the difference?

We do this with expand_limits

Adding insets

Step 1: Create the main plot

Step 1: Create the main plot

Step 2: Create the insets

Step 2: Create the insets

Step 2: Create the insets

Step 2: Create the insets

Step 3: Add insets with ggdraw

Step 3: Add insets with ggdraw

Step 3: Add insets with ggdraw

This still isn’t quite right

The Blantyre legend - what’s the difference?

Using limits in scale_fill_distiller

Delete the x-axis label

Delete the x-axis label

Finally, move legend title to top

Finally, move legend title to top

Adding titles for the insets

One more small change: the width of the legend

Putting it all together

Intersections and spatial joins

Let’s move on to a new topic

What we need

Step 1: Load the points

Let’s just plot the points

Now let’s load the admin3 and aggregate

Let’s plot it

Time to extract

The apply function

The apply function

The apply function

The apply function

Back to our data

How do we use apply here?

How do we use apply here?

Now we can graph it!

Make it a little nicer

Now it’s your turn!

Calculating areas and lengths

Area

Area

Perimeter

Perimeter

More distances

Distance matrix

Distance matrix

Distance matrix

Distance matrix - “Heat map”

Closest health facility by household

The package `terra`

Note that you can extract variables with `$`

We do this with `expand_limits`

Step 3: Add insets with `ggdraw`

Step 3: Add insets with `ggdraw`

Step 3: Add insets with `ggdraw`

Using `limits` in `scale_fill_distiller`

The `apply` function

The `apply` function

The `apply` function

The `apply` function

How do we use `apply` here?

How do we use `apply` here?

A grid in Korea - `kgrid.zip`

A grid in Korea - `kgrid.zip`

The `intersect` function from `terra`

The `intersect` function from `terra`

The `intersect` function from `terra`