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Spatial Data Wrangling with R

A Comprehensive Guide

Long Nguyen

Workshops for Ukraine · 2023-04-06

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Today's objectives

  • Systematic, fundamental overview of spatial data manipulation techniques
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Today's objectives

  • Systematic, fundamental overview of spatial data manipulation techniques

  • Focus on operations on vector data geometries, implemented by the {sf} package

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Today's objectives

  • Systematic, fundamental overview of spatial data manipulation techniques

  • Focus on operations on vector data geometries, implemented by the {sf} package

Predicates
(return boolean)		 Measures
(return number)		 Transformers
(return geometry)
Unary x x
Binary x x x
N-ary x
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Today's objectives

  • Systematic, fundamental overview of spatial data manipulation techniques

  • Focus on operations on vector data geometries, implemented by the {sf} package

Predicates
(return boolean)		 Measures
(return number)		 Transformers
(return geometry)
Unary x x
Binary x x x
N-ary x
  • Raster data operations to complement vector data attributes
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Today's objectives

  • Systematic, fundamental overview of spatial data manipulation techniques

  • Focus on operations on vector data geometries, implemented by the {sf} package

Predicates
(return boolean)		 Measures
(return number)		 Transformers
(return geometry)
Unary x x
Binary x x x
N-ary x

  • Raster data operations to complement vector data attributes

  • Leans heavily on the philosophy of {dplyr} and {ggplot2}

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Non-goals

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Non-goals

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Non-goals

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Topics I'll be hand-wavy about

  • Data import/export

    • Import: sf::read_sf() for vector data, terra::rast() for raster data

    • Export: sf::write_sf(), terra::writeRaster()

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Topics I'll be hand-wavy about

  • Data import/export

    • Import: sf::read_sf() for vector data, terra::rast() for raster data

    • Export: sf::write_sf(), terra::writeRaster()

  • Coordinate reference systems (CRSs)

    • Important!

    • But default just works most of the time (esp. since {sf} 1.0.0)

    • Further reading: Chapter 7 of Geocomputation with R

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Motivating Example: Non-Contiguous Cartogram of 2020 US Election

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library(sf)
library(tidyverse)
us_states <- read_sf("https://lo-ng.netlify.app/slides/data/us-states-albers.geojson")
us_states
Simple feature collection with 51 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 3
   fips_state name                                                      geometry
   <chr>      <chr>                                           <MULTIPOLYGON [m]>
 1 04         Arizona              (((-805018.1 -844577, -820986 -1004768, -847…
 2 05         Arkansas             (((558984.4 -1308375, 555289.4 -1249633, 542…
 3 06         California           (((-1857115 -997259, -1840861 -998344.4, -18…
 4 08         Colorado             (((-340987 -435780.7, -241136.9 -439894.2, -…
 5 09         Connecticut          (((2279630 65808.41, 2301408 2913.019, 23000…
 6 11         District of Columbia (((1955464 -402054, 1960244 -393610.9, 19740…
 7 13         Georgia              (((1426586 -978019.1, 1484709 -967004.7, 153…
 8 17         Illinois             (((998533.7 -204278.7, 998810.4 -225676.6, 1…
 9 18         Indiana              (((1305615 -535053.6, 1300530 -540420.3, 131…
10 22         Louisiana            (((558984.4 -1308375, 729349.1 -1295816, 828…
# ℹ 41 more rows
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library(sf)
library(tidyverse)
us_states <- read_sf("https://lo-ng.netlify.app/slides/data/us-states-albers.geojson")
us_votes <- read_rds("https://lo-ng.netlify.app/slides/data/us_votes.rds")
us_states |>
  left_join(us_votes) |>
  select(called, pop18)
Simple feature collection with 51 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 3
   called    pop18                                                      geometry
   <chr>     <dbl>                                            <MULTIPOLYGON [m]>
 1 D       7171646 (((-805018.1 -844577, -820986 -1004768, -847281.8 -1277600, …
 2 R       3013825 (((558984.4 -1308375, 555289.4 -1249633, 542141.8 -1246005, …
 3 D      39557045 (((-1857115 -997259, -1840861 -998344.4, -1833809 -1009424, …
 4 D       5695564 (((-340987 -435780.7, -241136.9 -439894.2, -172639.7 -441887…
 5 D       3572665 (((2279630 65808.41, 2301408 2913.019, 2300040 -8879.752, 22…
 6 D        702455 (((1955464 -402054, 1960244 -393610.9, 1974075 -401534.9, 19…
 7 D      10519475 (((1426586 -978019.1, 1484709 -967004.7, 1534994 -955419.9, …
 8 D      12741080 (((998533.7 -204278.7, 998810.4 -225676.6, 1014867 -248685, …
 9 R       6691878 (((1305615 -535053.6, 1300530 -540420.3, 1313039 -560392.6, …
10 R       4659978 (((558984.4 -1308375, 729349.1 -1295816, 828091.2 -1286761, …
# ℹ 41 more rows
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library(sf)
library(tidyverse)
us_states <- read_sf("https://lo-ng.netlify.app/slides/data/us-states-albers.geojson")
us_votes <- read_rds("https://lo-ng.netlify.app/slides/data/us_votes.rds")
us_states |>
  left_join(us_votes) |>
  select(called, pop18) |>
  ggplot(aes(fill = called)) +
  geom_sf(show.legend = FALSE) +
  scale_fill_manual(values = c("#25428f", "#c70000")) +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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library(sf)
library(tidyverse)
us_states <- read_sf("https://lo-ng.netlify.app/slides/data/us-states-albers.geojson")
us_votes <- read_rds("https://lo-ng.netlify.app/slides/data/us_votes.rds")
us_states |>
  left_join(us_votes) |>
  select(called, pop18) |>
  mutate(
    area = as.numeric(st_area(geometry)),
    scaled_area = pop18 / max(pop18) *
      area[which.max(pop18)],
    scale = sqrt(scaled_area / area),
    cent = st_centroid(geometry),
    geometry = (geometry - cent) * scale + cent
  ) |>
  ggplot(aes(fill = called)) +
  geom_sf(show.legend = FALSE) +
  scale_fill_manual(values = c("#25428f", "#c70000")) +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Spatial Vector Data in R

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Points, lines, and polygons

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Simple Features

  • Standard for representing spatial vector data
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Simple Features

  • Standard for representing spatial vector data

  • Simple Feature geometries:

POINT (2 2)
MULTIPOINT ((0 1), (2 2), (4 1), (2 3), (1 4))
LINESTRING (1 1, 5 5, 5 6, 4 6, 3 4, 2 3)
MULTILINESTRING ((1 1, 5 5, 5 6, 4 6, 3 4, 2 3), (3 0, 4 1, 2 1))
POLYGON ((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2))
MULTIPOLYGON (((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2)), ((3 5, 4 5, 5 6, 3 7, 2 6, 3 5)))
  • Simple: linestrings and polygons are built from points connected by straight line segments
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Simple Features

  • Standard for representing spatial vector data

  • Simple Feature geometries:

POINT (2 2)
MULTIPOINT ((0 1), (2 2), (4 1), (2 3), (1 4))
LINESTRING (1 1, 5 5, 5 6, 4 6, 3 4, 2 3)
MULTILINESTRING ((1 1, 5 5, 5 6, 4 6, 3 4, 2 3), (3 0, 4 1, 2 1))
POLYGON ((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2))
MULTIPOLYGON (((2 1, 3 1, 5 2, 6 3, 5 3, 4 4, 3 4, 1 3, 2 1), (2 2, 3 3, 4 3, 4 2, 2 2)), ((3 5, 4 5, 5 6, 3 7, 2 6, 3 5)))
  • Simple: linestrings and polygons are built from points connected by straight line segments
  • Features can have other non-geographic properties: attributes
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Package {sf}

  • Provides support for Simple Features in R

  • Represents Simple Features as records in a data frame:

us_states
Simple feature collection with 51 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 3
  fips_state name                           geometry
  <chr>      <chr>                <MULTIPOLYGON [m]>
1 04         Arizona    (((-805018.1 -844577, -8209…
2 05         Arkansas   (((558984.4 -1308375, 55528…
3 06         California (((-1857115 -997259, -18408…
4 08         Colorado   (((-340987 -435780.7, -2411…
# ℹ 47 more rows




  • sf object: data frame with spatial geometries

  • Every row is a single Simple Feature consisting of attributes and geometry

  • geometry: list column that contains geometries (class sfc)

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Package {sf}

  • Implements operations on Simple Feature geometries

  • st_*() functions that work on sf and sfc objects, following PostGIS convention

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Operations on Attributes

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Subsetting

(de_nuts3 <- read_sf("https://lo-ng.netlify.app/slides/data/de-nuts3.geojson"))
Simple feature collection with 401 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 401 × 3
   nuts  name                                                           geometry
   <chr> <chr>                                                <MULTIPOLYGON [m]>
 1 DE254 Nürnberg, Kreisfreie Stadt  (((4395582 2936363, 4406401 2929710, 44059…
 2 DE255 Schwabach, Kreisfreie Stadt (((4399488 2914611, 4399340 2912229, 43949…
 3 DE256 Ansbach, Landkreis          (((4384059 2916281, 4386305 2905631, 43820…
 4 DE257 Erlangen-Höchstadt          (((4387813 2962179, 4400140 2944483, 44135…
 5 DE234 Amberg-Sulzbach             (((4461918 2902744, 4434038 2924102, 44360…
 6 DE235 Cham                        (((4552189 2900592, 4548518 2895155, 45230…
 7 DE236 Neumarkt i. d. OPf.         (((4461918 2902744, 4462116 2899857, 44516…
 8 DE237 Neustadt a. d. Waldnaab     (((4508712 2939911, 4474093 2939442, 44601…
 9 DE238 Regensburg, Landkreis       (((4502290 2882921, 4497804 2865770, 44780…
10 DE239 Schwandorf                  (((4508712 2939911, 4511976 2937497, 45118…
# ℹ 391 more rows
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Subsetting

de_nuts3 |>
  filter(startsWith(name, "A"))
Simple feature collection with 19 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4091473 ymin: 2775241 xmax: 4539907 ymax: 3403586
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 19 × 3
   nuts  name                                                           geometry
 * <chr> <chr>                                                <MULTIPOLYGON [m]>
 1 DE256 Ansbach, Landkreis              (((4384059 2916281, 4386305 2905631, 4…
 2 DE234 Amberg-Sulzbach                 (((4461918 2902744, 4434038 2924102, 4…
 3 DE251 Ansbach, Kreisfreie Stadt       (((4356772 2910520, 4363699 2914647, 4…
 4 DE261 Aschaffenburg, Kreisfreie Stadt (((4266274 2980543, 4256895 2980211, 4…
 5 DE264 Aschaffenburg, Landkreis        (((4278414 2997441, 4279506 2974455, 4…
 6 DE271 Augsburg, Kreisfreie Stadt      (((4388654 2794282, 4381950 2800775, 4…
 7 DE145 Alb-Donau-Kreis                 (((4338058 2821924, 4330908 2815722, 4…
 8 DE214 Altötting                       (((4539907 2792493, 4525936 2781512, 4…
 9 DE231 Amberg, Kreisfreie Stadt        (((4449020 2927007, 4457142 2934255, 4…
10 DEG0M Altenburger Land                (((4504767 3099894, 4507488 3093634, 4…
11 DEB12 Ahrweiler                       (((4132529 3043937, 4106413 3030730, 4…
12 DEB13 Altenkirchen (Westerwald)       (((4169961 3092710, 4182511 3066932, 4…
13 DEB3B Alzey-Worms                     (((4205818 2966931, 4209134 2959072, 4…
14 DEE04 Altmarkkreis Salzwedel          (((4428877 3262216, 4420644 3255037, 4…
15 DEE05 Anhalt-Bitterfeld               (((4483990 3217678, 4477747 3209147, 4…
16 DE275 Aichach-Friedberg               (((4416814 2818744, 4418060 2816287, 4…
17 DE276 Augsburg, Landkreis             (((4389187 2830822, 4386413 2816502, 4…
18 DE946 Ammerland                       (((4208210 3345003, 4201080 3344123, 4…
19 DE947 Aurich                          (((4141718 3363568, 4136206 3369633, 4…
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Joining

de_nuts3_pop <- de_nuts3 |>
  left_join(
    # Downloaded from inkar.de
    read_csv("https://lo-ng.netlify.app/slides/data/de_pop20.csv"),
    by = join_by(nuts == nuts)
  )
Simple feature collection with 401 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 401 × 4
  nuts  name                     geometry  pop20
  <chr> <chr>          <MULTIPOLYGON [m]>  <dbl>
1 DE254 Nürnbe… (((4395582 2936363, 4406… 515543
2 DE255 Schwab… (((4399488 2914611, 4399…  41056
3 DE256 Ansbac… (((4384059 2916281, 4386… 185316
4 DE257 Erlang… (((4387813 2962179, 4400… 138105
5 DE234 Amberg… (((4461918 2902744, 4434… 102998
6 DE235 Cham    (((4552189 2900592, 4548… 128094
# ℹ 395 more rows
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Joining

de_nuts3_pop <- de_nuts3 |>
  left_join(
    # Downloaded from inkar.de
    read_csv("https://lo-ng.netlify.app/slides/data/de_pop20.csv"),
    by = join_by(nuts == nuts)
  )
Simple feature collection with 401 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 401 × 4
  nuts  name                     geometry  pop20
  <chr> <chr>          <MULTIPOLYGON [m]>  <dbl>
1 DE254 Nürnbe… (((4395582 2936363, 4406… 515543
2 DE255 Schwab… (((4399488 2914611, 4399…  41056
3 DE256 Ansbac… (((4384059 2916281, 4386… 185316
4 DE257 Erlang… (((4387813 2962179, 4400… 138105
5 DE234 Amberg… (((4461918 2902744, 4434… 102998
6 DE235 Cham    (((4552189 2900592, 4548… 128094
# ℹ 395 more rows

  • New since {dplyr} 1.1.0: by = join_by(...)

    Old syntax: by = c("nuts" = "nuts")

    Shortcut: by = join_by(nuts)
    (old syntax: by = "nuts")

  • Allows for other types of joins than equality joins (==)

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Aggregation

de_nuts1_pop <- de_nuts3_pop |>
  mutate(nuts = str_sub(nuts, 1, 3)) |>
  group_by(nuts) |>
  summarise(pop = sum(pop20))
Simple feature collection with 16 features and 2 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 16 × 3
  nuts       pop                                                        geometry
  <chr>    <dbl>                                                  <GEOMETRY [m]>
1 DE1   11103043 POLYGON ((4162515 2721450, 4148793 2716636, 4142867 2719236, 4…
2 DE2   13140183 POLYGON ((4287757 2714352, 4297839 2721916, 4325984 2728191, 4…
3 DE3    3664088 MULTIPOLYGON (((4565855 3276636, 4570054 3269038, 4574088 3265…
4 DE4    2531071 POLYGON ((4556978 3153409, 4544296 3148678, 4541206 3166243, 4…
# ℹ 12 more rows
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Aggregation

de_nuts1_pop <- de_nuts3_pop |>
  mutate(nuts = str_sub(nuts, 1, 3)) |>
  group_by(nuts) |>
  summarise(pop = sum(pop20))
Simple feature collection with 16 features and 2 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 16 × 3
  nuts       pop                                                        geometry
  <chr>    <dbl>                                                  <GEOMETRY [m]>
1 DE1   11103043 POLYGON ((4162515 2721450, 4148793 2716636, 4142867 2719236, 4…
2 DE2   13140183 POLYGON ((4287757 2714352, 4297839 2721916, 4325984 2728191, 4…
3 DE3    3664088 MULTIPOLYGON (((4565855 3276636, 4570054 3269038, 4574088 3265…
4 DE4    2531071 POLYGON ((4556978 3153409, 4544296 3148678, 4541206 3166243, 4…
# ℹ 12 more rows
  • {sf} silently creates a union out of geometries in the same group
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Operations on Simple Feature Geometries

Predicates Measures Transformers
Unary x x
Binary x x x
N-ary x
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Unary measures

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  • st_length(): length of a LINESTRING or MULTILINESTRING geometry
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  • st_length(): length of a LINESTRING or MULTILINESTRING geometry

  • st_area(): area of a geometry

us_states |>
  mutate(area = st_area(geometry))
Simple feature collection with 51 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 4
   fips_state name                          geometry    area
 * <chr>      <chr>               <MULTIPOLYGON [m]>   [m^2]
 1 04         Arizona      (((-805018.1 -844577, -8… 2.95e11
 2 05         Arkansas     (((558984.4 -1308375, 55… 1.37e11
 3 06         California   (((-1857115 -997259, -18… 4.09e11
 4 08         Colorado     (((-340987 -435780.7, -2… 2.70e11
 5 09         Connecticut  (((2279630 65808.41, 230… 1.30e10
 6 11         District of… (((1955464 -402054, 1960… 2.06e 8
 7 13         Georgia      (((1426586 -978019.1, 14… 1.53e11
 8 17         Illinois     (((998533.7 -204278.7, 9… 1.46e11
 9 18         Indiana      (((1305615 -535053.6, 13… 9.36e10
10 22         Louisiana    (((558984.4 -1308375, 72… 1.22e11
# ℹ 41 more rows
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Unary transformers

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st_centroid()

  us_states |>
  ggplot() +
  geom_sf() +
  theme_void()

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st_centroid()

us_centroids <-
  us_states |>
  st_centroid()
us_centroids |>
  ggplot() +
  geom_sf() +
  theme_void()

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st_point_on_surface()

bremen <- de_nuts1_pop |>
  filter(nuts == "DE5")
bremen |>
  ggplot() +
  geom_sf() +
  coord_sf(
    xlim = st_bbox(bremen)[c(1, 3)],
    ylim = st_bbox(bremen)[c(2, 4)]
  ) +
  theme_void()

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st_point_on_surface()

bremen <- de_nuts1_pop |>
  filter(nuts == "DE5")
bremen |>
  st_centroid() |>
  ggplot() +
  geom_sf() +
  coord_sf(
    xlim = st_bbox(bremen)[c(1, 3)],
    ylim = st_bbox(bremen)[c(2, 4)]
  ) +
  theme_void()

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st_point_on_surface()

bremen <- de_nuts1_pop |>
  filter(nuts == "DE5")
bremen |>
  st_point_on_surface() |>
  ggplot() +
  geom_sf() +
  coord_sf(
    xlim = st_bbox(bremen)[c(1, 3)],
    ylim = st_bbox(bremen)[c(2, 4)]
  ) +
  theme_void()

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st_buffer()

us_centroids |>
  ggplot() +
  geom_sf() +
  theme_void()

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st_buffer()

st_buffer(us_centroids, 50000) |>
  ggplot() +
  geom_sf() +
  theme_void()

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st_buffer()

st_buffer(us_centroids, 100000) |>
  ggplot() +
  geom_sf() +
  theme_void()

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st_buffer()

st_buffer(us_centroids, 200000) |>
  ggplot() +
  geom_sf() +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  #
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  mutate(geometry = geometry + c(-100000, 100000)) |>
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  mutate(geometry = geometry * 0.5) |>
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  mutate(geometry = geometry - us_centroids$geometry) |>
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  mutate(geometry = (geometry - us_centroids$geometry) * 0.5) |>
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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Affine transformations

Linear transformations that preserve lines and parallelism, but may change the orientation, scale, and position of objects

us_states |>
  mutate(geometry = (geometry - us_centroids$geometry) * 0.5 + us_centroids$geometry) |>
  ggplot() +
  geom_sf() +
  coord_sf(xlim = st_bbox(us_states)[c(1, 3)], ylim = st_bbox(us_states)[c(2, 4)]) +
  theme_void()

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st_cast(): Type transformations

us_states |>
  select(name) |>
  print(width = 48)
Simple feature collection with 51 features and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 2
   name                                 geometry
   <chr>                      <MULTIPOLYGON [m]>
 1 Arizona             (((-805018.1 -844577, -8…
 2 Arkansas            (((558984.4 -1308375, 55…
 3 California          (((-1857115 -997259, -18…
 4 Colorado            (((-340987 -435780.7, -2…
 5 Connecticut         (((2279630 65808.41, 230…
 6 District of Columb… (((1955464 -402054, 1960…
 7 Georgia             (((1426586 -978019.1, 14…
 8 Illinois            (((998533.7 -204278.7, 9…
 9 Indiana             (((1305615 -535053.6, 13…
10 Louisiana           (((558984.4 -1308375, 72…
# ℹ 41 more rows
26 / 54

st_cast(): Type transformations

us_states |>
  select(name) |>
  st_cast("MULTILINESTRING") |>
  print(width = 48)
Simple feature collection with 51 features and 1 field
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 2
   name                                 geometry
   <chr>                   <MULTILINESTRING [m]>
 1 Arizona             ((-805018.1 -844577, -82…
 2 Arkansas            ((558984.4 -1308375, 555…
 3 California          ((-1857115 -997259, -184…
 4 Colorado            ((-340987 -435780.7, -24…
 5 Connecticut         ((2279630 65808.41, 2301…
 6 District of Columb… ((1955464 -402054, 19602…
 7 Georgia             ((1426586 -978019.1, 148…
 8 Illinois            ((998533.7 -204278.7, 99…
 9 Indiana             ((1305615 -535053.6, 130…
10 Louisiana           ((558984.4 -1308375, 729…
# ℹ 41 more rows
26 / 54

st_cast(): Type transformations

us_states |>
  select(name) |>
  st_cast("MULTILINESTRING") |>
  mutate(
    border_len = st_length(geometry)
  ) |>
  arrange(desc(border_len)) |>
  print(width = 48)
Simple feature collection with 51 features and 2 fields
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: -2036903 ymin: -2465746 xmax: 2521918 ymax: 731860.1
Projected CRS: NAD27 / US National Atlas Equal Area
# A tibble: 51 × 3
   name                      geometry border_len
   <chr>        <MULTILINESTRING [m]>        [m]
 1 Alaska   ((-1516285 -2108476, -15…   6138253.
 2 Texas    ((513575.4 -1242958, 526…   4735889.
 3 Califor… ((-1857115 -997259, -184…   3606845.
 4 Michigan ((1115493 184361.4, 1124…   3499197.
 5 Montana  ((-875690.5 1111.16, -88…   2825510.
 6 Florida  ((1876927 -2050256, 1894…   2776267.
 7 Idaho    ((-875690.5 1111.16, -91…   2566386.
 8 Minneso… ((708640.3 -128513.3, 58…   2534621.
 9 New Mex… ((-621546.9 -1440456, -7…   2374297.
10 Virginia ((2138105 -453486.6, 213…   2343343.
# ℹ 41 more rows
26 / 54

Binary predicates (and spatial joins)

27 / 54

Topological relations: spatial relationships between two objects

st_*(x, y) → boolean

(Geocomputation with R, Chapter 4)

28 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_intersects,
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

Nothing happened 🤔

29 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_intersects,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

30 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_within,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

31 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_touches,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

32 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_disjoint,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

33 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  de_nuts3,
  bavaria,
  join = st_is_within_distance, dist = 100000,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

34 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  st_centroid(de_nuts3),
  bavaria,
  join = st_is_within_distance, dist = 100000,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

35 / 54

Spatial joins

bavaria <- de_nuts1_pop |>
  filter(nuts == "DE2")
st_join(
  st_centroid(de_nuts3),
  bavaria,
  join = negate(st_is_within_distance),
  dist = 100000,
  left = FALSE # Performs inner join
) |>
  ggplot() +
  geom_sf() +
  geom_sf(data = bavaria, linewidth = 1, fill = NA) +
  coord_sf(xlim = st_bbox(de_nuts3)[c(1, 3)], ylim = st_bbox(de_nuts3)[c(2, 4)]) +
  theme_void()

36 / 54

Binary measures

37 / 54

st_distance()

Returns a numeric matrix of length(x) rows and length(y) columns

bielefeld <- de_nuts3 |>
  filter(str_detect(name, "Bielefeld"))
de_nuts3 |>
  mutate(dist = st_distance(
    x = geometry,
    y = bielefeld
  )[, 1]) |>
  arrange(dist) |>
  print(width = 60)
Simple feature collection with 401 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 401 × 4
   nuts  name                                geometry   dist
   <chr> <chr>                     <MULTIPOLYGON [m]>    [m]
 1 DEA41 Bielefeld, Kreisf… (((4228708 3217545, 4225…     0 
 2 DEA42 Gütersloh          (((4216273 3222309, 4214…     0 
 3 DEA43 Herford            (((4247622 3231061, 4228…     0 
 4 DEA45 Lippe              (((4263781 3220253, 4273…     0 
 5 DE94E Osnabrück, Landkr… (((4206125 3266872, 4205…  4471.
 6 DEA47 Paderborn          (((4249193 3187118, 4250…  9360.
 7 DEA46 Minden-Lübbecke    (((4261478 3256200, 4252… 17729.
 8 DEA38 Warendorf          (((4190451 3218063, 4195… 20998.
 9 DEA5B Soest              (((4210655 3179982, 4221… 26458.
10 DE928 Schaumburg         (((4283343 3240666, 4265… 27191.
# ℹ 391 more rows
38 / 54

st_distance()

Returns a numeric matrix of length(x) rows and length(y) columns

bielefeld <- de_nuts3 |>
  filter(str_detect(name, "Bielefeld"))
de_nuts3 |>
  mutate(dist = st_distance(
    x = geometry,
    y = st_centroid(bielefeld)
  )[, 1]) |>
  arrange(dist) |>
  print(width = 60)
Simple feature collection with 401 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 4031952 ymin: 2684075 xmax: 4671180 ymax: 3543086
Projected CRS: ETRS89-extended / LAEA Europe
# A tibble: 401 × 4
   nuts  name                                geometry   dist
   <chr> <chr>                     <MULTIPOLYGON [m]>    [m]
 1 DEA41 Bielefeld, Kreisf… (((4228708 3217545, 4225…     0 
 2 DEA42 Gütersloh          (((4216273 3222309, 4214…  5866.
 3 DEA45 Lippe              (((4263781 3220253, 4273…  6058.
 4 DEA43 Herford            (((4247622 3231061, 4228…  7301.
 5 DE94E Osnabrück, Landkr… (((4206125 3266872, 4205… 14625.
 6 DEA47 Paderborn          (((4249193 3187118, 4250… 19621.
 7 DEA46 Minden-Lübbecke    (((4261478 3256200, 4252… 25521.
 8 DEA38 Warendorf          (((4190451 3218063, 4195… 28652.
 9 DEA5B Soest              (((4210655 3179982, 4221… 34279.
10 DE928 Schaumburg         (((4283343 3240666, 4265… 36136.
# ℹ 391 more rows
39 / 54

Binary & n-ary transformers

40 / 54
st_buffer(us_centroids, 100000) |>
  filter(name %in% c("Massachusetts", "Connecticut")) |>
  st_intersection() |>
  ggplot() +
  geom_sf(aes(fill = factor(n.overlaps)), show.legend = FALSE) +
  geom_sf_text(aes(label = n.overlaps)) +
  theme_void()

#
st_buffer(us_centroids, 100000) |>
  filter(name %in% c("Massachusetts", "Connecticut")) |>
  st_union() |>
  ggplot() +
  geom_sf() +
  theme_void()

st_buffer(us_centroids, 100000) |>
  filter(name %in% c("Massachusetts", "Connecticut")) |>
  st_difference() |>
  slice(2) |>
  ggplot() +
  geom_sf() +
  theme_void()

st_buffer(us_centroids, 100000) |>
  filter(name %in% c("Massachusetts", "Connecticut")) |>
  st_geometry() |>
  do.call(what = st_sym_difference) |>
  ggplot() +
  geom_sf() +
  theme_void()

41 / 54

Operations on Raster Data

42 / 54

Raster

library(terra)
(de_elevation <- rast(
  "https://lo-ng.netlify.app/slides/data/de_elevation.tif"
))
class       : SpatRaster 
dimensions  : 119, 140, 1  (nrow, ncol, nlyr)
resolution  : 0.07145545, 0.07145545  (x, y)
extent      : 5.50207, 15.50583, 47.00592, 55.50912  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : de_elevation.tif 
name        : elev 
min value   :  -94 
max value   : 2759
plot(de_elevation)

43 / 54

Reprojection of NUTS-3 polygons to work with elevation raster

(de_nuts3 <- de_nuts3 |>
  st_transform(crs(de_elevation)))
Simple feature collection with 401 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5.877085 ymin: 47.27011 xmax: 15.02282 ymax: 54.98211
Geodetic CRS:  WGS 84
# A tibble: 401 × 3
   nuts  name                                                           geometry
 * <chr> <chr>                                                <MULTIPOLYGON [°]>
 1 DE254 Nürnberg, Kreisfreie Stadt  (((11.03021 49.53537, 11.17818 49.47409, 1…
 2 DE255 Schwabach, Kreisfreie Stadt (((11.07981 49.33928, 11.07731 49.31788, 1…
 3 DE256 Ansbach, Landkreis          (((10.86784 49.35613, 10.89699 49.26011, 1…
 4 DE257 Erlangen-Höchstadt          (((10.92734 49.76843, 11.09481 49.6078, 11…
 5 DE234 Amberg-Sulzbach             (((11.93422 49.22107, 11.55776 49.41904, 1…
 6 DE235 Cham                        (((13.17091 49.17358, 13.11751 49.12614, 1…
 7 DE236 Neumarkt i. d. OPf.         (((11.93422 49.22107, 11.9359 49.19507, 11…
 8 DE237 Neustadt a. d. Waldnaab     (((12.59378 49.54219, 12.11549 49.54795, 1…
 9 DE238 Regensburg, Landkreis       (((12.47903 49.03199, 12.4102 48.87916, 12…
10 DE239 Schwandorf                  (((12.59378 49.54219, 12.63766 49.51946, 1…
# ℹ 391 more rows
44 / 54

Cropping & masking

bavaria <- st_transform(bavaria, crs(de_elevation))
de_elevation |>
  crop(bavaria) |>
  #
  plot()

45 / 54

Cropping & masking

bavaria <- st_transform(bavaria, crs(de_elevation))
de_elevation |>
  crop(bavaria) |>
  mask(bavaria) |>
  plot()

46 / 54

Extraction

de_elevation
class       : SpatRaster 
dimensions  : 119, 140, 1  (nrow, ncol, nlyr)
resolution  : 0.07145545, 0.07145545  (x, y)
extent      : 5.50207, 15.50583, 47.00592, 55.50912  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : de_elevation.tif 
name        : elev 
min value   :  -94 
max value   : 2759
46 / 54

Extraction

de_elevation |>
  extract(bavaria)
     ID elev
1     1  626
2     1  474
3     1  375
4     1  366
5     1  624
6     1  616
7     1  658
8     1  630
9     1  488
10    1  385
11    1  325
12    1  301
13    1  335
14    1  346
15    1  568
16    1  602
17    1  609
18    1  635
19    1  555
20    1  558
21    1  539
22    1  554
23    1  464
24    1  526
25    1  596
26    1  469
27    1  391
28    1  325
29    1  297
30    1  310
31    1  314
32    1  317
33    1  314
34    1  325
35    1  357
36    1  372
37    1  381
38    1  383
39    1  415
40    1  460
41    1  514
42    1  516
43    1  593
44    1  611
45    1  600
46    1  582
47    1  565
48    1  523
49    1  534
50    1  564
51    1  388
52    1  414
53    1  431
54    1  449
55    1  360
56    1  312
57    1  309
58    1  316
59    1  313
60    1  315
61    1  360
62    1  355
63    1  348
64    1  334
65    1  335
66    1  337
67    1  355
68    1  342
69    1  345
70    1  370
71    1  408
72    1  457
73    1  522
74    1  573
75    1  618
76    1  619
77    1  588
78    1  571
79    1  533
80    1  562
81    1  595
82    1  619
83    1  393
84    1  329
85    1  338
86    1  349
87    1  335
88    1  299
89    1  298
90    1  301
91    1  323
92    1  341
93    1  343
94    1  343
95    1  351
96    1  339
97    1  343
98    1  356
99    1  338
100   1  337
101   1  324
102   1  322
103   1  325
104   1  322
105   1  318
106   1  325
107   1  350
108   1  390
109   1  432
110   1  454
111   1  505
112   1  564
113   1  585
114   1  578
115   1  596
116   1  603
117   1  605
118   1  593
119   1  588
120   1  348
121   1  416
122   1  420
123   1  313
124   1  291
125   1  289
126   1  281
127   1  268
128   1  302
129   1  313
130   1  298
131   1  301
132   1  337
133   1  354
134   1  340
135   1  294
136   1  312
137   1  369
138   1  345
139   1  328
140   1  312
141   1  302
142   1  321
143   1  377
144   1  415
145   1  389
146   1  382
147   1  375
148   1  363
149   1  397
150   1  406
151   1  474
152   1  551
153   1  627
154   1  715
155   1  646
156   1  597
157   1  572
158   1  556
159   1  536
160   1  162
161   1  256
162   1  228
163   1  320
164   1  376
165   1  377
166   1  356
167   1  372
168   1  298
169   1  268
170   1  266
171   1  276
172   1  289
173   1  294
174   1  283
175   1  251
176   1  231
177   1  247
178   1  288
179   1  301
180   1  275
181   1  293
182   1  346
183   1  350
184   1  326
185   1  297
186   1  286
187   1  334
188   1  449
189   1  504
190   1  468
191   1  462
192   1  437
193   1  376
194   1  369
195   1  382
196   1  429
197   1  552
198   1  707
199   1  782
200   1  661
201   1  612
202   1  581
203   1  557
204   1  545
205   1  551
206   1  145
207   1  164
208   1  230
209   1  317
210   1  389
211   1  422
212   1  401
213   1  324
214   1  292
215   1  271
216   1  254
217   1  261
218   1  303
219   1  292
220   1  269
221   1  247
222   1  229
223   1  223
224   1  242
225   1  296
226   1  332
227   1  345
228   1  335
229   1  308
230   1  301
231   1  278
232   1  283
233   1  336
234   1  433
235   1  500
236   1  464
237   1  456
238   1  465
239   1  459
240   1  398
241   1  383
242   1  421
243   1  524
244   1  640
245   1  677
246   1  614
247   1  623
248   1  628
249   1  601
250   1  547
251   1  526
252   1  557
253   1  605
254   1  172
255   1  237
256   1  333
257   1  403
258   1  430
259   1  397
260   1  289
261   1  261
262   1  279
263   1  267
264   1  260
265   1  306
266   1  300
267   1  283
268   1  263
269   1  244
270   1  231
271   1  238
272   1  333
273   1  387
274   1  389
275   1  356
276   1  324
277   1  299
278   1  280
279   1  272
280   1  322
281   1  448
282   1  492
283   1  459
284   1  434
285   1  434
286   1  456
287   1  475
288   1  450
289   1  439
290   1  462
291   1  501
292   1  513
293   1  528
294   1  607
295   1  615
296   1  560
297   1  504
298   1  513
299   1  536
300   1  613
301   1  672
302   1  659
303   1  229
304   1  214
305   1  304
306   1  346
307   1  344
308   1  306
309   1  238
310   1  244
311   1  281
312   1  296
313   1  283
314   1  257
315   1  261
316   1  283
317   1  256
318   1  226
319   1  230
320   1  258
321   1  363
322   1  389
323   1  372
324   1  343
325   1  323
326   1  306
327   1  296
328   1  282
329   1  297
330   1  365
331   1  438
332   1  435
333   1  437
334   1  442
335   1  456
336   1  515
337   1  504
338   1  501
339   1  484
340   1  460
341   1  459
342   1  486
343   1  555
344   1  546
345   1  500
346   1  493
347   1  529
348   1  580
349   1  660
350   1  689
351   1  230
352   1  287
353   1  307
354   1  301
355   1  323
356   1  322
357   1  281
358   1  254
359   1  272
360   1  241
361   1  223
362   1  262
363   1  319
364   1  380
365   1  387
366   1  366
367   1  341
368   1  322
369   1  302
370   1  293
371   1  298
372   1  301
373   1  294
374   1  358
375   1  434
376   1  474
377   1  484
378   1  481
379   1  482
380   1  471
381   1  509
382   1  505
383   1  462
384   1  450
385   1  443
386   1  477
387   1  485
388   1  472
389   1  472
390   1  532
391   1  645
392   1  705
393   1  276
394   1  338
395   1  351
396   1  353
397   1  320
398   1  299
399   1  272
400   1  270
401   1  254
402   1  260
403   1  306
404   1  353
405   1  347
406   1  359
407   1  357
408   1  334
409   1  311
410   1  308
411   1  300
412   1  304
413   1  306
414   1  298
415   1  358
416   1  435
417   1  487
418   1  515
419   1  517
420   1  469
421   1  448
422   1  477
423   1  478
424   1  449
425   1  443
426   1  434
427   1  431
428   1  429
429   1  437
430   1  464
431   1  529
432   1  599
433   1  619
434   1  419
435   1  378
436   1  390
437   1  309
438   1  295
439   1  302
440   1  302
441   1  312
442   1  339
443   1  354
444   1  338
445   1  331
446   1  333
447   1  340
448   1  344
449   1  346
450   1  332
451   1  309
452   1  298
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1524  1  661
1525  1  480
1526  1  860
1527  1  872
1528  1  757
1529  1  719
1530  1  836
1531  1  827
1532  1  786
1533  1  774
1534  1  807
1535  1  802
1536  1  780
1537  1  777
1538  1  769
1539  1  719
1540  1  642
1541  1  666
1542  1  642
1543  1  619
1544  1  672
1545  1  870
1546  1  748
1547  1  855
1548  1  891
1549  1  849
1550  1  931
1551  1  872
1552  1  905
1553  1  755
1554  1  633
1555  1  787
1556  1  998
1557  1 1007
1558  1  845
1559  1  852
1560  1 1050
1561  1  916
1562  1  971
1563  1  946
1564  1  816
1565  1  708
1566  1  905
1567  1  907
1568  1  788
1569  1  768
1570  1  893
1571  1  891
1572  1  845
1573  1  816
1574  1  822
1575  1  771
1576  1  885
1577  1  937
1578  1  913
1579  1  858
1580  1  703
1581  1  708
1582  1  737
1583  1  787
1584  1  888
1585  1 1104
1586  1  853
1587  1 1020
1588  1 1090
1589  1 1038
1590  1 1193
1591  1 1149
1592  1 1166
1593  1 1053
1594  1  843
1595  1  706
1596  1  902
1597  1  980
1598  1 1138
1599  1 1117
1600  1 1038
1601  1 1037
1602  1 1013
1603  1  970
1604  1  963
1605  1  593
1606  1  699
1607  1  719
1608  1  775
1609  1  875
1610  1  951
1611  1  911
1612  1  810
1613  1  996
1614  1 1046
1615  1 1054
1616  1  995
1617  1  898
1618  1  842
1619  1 1001
1620  1 1208
1621  1 1244
1622  1 1166
1623  1 1089
1624  1  880
1625  1 1024
1626  1 1012
1627  1  972
1628  1 1012
1629  1 1044
1630  1 1002
1631  1 1100
1632  1 1187
1633  1 1222
1634  1 1251
1635  1 1262
1636  1 1187
1637  1 1372
1638  1 1205
1639  1  963
1640  1 1372
1641  1 1070
1642  1 1168
1643  1 1070
1644  1  873
1645  1 1131
1646  1 1232
1647  1 1405
1648  1 1363
1649  1 1229
1650  1 1131
1651  1 1040
1652  1 1161
1653  1 1165
1654  1 1156
1655  1 1205
1656  1 1208
1657  1 1249
1658  1 1687
1659  1 1470
1660  1 1755
1661  1 1202
1662  1 1307
1663  1 1190
1664  1  965
1665  1 1360
1666  1 1447
1667  1 1428
1668  1 1437
1669  1 1348
1670  1 1169
1671  1 1292
1672  1 1530
1673  1 1659
1674  1 1388
1675  1 1264
1676  1 1202
1677  1 1640
1678  1 1694
1679  1 1647
46 / 54

Extraction

de_elevation |>
  extract(bavaria) |>
  summarise(across(elev, lst(min, max, mean)))
  elev_min elev_max elev_mean
1      145     1755   517.601
46 / 54

Extraction

(de_nuts1 <- de_nuts3 |>
  group_by(nuts = str_sub(nuts, 1, 3)) |>
  summarise(geometry = st_union(geometry)))
Simple feature collection with 16 features and 1 field
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 5.877085 ymin: 47.27011 xmax: 15.02282 ymax: 54.98211
Geodetic CRS:  WGS 84
# A tibble: 16 × 2
   nuts                                                                 geometry
   <chr>                                                          <GEOMETRY [°]>
 1 DE1   POLYGON ((9.182192 47.65589, 9.364413 47.62806, 9.495604 47.55145, 9.5…
 2 DE2   POLYGON ((10.7292 50.23001, 10.61011 50.22799, 10.58959 50.31986, 10.4…
 3 DE3   POLYGON ((13.39854 52.64819, 13.16426 52.5989, 13.13901 52.50206, 13.1…
 4 DE4   POLYGON ((12.98468 53.16499, 12.92016 53.19372, 12.8793 53.18263, 12.8…
 5 DE5   MULTIPOLYGON (((8.585106 53.20144, 8.48533 53.22712, 8.654899 53.10886…
 6 DE6   MULTIPOLYGON (((10.30795 53.4332, 10.23668 53.49635, 10.17859 53.53497…
 7 DE7   POLYGON ((8.99056 50.06712, 9.165176 50.10605, 9.270941 50.14205, 9.40…
 8 DE8   POLYGON ((12.33175 53.31801, 12.74191 53.20144, 12.77713 53.18877, 12.…
 9 DE9   MULTIPOLYGON (((6.930503 53.68451, 6.848968 53.69901, 6.809192 53.6793…
10 DEA   POLYGON ((7.785898 50.93991, 7.851496 50.92583, 8.03969 50.69738, 8.12…
11 DEB   POLYGON ((8.414774 49.59505, 8.37993 49.68695, 8.446378 49.7308, 8.448…
12 DEC   POLYGON ((6.556986 49.41921, 6.723466 49.21883, 6.784494 49.16084, 6.9…
13 DED   POLYGON ((13.65217 50.73036, 13.82971 50.73285, 14.36331 50.90343, 14.…
14 DEE   POLYGON ((11.00878 52.49675, 10.93454 52.47179, 11.05106 52.37008, 11.…
15 DEF   MULTIPOLYGON (((7.940571 54.20555, 7.888383 54.23613, 7.800922 54.2111…
16 DEG   POLYGON ((10.38003 51.57562, 10.26577 51.48461, 9.928339 51.3753, 9.98…
47 / 54

Extraction

de_nuts1 |>
  print(width = 44)
Simple feature collection with 16 features and 1 field
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 5.877085 ymin: 47.27011 xmax: 15.02282 ymax: 54.98211
Geodetic CRS:  WGS 84
# A tibble: 16 × 2
   nuts                             geometry
   <chr>                      <GEOMETRY [°]>
 1 DE1   POLYGON ((9.182192 47.65589, 9.364…
 2 DE2   POLYGON ((10.7292 50.23001, 10.610…
 3 DE3   POLYGON ((13.39854 52.64819, 13.16…
 4 DE4   POLYGON ((12.98468 53.16499, 12.92…
 5 DE5   MULTIPOLYGON (((8.585106 53.20144,…
 6 DE6   MULTIPOLYGON (((10.30795 53.4332, …
 7 DE7   POLYGON ((8.99056 50.06712, 9.1651…
 8 DE8   POLYGON ((12.33175 53.31801, 12.74…
 9 DE9   MULTIPOLYGON (((6.930503 53.68451,…
10 DEA   POLYGON ((7.785898 50.93991, 7.851…
11 DEB   POLYGON ((8.414774 49.59505, 8.379…
12 DEC   POLYGON ((6.556986 49.41921, 6.723…
13 DED   POLYGON ((13.65217 50.73036, 13.82…
14 DEE   POLYGON ((11.00878 52.49675, 10.93…
15 DEF   MULTIPOLYGON (((7.940571 54.20555,…
16 DEG   POLYGON ((10.38003 51.57562, 10.26…
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  print(width = 44)
Simple feature collection with 16 features and 2 fields
Geometry type: GEOMETRY
Dimension:     XY
Bounding box:  xmin: 5.877085 ymin: 47.27011 xmax: 15.02282 ymax: 54.98211
Geodetic CRS:  WGS 84
# A tibble: 16 × 3
   nuts                   geometry elev_rast
 * <chr>            <GEOMETRY [°]> <list>   
 1 DE1   POLYGON ((9.182192 47.65… <tibble> 
 2 DE2   POLYGON ((10.7292 50.230… <tibble> 
 3 DE3   POLYGON ((13.39854 52.64… <tibble> 
 4 DE4   POLYGON ((12.98468 53.16… <tibble> 
 5 DE5   MULTIPOLYGON (((8.585106… <tibble> 
 6 DE6   MULTIPOLYGON (((10.30795… <tibble> 
 7 DE7   POLYGON ((8.99056 50.067… <tibble> 
 8 DE8   POLYGON ((12.33175 53.31… <tibble> 
 9 DE9   MULTIPOLYGON (((6.930503… <tibble> 
10 DEA   POLYGON ((7.785898 50.93… <tibble> 
11 DEB   POLYGON ((8.414774 49.59… <tibble> 
12 DEC   POLYGON ((6.556986 49.41… <tibble> 
13 DED   POLYGON ((13.65217 50.73… <tibble> 
14 DEE   POLYGON ((11.00878 52.49… <tibble> 
15 DEF   MULTIPOLYGON (((7.940571… <tibble> 
16 DEG   POLYGON ((10.38003 51.57… <tibble>
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  st_drop_geometry() |>
  print(width = 44)
# A tibble: 16 × 2
   nuts  elev_rast           
 * <chr> <list>              
 1 DE1   <tibble [866 × 1]>  
 2 DE2   <tibble [1,679 × 1]>
 3 DE3   <tibble [24 × 1]>   
 4 DE4   <tibble [765 × 1]>  
 5 DE5   <tibble [10 × 1]>   
 6 DE6   <tibble [19 × 1]>   
 7 DE7   <tibble [524 × 1]>  
 8 DE8   <tibble [617 × 1]>  
 9 DE9   <tibble [1,253 × 1]>
10 DEA   <tibble [867 × 1]>  
11 DEB   <tibble [485 × 1]>  
12 DEC   <tibble [61 × 1]>   
13 DED   <tibble [464 × 1]>  
14 DEE   <tibble [529 × 1]>  
15 DEF   <tibble [415 × 1]>  
16 DEG   <tibble [404 × 1]>
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  st_drop_geometry() |>
  mutate(elev_sum = map(elev_rast, \(x) {
    summarise(x, across(elev, lst(min, max, mean)))
  })) |>
  print(width = 44)
# A tibble: 16 × 3
   nuts  elev_rast            elev_sum
   <chr> <list>               <list>  
 1 DE1   <tibble [866 × 1]>   <tibble>
 2 DE2   <tibble [1,679 × 1]> <tibble>
 3 DE3   <tibble [24 × 1]>    <tibble>
 4 DE4   <tibble [765 × 1]>   <tibble>
 5 DE5   <tibble [10 × 1]>    <tibble>
 6 DE6   <tibble [19 × 1]>    <tibble>
 7 DE7   <tibble [524 × 1]>   <tibble>
 8 DE8   <tibble [617 × 1]>   <tibble>
 9 DE9   <tibble [1,253 × 1]> <tibble>
10 DEA   <tibble [867 × 1]>   <tibble>
11 DEB   <tibble [485 × 1]>   <tibble>
12 DEC   <tibble [61 × 1]>    <tibble>
13 DED   <tibble [464 × 1]>   <tibble>
14 DEE   <tibble [529 × 1]>   <tibble>
15 DEF   <tibble [415 × 1]>   <tibble>
16 DEG   <tibble [404 × 1]>   <tibble>
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  st_drop_geometry() |>
  mutate(elev_sum = map(elev_rast, \(x) {
    summarise(x, across(elev, lst(min, max, mean)))
  })) |>
  select(-elev_rast) |>
  print(width = 44)
# A tibble: 16 × 2
   nuts  elev_sum        
   <chr> <list>          
 1 DE1   <tibble [1 × 3]>
 2 DE2   <tibble [1 × 3]>
 3 DE3   <tibble [1 × 3]>
 4 DE4   <tibble [1 × 3]>
 5 DE5   <tibble [1 × 3]>
 6 DE6   <tibble [1 × 3]>
 7 DE7   <tibble [1 × 3]>
 8 DE8   <tibble [1 × 3]>
 9 DE9   <tibble [1 × 3]>
10 DEA   <tibble [1 × 3]>
11 DEB   <tibble [1 × 3]>
12 DEC   <tibble [1 × 3]>
13 DED   <tibble [1 × 3]>
14 DEE   <tibble [1 × 3]>
15 DEF   <tibble [1 × 3]>
16 DEG   <tibble [1 × 3]>
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  st_drop_geometry() |>
  mutate(elev_sum = map(elev_rast, \(x) {
    summarise(x, across(elev, lst(min, max, mean)))
  })) |>
  select(-elev_rast) |>
  unnest_wider(elev_sum) |>
  print(width = 44)
# A tibble: 16 × 4
   nuts  elev_min elev_max elev_mean
   <chr>    <int>    <int>     <dbl>
 1 DE1         90     1074     498. 
 2 DE2        145     1755     518. 
 3 DE3         39       51      43.7
 4 DE4          1      156      63.8
 5 DE5         -1       17       4.8
 6 DE6         -5       48      15.3
 7 DE7         83      737     302. 
 8 DE8         -4      116      38.5
 9 DE9         -8      714      71.7
10 DEA         14      679     179. 
11 DEB         84      590     321. 
12 DEC        212      486     313. 
13 DED         81      891     305. 
14 DEE         19      580     116. 
15 DEF        -21       78      19.9
16 DEG        148      740     367.
47 / 54

Extraction

de_nuts1 |>
  bind_cols(
    extract(de_elevation, de_nuts1) |>
      nest(elev_rast = -ID) |>
      select(-ID)
  ) |>
  st_drop_geometry() |>
  mutate(elev_sum = map(elev_rast, \(x) {
    summarise(x, across(elev, lst(min, max, mean)))
  })) |>
  select(-elev_rast) |>
  unnest_wider(elev_sum) |>
  arrange(desc(elev_mean)) |>
  print(width = 44)
# A tibble: 16 × 4
   nuts  elev_min elev_max elev_mean
   <chr>    <int>    <int>     <dbl>
 1 DE2        145     1755     518. 
 2 DE1         90     1074     498. 
 3 DEG        148      740     367. 
 4 DEB         84      590     321. 
 5 DEC        212      486     313. 
 6 DED         81      891     305. 
 7 DE7         83      737     302. 
 8 DEA         14      679     179. 
 9 DEE         19      580     116. 
10 DE9         -8      714      71.7
11 DE4          1      156      63.8
12 DE3         39       51      43.7
13 DE8         -4      116      38.5
14 DEF        -21       78      19.9
15 DE6         -5       48      15.3
16 DE5         -1       17       4.8
47 / 54

References

48 / 54

Thank you!


long39ng

https://lo-ng.netlify.app

long.nguyen@uni-bielefeld.de

49 / 54

Appendix: Data Preparation

50 / 54

GeoJSON of US states in AlbersUSA projection

library(sf)
library(tidyverse)
us_states_albers <- read_rds(
  "https://github.com/hrbrmstr/albersusa/raw/master/inst/extdata/states_sf.rda"
) |>
  select(fips_state, name) |>
  st_transform("EPSG:2163") |>
  rmapshaper::ms_simplify()
write_sf(us_states_albers, "slides/data/us-states-albers.geojson")
51 / 54

US 2020 presidential election results

us_pop <- httr::GET(
  "https://api.census.gov/data/2018/pep/population?get=POP&for=state:*"
) |>
  httr::content() |>
  tail(-1) |>
  map(set_names, "pop18", "fips_state") |>
  bind_rows() |>
  mutate(pop18 = as.numeric(pop18))
us_votes <- read_csv(
  "https://github.com/walkerke/census-with-r-book/raw/master/data/us_vote_2020.csv"
) |>
  select(state, called) |>
  left_join(x = st_drop_geometry(us_states_albers), by = join_by(name == state)) |>
  left_join(us_pop, by = join_by(fips_state))
write_rds(us_votes, "slides/data/us_votes.rds")
52 / 54

GeoJSON of German districts (NUTS-3)

giscoR::gisco_get_nuts(epsg = "3035", country = "DE", nuts_level = 3) |>
  select(nuts = NUTS_ID, name = NAME_LATN) |>
  write_sf("slides/data/de-nuts3.geojson")
53 / 54

Germany DEM raster

IsoriX::ElevRasterDE |>
  rast() |>
  `names<-`("elev") |>
  writeRaster("slides/data/de_elevation.tif", datatype = "INT2S")
54 / 54
2 / 54
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