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CS&SS 569 Visualizing Data and Models

Lab 6: Visualizing spatial data

Ramses Llobet

Department of Political Science, UW

2025-02-14

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Introduction

  • A long rant on space: What do we mean when we say visualizing "spatial data"?
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Introduction

  • A long rant on space: What do we mean when we say visualizing "spatial data"?

  1. Physical world

    • Physical, spatial features: geographical area, features, contours, etc
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Introduction

  • A long rant on space: What do we mean when we say visualizing "spatial data"?

  1. Physical world

    • Physical, spatial features: geographical area, features, contours, etc

  2. Social world

    • Some socio-economic-political variables that could be mapped onto physical, geographical features

    • E.g., district-level income level, crime rate, education spending, etc

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Introduction

  • A long rant on space: What do we mean when we say visualizing "spatial data"?

  1. Physical world

    • Physical, spatial features: geographical area, features, contours, etc

  2. Social world

    • Some socio-economic-political variables that could be mapped onto physical, geographical features

    • E.g., district-level income level, crime rate, education spending, etc

  3. Graphical space as dimension(s) to express some variables

    • E.g., x and y coordinates on our 2D canvas
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Introduction

  • We can project (1) physical space onto (3) graphical space

    • Traditional understanding of "map"
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Introduction

  • We can project (1) physical space onto (3) graphical space

    • Traditional understanding of "map"

  • We can project (2) social variables directly onto (3) graphical space

    • Bubble plot uses size/area to express magnitude; bar plot uses length to express magnitude
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Introduction

  • We can project (1) physical space onto (3) graphical space

    • Traditional understanding of "map"

  • We can project (2) social variables directly onto (3) graphical space

    • Bubble plot uses size/area to express magnitude; bar plot uses length to express magnitude

  • Problems start to occur when we project (2) social variables onto (1) physical space, then onto (3) graphical space

    • Let's say we want to visualize some district-level covariates
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Introduction

  • We can project (1) physical space onto (3) graphical space

    • Traditional understanding of "map"

  • We can project (2) social variables directly onto (3) graphical space

    • Bubble plot uses size/area to express magnitude; bar plot uses length to express magnitude

  • Problems start to occur when we project (2) social variables onto (1) physical space, then onto (3) graphical space

    • Let's say we want to visualize some district-level covariates

    • Problem 1: incongruity b/w social space and physical space

    • We visualize district-level variables onto the whole WA state; too much extrapolation

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Introduction

  • We can project (1) physical space onto (3) graphical space

    • Traditional understanding of "map"

  • We can project (2) social variables directly onto (3) graphical space

    • Bubble plot uses size/area to express magnitude; bar plot uses length to express magnitude

  • Problems start to occur when we project (2) social variables onto (1) physical space, then onto (3) graphical space

    • Let's say we want to visualize some district-level covariates

    • Problem 1: incongruity b/w social space and physical space

    • We visualize district-level variables onto the whole WA state; too much extrapolation

    • Problem 2: why do we prioritize physical space?

    • E.g., why a vast piece of land w/ little people should be prioritized over a small but densely populated area?

    • Can we use variables other than physical space to mediate (2) and (3)?

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(In)famous US election map...

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... inspires many advances in visualizing spatial data

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But the problem is more common than you think...

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Roadmap

  • Cover different ways to visualize spatial data:
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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

    • Brief introduction to spatial data file (e.g., shapefile; .shp)
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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

    • Brief introduction to spatial data file (e.g., shapefile; .shp)

    • package sf to wrangle spatial data and tmap to visualize map

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

    • Brief introduction to spatial data file (e.g., shapefile; .shp)

    • package sf to wrangle spatial data and tmap to visualize map

    • Interactive maps; small multiples in choropleth maps

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

    • Brief introduction to spatial data file (e.g., shapefile; .shp)

    • package sf to wrangle spatial data and tmap to visualize map

    • Interactive maps; small multiples in choropleth maps

  • Be mindful of the issues of space(s) and the curse of dimensionality

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Roadmap

  • Cover different ways to visualize spatial data:

  • Geo-faceted small multiples w/ package geofacet

  • Cartogram heatmap w/ package statebins

  • Tiled cartogram w/ package tilegramsR

  • Choropleth maps (a.k.a. "maps'')

    • Brief introduction to spatial data file (e.g., shapefile; .shp)

    • package sf to wrangle spatial data and tmap to visualize map

    • Interactive maps; small multiples in choropleth maps

  • Be mindful of the issues of space(s) and the curse of dimensionality

    • How to incorporate time series or extra covariates when visualizing spatial data?
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Prerequisite

# download packages 
packages <- c("tidyverse", "geofacet", "statebins", "tilegramsR", "sf", "tmap", "tigris")
not_installed <- setdiff(packages, rownames(installed.packages()))
if (length(not_installed)) install.packages(not_installed)
# load packages
library(tidyverse)
library(geofacet)
library(statebins)
library(tilegramsR)
library(sf)
library(tmap)
library(maptiles)
library(tigris)
# load theme
source("https://students.washington.edu/kpleung/vis/theme_cavis.R")
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Geo-faceted small multiples

WaPo_data <- read_csv("data/WaPo_data.csv")
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Geo-faceted small multiples

WaPo_data <- read_csv("data/WaPo_data.csv")
print(WaPo_data, n = 5)
# A tibble: 51 × 23
  stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
  <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
1 NC    North Carolina           1.4           0.9           3             4.1
2 AL    Alabama                  1.1           2.4           5.2           4.6
3 MS    Mississippi              2             3.9           5.1           2.2
4 AR    Arkansas                 1.6           1.3           1.6           2.7
5 TN    Tennessee                1.6           2             3.4           3.5
# ℹ 46 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
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Geo-faceted small multiples

# convert the data into long format
WaPo_data
# A tibble: 51 × 23
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
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Geo-faceted small multiples

# convert the data into long format
WaPo_data %>%
  select(-avgshare)
# A tibble: 51 × 22
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 16 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>
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Geo-faceted small multiples

# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
  pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut")
# A tibble: 1,020 × 4
   stab  state          year          share_cut
   <chr> <chr>          <chr>             <dbl>
 1 NC    North Carolina share_cut1994       1.4
 2 NC    North Carolina share_cut1995       0.9
 3 NC    North Carolina share_cut1996       3  
 4 NC    North Carolina share_cut1997       4.1
 5 NC    North Carolina share_cut1998       3  
 6 NC    North Carolina share_cut1999       3.9
 7 NC    North Carolina share_cut2000       1.5
 8 NC    North Carolina share_cut2001       3.8
 9 NC    North Carolina share_cut2002       6.1
10 NC    North Carolina share_cut2003       6.7
# ℹ 1,010 more rows
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Geo-faceted small multiples

# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
  pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut") %>%
  mutate(year = str_extract(year, "\\d+"),
         year = as.numeric(year))
# A tibble: 1,020 × 4
   stab  state           year share_cut
   <chr> <chr>          <dbl>     <dbl>
 1 NC    North Carolina  1994       1.4
 2 NC    North Carolina  1995       0.9
 3 NC    North Carolina  1996       3  
 4 NC    North Carolina  1997       4.1
 5 NC    North Carolina  1998       3  
 6 NC    North Carolina  1999       3.9
 7 NC    North Carolina  2000       1.5
 8 NC    North Carolina  2001       3.8
 9 NC    North Carolina  2002       6.1
10 NC    North Carolina  2003       6.7
# ℹ 1,010 more rows
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Geo-faceted small multiples

# convert the data into long format
WaPo_data %>%
  select(-avgshare) %>%
  pivot_longer(contains("share_cut"), names_to = "year", values_to = "share_cut") %>%
  mutate(year = str_extract(year, "\\d+"),
         year = as.numeric(year)) ->
  WaPo_data_long
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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut))

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line()

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state)

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  )

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  )

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  )

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_wrap(~ state) +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
  labs(x = NULL, y = "Share of job loss due to trade")

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_geo(~ state, grid = "us_state_grid2") +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
  labs(x = NULL, y = "Share of job loss due to trade")

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Geo-faceted small multiples

ggplot(WaPo_data_long, aes(x = year , y = share_cut)) +
  theme_cavis_hgrid +
  geom_line() +
  facet_geo(~ state, grid = "us_state_grid1") +
  scale_x_continuous(
    labels = function(x) paste0("'", str_sub(x, 3, 4))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%")
  ) +
  theme(
    strip.text.x = element_text(size = 7),
    axis.text.x = element_text(size = 7),
    axis.text.y = element_text(size = 7)
  ) +
  labs(x = NULL, y = "Share of job loss due to trade")

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Geo-faceted small multiples

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Geo-faceted small multiples

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Cartogram Heatmaps

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Cartogram heatmaps with statebins package

# coerce avgshare into a factor
WaPo_data
# A tibble: 51 × 23
   stab  state          share_cut1994 share_cut1995 share_cut1996 share_cut1997
   <chr> <chr>                  <dbl>         <dbl>         <dbl>         <dbl>
 1 NC    North Carolina           1.4           0.9           3             4.1
 2 AL    Alabama                  1.1           2.4           5.2           4.6
 3 MS    Mississippi              2             3.9           5.1           2.2
 4 AR    Arkansas                 1.6           1.3           1.6           2.7
 5 TN    Tennessee                1.6           2             3.4           3.5
 6 SC    South Carolina           1.5           1             2.5           0.7
 7 ME    Maine                    2.3           1.9           3.5           1.7
 8 MI    Michigan                 0.3           0.4           1.1           0.9
 9 OR    Oregon                   0.4           0.7           1.3           0.8
10 KY    Kentucky                 2.5           1             3.1           1.8
# ℹ 41 more rows
# ℹ 17 more variables: share_cut1998 <dbl>, share_cut1999 <dbl>,
#   share_cut2000 <dbl>, share_cut2001 <dbl>, share_cut2002 <dbl>,
#   share_cut2003 <dbl>, share_cut2004 <dbl>, share_cut2005 <dbl>,
#   share_cut2006 <dbl>, share_cut2007 <dbl>, share_cut2008 <dbl>,
#   share_cut2009 <dbl>, share_cut2010 <dbl>, share_cut2011 <dbl>,
#   share_cut2012 <dbl>, share_cut2013 <dbl>, avgshare <dbl>
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Cartogram heatmaps with statebins package

# coerce avgshare into a factor
WaPo_data %>%
  select(stab, state, avgshare)
# A tibble: 51 × 3
   stab  state          avgshare
   <chr> <chr>             <dbl>
 1 NC    North Carolina     3.56
 2 AL    Alabama            3.22
 3 MS    Mississippi        2.94
 4 AR    Arkansas           2.89
 5 TN    Tennessee          2.86
 6 SC    South Carolina     2.61
 7 ME    Maine              2.58
 8 MI    Michigan           2.55
 9 OR    Oregon             2.38
10 KY    Kentucky           2.24
# ℹ 41 more rows
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Cartogram heatmaps with statebins package

# coerce avgshare into a factor
WaPo_data %>%
  select(stab, state, avgshare) %>%
  mutate(avgshare2 = cut(avgshare,
                         breaks = 4,
                         labels = c("0-1", "1-2", "2-3", "3-4")))
# A tibble: 51 × 4
   stab  state          avgshare avgshare2
   <chr> <chr>             <dbl> <fct>    
 1 NC    North Carolina     3.56 3-4      
 2 AL    Alabama            3.22 3-4      
 3 MS    Mississippi        2.94 3-4      
 4 AR    Arkansas           2.89 3-4      
 5 TN    Tennessee          2.86 3-4      
 6 SC    South Carolina     2.61 2-3      
 7 ME    Maine              2.58 2-3      
 8 MI    Michigan           2.55 2-3      
 9 OR    Oregon             2.38 2-3      
10 KY    Kentucky           2.24 2-3      
# ℹ 41 more rows
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Cartogram heatmaps with statebins package

# coerce avgshare into a factor
WaPo_data %>%
  select(stab, state, avgshare) %>%
  mutate(avgshare2 = cut(avgshare,
                         breaks = 4,
                         labels = c("0-1", "1-2", "2-3", "3-4"))) ->
  WaPo_data2
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Cartogram heatmaps with statebins package

WaPo_data2
# A tibble: 51 × 4
   stab  state          avgshare avgshare2
   <chr> <chr>             <dbl> <fct>    
 1 NC    North Carolina     3.56 3-4      
 2 AL    Alabama            3.22 3-4      
 3 MS    Mississippi        2.94 3-4      
 4 AR    Arkansas           2.89 3-4      
 5 TN    Tennessee          2.86 3-4      
 6 SC    South Carolina     2.61 2-3      
 7 ME    Maine              2.58 2-3      
 8 MI    Michigan           2.55 2-3      
 9 OR    Oregon             2.38 2-3      
10 KY    Kentucky           2.24 2-3      
# ℹ 41 more rows
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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
  )

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
  ) +
  theme_statebins()

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins()

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade")

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
  guides(fill = guide_legend(title.position = "top",
                             label.position = "bottom"))

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
  guides(fill = guide_legend(title.position = "top",
                             label.position = "bottom")) +
  theme(legend.position = c(0, 0.85),
        legend.direction = "horizontal",
        )

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Cartogram heatmaps with statebins package

WaPo_data2 %>%
  statebins(
    value_col = "avgshare2",
    ggplot2_scale_function = scale_fill_brewer,
    round = TRUE
  ) +
  theme_statebins() +
  scale_fill_brewer(
    palette = "Blues",
    labels = c("Smallest\nshare", "", "", "Largest"),
    name = "Share of job loss due to trade") +
  guides(fill = guide_legend(title.position = "top",
                             label.position = "bottom")) +
  theme(legend.position = c(0, 0.85),
        legend.direction = "horizontal",
        legend.key.height = unit(0.2, "cm"),
        legend.key.width = unit(1.6, "cm")
        )

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Cartogram heatmaps with statebins package

  • The statebins package only supports the United States data at the moment

  • But customizing grid should be easy to implement, like geofacet package?

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Tiled cartogram with tilegramsR package

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Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
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Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
[1] "sf"         "data.frame"
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Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
ec_tiles
[1] "sf"         "data.frame"
Simple feature collection with 538 features and 3 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 293.6239 ymin: 16.95238 xmax: 2495.803 ymax: 1661.333
CRS:           NA
First 10 features:
   FID state tilegramVa                       geometry
1   02    AK          3 POLYGON ((293.6239 237.3333...
2   06    CA         55 POLYGON ((352.3486 847.619,...
3   06    CA         55 POLYGON ((322.9862 796.7619...
4   06    CA         55 POLYGON ((352.3486 745.9048...
5   06    CA         55 POLYGON ((322.9862 695.0476...
6   06    CA         55 POLYGON ((352.3486 644.1905...
7   02    AK          3 POLYGON ((322.9862 288.1905...
8   02    AK          3 POLYGON ((352.3486 237.3333...
9   06    CA         55 POLYGON ((411.0734 949.3333...
10  06    CA         55 POLYGON ((381.711 898.4762,...
34 / 56

Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab"))
[1] "sf"         "data.frame"
Simple feature collection with 538 features and 6 fields
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 293.6239 ymin: 16.95238 xmax: 2495.803 ymax: 1661.333
CRS:           NA
First 10 features:
   FID state tilegramVa    state.y avgshare avgshare2
1   02    AK          3     Alaska     1.88       2-3
2   06    CA         55 California     0.72       0-1
3   06    CA         55 California     0.72       0-1
4   06    CA         55 California     0.72       0-1
5   06    CA         55 California     0.72       0-1
6   06    CA         55 California     0.72       0-1
7   02    AK          3     Alaska     1.88       2-3
8   02    AK          3     Alaska     1.88       2-3
9   06    CA         55 California     0.72       0-1
10  06    CA         55 California     0.72       0-1
                         geometry
1  POLYGON ((293.6239 237.3333...
2  POLYGON ((352.3486 847.619,...
3  POLYGON ((322.9862 796.7619...
4  POLYGON ((352.3486 745.9048...
5  POLYGON ((322.9862 695.0476...
6  POLYGON ((352.3486 644.1905...
7  POLYGON ((322.9862 288.1905...
8  POLYGON ((352.3486 237.3333...
9  POLYGON ((411.0734 949.3333...
10 POLYGON ((381.711 898.4762,...
34 / 56

Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
  ec_tiles
[1] "sf"         "data.frame"
34 / 56

Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
  ec_tiles
state_tiles <- sf_FiveThirtyEightElectoralCollege.states  # state polygons for drawing borders
[1] "sf"         "data.frame"
34 / 56

Tiled cartogram with tilegramsR package

ec_tiles <- tilegramsR::sf_FiveThirtyEightElectoralCollege
class(ec_tiles)  # more on sf obejct soon
ec_tiles %>%
  left_join(WaPo_data2, by = c("state" = "stab")) ->
  ec_tiles
state_tiles <- sf_FiveThirtyEightElectoralCollege.states  # state polygons for drawing borders
ec_centers <- sf_FiveThirtyEightElectoralCollege.centers  # state centroids for drawing labels
[1] "sf"         "data.frame"
34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void()

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
  )

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
    )
  )

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  )

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  ) +
  scale_fill_brewer(palette = "Blues",
                    name = "Share of job loss\ndue to trade")

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  ) +
  scale_fill_brewer(palette = "Blues",
                    name = "Share of job loss\ndue to trade") +
  scale_color_brewer(palette = "Blues",
                     name = "Share of job loss\ndue to trade")

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  ) +
  scale_fill_brewer(palette = "Blues",
                    name = "Share of job loss\ndue to trade") +
  scale_color_brewer(palette = "Blues",
                     name = "Share of job loss\ndue to trade") +
  geom_sf_text(
    data = ec_centers,
    aes(label = state)
  )

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  ) +
  scale_fill_brewer(palette = "Blues",
                    name = "Share of job loss\ndue to trade") +
  scale_color_brewer(palette = "Blues",
                     name = "Share of job loss\ndue to trade") +
  geom_sf_text(
    data = ec_centers,
    aes(label = state)
  ) +
  geom_sf(
    data = state_tiles,
    color = "white", alpha = 0
  )

34 / 56

Tiled cartogram with tilegramsR package

ggplot() +
  theme_void() +
  geom_sf(
    data = ec_tiles,
    aes(
      fill = avgshare2,
      color = avgshare2
    )
  ) +
  scale_fill_brewer(palette = "Blues",
                    name = "Share of job loss\ndue to trade") +
  scale_color_brewer(palette = "Blues",
                     name = "Share of job loss\ndue to trade") +
  geom_sf_text(
    data = ec_centers,
    aes(label = state)
  ) +
  geom_sf(
    data = state_tiles,
    color = "white", alpha = 0
  ) +
  theme(legend.position = c(0.925, 0.25))

34 / 56

Final thoughts about tiled cartograms

35 / 56

Final thoughts about tiled cartograms

36 / 56

Final thoughts about tiled cartograms

  • Why hexagos? Because hexagons are the bestagons

  • To my knowledge, there is no existing algorithm to automatically generate the tile cartogram layouts

  • I'm trying to develop a function/package to do that: inspirations from using neural networks/ genetic algorithms to solve the Tetris game

37 / 56

Introduction to spatial data in R

  • Numerous spatial data formats
38 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...
39 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

40 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

    • Recent package sf allows geospatial data to be stored in data frames
41 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

    • Recent package sf allows geospatial data to be stored in data frames

    • Well integrated with tidyverse

42 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

    • Recent package sf allows geospatial data to be stored in data frames

    • Well integrated with tidyverse

  • Many packages to draw maps

43 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

    • Recent package sf allows geospatial data to be stored in data frames

    • Well integrated with tidyverse

  • Many packages to draw maps

    • tmap allows easy visualization of static and interactive maps
44 / 56

Introduction to spatial data in R

  • Numerous spatial data formats

    • .shp (shapefile; the most common); .geojson, .json; .gml; .csv; .tiff...

  • Countless packages to work with spatial data

    • Recent package sf allows geospatial data to be stored in data frames

    • Well integrated with tidyverse

  • Many packages to draw maps

    • tmap allows easy visualization of static and interactive maps

    • Also employs the "grammar of graphics"

45 / 56

Introduction to spatial data in R

  • Grammar of graphics in tmap:
ggplot2 tmap
Data ggplot(...) + tm_shape(...) +
Layers geom_...(...) + tm_...(...) +
Small Multiples facet_grid(...) tm_facets(...)
Layout theme(...) tm_layout(...)
46 / 56

Introduction to spatial data in R

  • Before plotting choropleth maps, we need to find the appropriate shapefile
47 / 56

Introduction to spatial data in R

  • Before plotting choropleth maps, we need to find the appropriate shapefile

  • Download the US shapefile here

48 / 56

Choropleth maps with sf and tmap

us_shp <- st_read("data/cb_2018_us_state_20m/cb_2018_us_state_20m.shp")
Reading layer `cb_2018_us_state_20m' from data source 
  `D:\Google Drive\Phd UW\Courses\5th year\CS&SS 569 - Visualizing Data and Models\Labs\Lab6\data\cb_2018_us_state_20m\cb_2018_us_state_20m.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 52 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  NAD83
48 / 56

Choropleth maps with sf and tmap

us_shp <- st_read("data/cb_2018_us_state_20m/cb_2018_us_state_20m.shp")
class(us_shp)
Reading layer `cb_2018_us_state_20m' from data source 
  `D:\Google Drive\Phd UW\Courses\5th year\CS&SS 569 - Visualizing Data and Models\Labs\Lab6\data\cb_2018_us_state_20m\cb_2018_us_state_20m.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 52 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  NAD83
[1] "sf"         "data.frame"
48 / 56

Choropleth maps with sf and tmap

us_shp <- st_read("data/cb_2018_us_state_20m/cb_2018_us_state_20m.shp")
class(us_shp)
head(as_tibble(us_shp))
Reading layer `cb_2018_us_state_20m' from data source 
  `D:\Google Drive\Phd UW\Courses\5th year\CS&SS 569 - Visualizing Data and Models\Labs\Lab6\data\cb_2018_us_state_20m\cb_2018_us_state_20m.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 52 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  NAD83
[1] "sf"         "data.frame"
# A tibble: 6 × 10
  STATEFP STATENS  AFFGEOID    GEOID STUSPS NAME         LSAD      ALAND  AWATER
  <chr>   <chr>    <chr>       <chr> <chr>  <chr>        <chr>     <dbl>   <dbl>
1 24      01714934 0400000US24 24    MD     Maryland     00      2.52e10 6.98e 9
2 19      01779785 0400000US19 19    IA     Iowa         00      1.45e11 1.08e 9
3 10      01779781 0400000US10 10    DE     Delaware     00      5.05e 9 1.40e 9
4 39      01085497 0400000US39 39    OH     Ohio         00      1.06e11 1.03e10
5 42      01779798 0400000US42 42    PA     Pennsylvania 00      1.16e11 3.39e 9
6 31      01779792 0400000US31 31    NE     Nebraska     00      1.99e11 1.37e 9
# ℹ 1 more variable: geometry <MULTIPOLYGON [°]>
48 / 56

Choropleth maps with sf and tmap

us_shp
Simple feature collection with 52 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  NAD83
First 10 features:
   STATEFP  STATENS    AFFGEOID GEOID STUSPS         NAME LSAD        ALAND
1       24 01714934 0400000US24    24     MD     Maryland   00  25151100280
2       19 01779785 0400000US19    19     IA         Iowa   00 144661267977
3       10 01779781 0400000US10    10     DE     Delaware   00   5045925646
4       39 01085497 0400000US39    39     OH         Ohio   00 105828882568
5       42 01779798 0400000US42    42     PA Pennsylvania   00 115884442321
6       31 01779792 0400000US31    31     NE     Nebraska   00 198956658395
7       53 01779804 0400000US53    53     WA   Washington   00 172112588220
8       72 01779808 0400000US72    72     PR  Puerto Rico   00   8868896030
9       01 01779775 0400000US01    01     AL      Alabama   00 131174048583
10      05 00068085 0400000US05    05     AR     Arkansas   00 134768872727
        AWATER                       geometry
1   6979966958 MULTIPOLYGON (((-76.04621 3...
2   1084180812 MULTIPOLYGON (((-96.62187 4...
3   1399985648 MULTIPOLYGON (((-75.77379 3...
4  10268850702 MULTIPOLYGON (((-82.86334 4...
5   3394589990 MULTIPOLYGON (((-80.51989 4...
6   1371829134 MULTIPOLYGON (((-104.0531 4...
7  12559278850 MULTIPOLYGON (((-123.2371 4...
8   4922382562 MULTIPOLYGON (((-65.34207 1...
9   4593327154 MULTIPOLYGON (((-88.46866 3...
10  2962859592 MULTIPOLYGON (((-94.61792 3...
48 / 56

Choropleth maps with sf and tmap

us_shp %>%
  left_join(WaPo_data, by=c("NAME" = "state"))
Simple feature collection with 52 features and 31 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -179.1743 ymin: 17.91377 xmax: 179.7739 ymax: 71.35256
Geodetic CRS:  NAD83
First 10 features:
   STATEFP  STATENS    AFFGEOID GEOID STUSPS         NAME LSAD        ALAND
1       24 01714934 0400000US24    24     MD     Maryland   00  25151100280
2       19 01779785 0400000US19    19     IA         Iowa   00 144661267977
3       10 01779781 0400000US10    10     DE     Delaware   00   5045925646
4       39 01085497 0400000US39    39     OH         Ohio   00 105828882568
5       42 01779798 0400000US42    42     PA Pennsylvania   00 115884442321
6       31 01779792 0400000US31    31     NE     Nebraska   00 198956658395
7       53 01779804 0400000US53    53     WA   Washington   00 172112588220
8       72 01779808 0400000US72    72     PR  Puerto Rico   00   8868896030
9       01 01779775 0400000US01    01     AL      Alabama   00 131174048583
10      05 00068085 0400000US05    05     AR     Arkansas   00 134768872727
        AWATER stab share_cut1994 share_cut1995 share_cut1996 share_cut1997
1   6979966958   MD           0.4           1.1           0.7           0.4
2   1084180812   IA           0.1           0.4           0.5           0.6
3   1399985648   DE           0.6           0.0           0.0           0.0
4  10268850702   OH           0.6           0.8           0.6           1.0
5   3394589990   PA           1.5           1.4           2.0           1.7
6   1371829134   NE           0.0           0.2           0.7           0.1
7  12559278850   WA           1.0           1.2           0.5           0.4
8   4922382562 <NA>            NA            NA            NA            NA
9   4593327154   AL           1.1           2.4           5.2           4.6
10  2962859592   AR           1.6           1.3           1.6           2.7
   share_cut1998 share_cut1999 share_cut2000 share_cut2001 share_cut2002
1            0.5           0.4           0.3           0.1           0.3
2            0.1           0.5           0.1           0.6           0.9
3            0.0           0.4           0.0           0.0           0.0
4            0.5           1.0           0.9           1.3           2.5
5            1.5           1.3           1.4           3.6           2.8
6            0.1           0.2           1.0           0.0           0.6
7            1.0           0.7           2.5           1.4           9.4
8             NA            NA            NA            NA            NA
9            3.5           5.6           2.5           2.8           6.5
10           2.7           2.8           0.6           3.1           3.9
   share_cut2003 share_cut2004 share_cut2005 share_cut2006 share_cut2007
1            1.1           0.1           0.5           0.7           0.8
2            0.9           1.0           0.6           2.4           0.9
3            1.0           0.9           0.7           0.4           0.1
4            1.9           1.4           0.9           2.9           2.0
5            2.8           1.2           1.5           0.9           1.6
6            2.0           1.2           0.2           0.2           0.9
7            1.3           3.4           0.6           0.4           0.6
8             NA            NA            NA            NA            NA
9            2.2           2.1           1.2           3.3           2.6
10           3.6           1.1           2.3           3.8           4.9
   share_cut2008 share_cut2009 share_cut2010 share_cut2011 share_cut2012
1            0.5           0.2           0.6           0.8           0.3
2            0.8           2.3           1.4           1.4           0.2
3            0.0           5.4           0.0           0.0           0.0
4            2.2           5.0           2.8           1.0           1.3
5            1.0           2.6           2.5           0.9           1.1
6            0.3           1.3           0.3           1.1           0.1
7            0.7           2.2           1.6           1.1           1.0
8             NA            NA            NA            NA            NA
9            3.9           4.3           3.6           0.4           0.7
10           3.2           3.0           3.3           2.4           3.9
   share_cut2013 avgshare                       geometry
1            0.1     0.47 MULTIPOLYGON (((-76.04621 3...
2            1.1     0.89 MULTIPOLYGON (((-96.62187 4...
3            0.1     0.53 MULTIPOLYGON (((-75.77379 3...
4            0.4     1.72 MULTIPOLYGON (((-82.86334 4...
5            0.8     1.79 MULTIPOLYGON (((-80.51989 4...
6            0.5     0.62 MULTIPOLYGON (((-104.0531 4...
7            0.7     1.69 MULTIPOLYGON (((-123.2371 4...
8             NA       NA MULTIPOLYGON (((-65.34207 1...
9            0.6     3.22 MULTIPOLYGON (((-88.46866 3...
10           0.5     2.89 MULTIPOLYGON (((-94.61792 3...
48 / 56

Choropleth maps with sf and tmap

us_shp %>%
  left_join(WaPo_data, by=c("NAME" = "state")) %>%
  tigris::shift_geometry()   # rescale AK, HI, PR
Simple feature collection with 52 features and 31 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -3112200 ymin: -1697728 xmax: 2258154 ymax: 1558935
Projected CRS: USA_Contiguous_Albers_Equal_Area_Conic
First 10 features:
   STATEFP  STATENS    AFFGEOID GEOID STUSPS         NAME LSAD        ALAND
1       24 01714934 0400000US24    24     MD     Maryland   00  25151100280
2       19 01779785 0400000US19    19     IA         Iowa   00 144661267977
3       10 01779781 0400000US10    10     DE     Delaware   00   5045925646
4       39 01085497 0400000US39    39     OH         Ohio   00 105828882568
5       42 01779798 0400000US42    42     PA Pennsylvania   00 115884442321
6       31 01779792 0400000US31    31     NE     Nebraska   00 198956658395
7       53 01779804 0400000US53    53     WA   Washington   00 172112588220
8       01 01779775 0400000US01    01     AL      Alabama   00 131174048583
9       05 00068085 0400000US05    05     AR     Arkansas   00 134768872727
10      35 00897535 0400000US35    35     NM   New Mexico   00 314196306401
        AWATER stab share_cut1994 share_cut1995 share_cut1996 share_cut1997
1   6979966958   MD           0.4           1.1           0.7           0.4
2   1084180812   IA           0.1           0.4           0.5           0.6
3   1399985648   DE           0.6           0.0           0.0           0.0
4  10268850702   OH           0.6           0.8           0.6           1.0
5   3394589990   PA           1.5           1.4           2.0           1.7
6   1371829134   NE           0.0           0.2           0.7           0.1
7  12559278850   WA           1.0           1.2           0.5           0.4
8   4593327154   AL           1.1           2.4           5.2           4.6
9   2962859592   AR           1.6           1.3           1.6           2.7
10   728776523   NM           1.4           0.8           0.9           3.0
   share_cut1998 share_cut1999 share_cut2000 share_cut2001 share_cut2002
1            0.5           0.4           0.3           0.1           0.3
2            0.1           0.5           0.1           0.6           0.9
3            0.0           0.4           0.0           0.0           0.0
4            0.5           1.0           0.9           1.3           2.5
5            1.5           1.3           1.4           3.6           2.8
6            0.1           0.2           1.0           0.0           0.6
7            1.0           0.7           2.5           1.4           9.4
8            3.5           5.6           2.5           2.8           6.5
9            2.7           2.8           0.6           3.1           3.9
10           1.4           4.5           0.4           0.3           1.7
   share_cut2003 share_cut2004 share_cut2005 share_cut2006 share_cut2007
1            1.1           0.1           0.5           0.7           0.8
2            0.9           1.0           0.6           2.4           0.9
3            1.0           0.9           0.7           0.4           0.1
4            1.9           1.4           0.9           2.9           2.0
5            2.8           1.2           1.5           0.9           1.6
6            2.0           1.2           0.2           0.2           0.9
7            1.3           3.4           0.6           0.4           0.6
8            2.2           2.1           1.2           3.3           2.6
9            3.6           1.1           2.3           3.8           4.9
10           0.5           0.1           0.1           0.1           0.0
   share_cut2008 share_cut2009 share_cut2010 share_cut2011 share_cut2012
1            0.5           0.2           0.6           0.8           0.3
2            0.8           2.3           1.4           1.4           0.2
3            0.0           5.4           0.0           0.0           0.0
4            2.2           5.0           2.8           1.0           1.3
5            1.0           2.6           2.5           0.9           1.1
6            0.3           1.3           0.3           1.1           0.1
7            0.7           2.2           1.6           1.1           1.0
8            3.9           4.3           3.6           0.4           0.7
9            3.2           3.0           3.3           2.4           3.9
10           4.1           1.7           1.9           0.1           0.8
   share_cut2013 avgshare                       geometry
1            0.1     0.47 MULTIPOLYGON (((1722285 240...
2            1.1     0.89 MULTIPOLYGON (((-50588.83 5...
3            0.1     0.53 MULTIPOLYGON (((1705278 431...
4            0.4     1.72 MULTIPOLYGON (((1081987 544...
5            0.8     1.79 MULTIPOLYGON (((1287712 486...
6            0.5     0.62 MULTIPOLYGON (((-670097.4 4...
7            0.7     1.69 MULTIPOLYGON (((-2000238 15...
8            0.6     3.22 MULTIPOLYGON (((708460.5 -5...
9            0.5     2.89 MULTIPOLYGON (((122656.3 -1...
10           0.0     1.26 MULTIPOLYGON (((-1226428 -5...
48 / 56

Choropleth maps with sf and tmap

us_shp %>%
  left_join(WaPo_data, by=c("NAME" = "state")) %>%
  tigris::shift_geometry() ->  # rescale AK, HI, PR
  us_shp2
48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
  ) +
  tm_text(
    text = "STUSPS",
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
  ) +
  tm_text(
    text = "STUSPS",
    size = "avgshare",
    legend.size.show = FALSE
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
  ) +
  tm_text(
    text = "STUSPS",
    size = "avgshare",
    legend.size.show = FALSE
  ) +
  tm_layout(
    frame = FALSE,
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
    legend.is.portrait = FALSE,
  ) +
  tm_text(
    text = "STUSPS",
    size = "avgshare",
    legend.size.show = FALSE
  ) +
  tm_layout(
    frame = FALSE,
    legend.stack = "horizontal",
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
    legend.is.portrait = FALSE,
  ) +
  tm_text(
    text = "STUSPS",
    size = "avgshare",
    legend.size.show = FALSE
  ) +
  tm_layout(
    frame = FALSE,
    legend.stack = "horizontal",
    legend.position = c('right', 'top'),
  )

48 / 56

Choropleth maps with sf and tmap

tm_shape(us_shp2) + tm_polygons(
    col = "avgshare",
    palette = "Blues",
    border.col = "white",
    legend.is.portrait = FALSE,
    title = "Share of job loss due to trade",
    labels = c("Smallest", "", "", "Largest share")
  ) +
  tm_text(
    text = "STUSPS",
    size = "avgshare",
    legend.size.show = FALSE
  ) +
  tm_layout(
    frame = FALSE,
    legend.stack = "horizontal",
    legend.position = c('right', 'top'),
    legend.title.size = 1,
    legend.text.size = 0.5
  )

48 / 56

Choropleth maps with sf and tmap

  • To save the map:
tmap_save(tmap_object, filename = "[...].pdf")
49 / 56

Choropleth maps with sf and tmap: NYC examples

50 / 56

Choropleth maps with sf and tmap: NYC examples

51 / 56

Choropleth maps with sf and tmap: NYC examples

# load .shp file with sf
nyc_shp <- st_read("data/nyc/nyc.shp")
Reading layer `nyc' from data source 
  `D:\Google Drive\Phd UW\Courses\5th year\CS&SS 569 - Visualizing Data and Models\Labs\Lab6\data\nyc\nyc.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 55 features and 34 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 913037.2 ymin: 120117 xmax: 1067549 ymax: 272751.4
Projected CRS: NAD83 / New York Long Island (ftUS)
52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  )

52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  )

52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  )

52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  )

52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  )

52 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_layout(frame = FALSE)

52 / 56

Choropleth maps with sf and tmap: NYC examples

## interactive mode in tmap
tmap_mode("view")
52 / 56

Choropleth maps with sf and tmap: NYC examples

## interactive mode in tmap
tmap_mode("view")
## switch off spherical geometry to avoid error
## usually you don't need to do this
sf::sf_use_s2(use_s2 = FALSE)
52 / 56

Choropleth maps with sf and tmap: NYC examples

## interactive mode in tmap
tmap_mode("view")
## switch off spherical geometry to avoid error
## usually you don't need to do this
sf::sf_use_s2(use_s2 = FALSE)
## tmap
tm_shape(nyc_shp) + tm_polygons(
    col = "rent2008",
    palette = "BrBG",
    title = "Rent in 2008",
    alpha = 0.5
  )

+−
Rent in 2008
0 to 500
500 to 1,000
1,000 to 1,500
1,500 to 2,000
2,000 to 2,500
2,500 to 3,000
Missing
Leaflet | Tiles © Esri — Esri, DeLorme, NAVTEQ
52 / 56

Choropleth maps with sf and tmap: NYC examples

## interactive mode in tmap
tmap_mode("view")
## switch off spherical geometry to avoid error
## usually you don't need to do this
sf::sf_use_s2(use_s2 = FALSE)
## tmap
tm_shape(nyc_shp) + tm_polygons(
    col = "rent2008",
    palette = "BrBG",
    title = "Rent in 2008",
    alpha = 0.5
  ) +
  tm_basemap(
    server = "OpenStreetMap",
    alpha = 0.5
  )

+−
Rent in 2008
0 to 500
500 to 1,000
1,000 to 1,500
1,500 to 2,000
2,000 to 2,500
2,500 to 3,000
Missing
Leaflet | © OpenStreetMap contributors
52 / 56

Choropleth maps with sf and tmap: NYC examples

## interactive mode in tmap
tmap_mode("view")
## switch off spherical geometry to avoid error
## usually you don't need to do this
sf::sf_use_s2(use_s2 = FALSE)
## tmap
tm_shape(nyc_shp) + tm_polygons(
    col = "rent2008",
    palette = "BrBG",
    title = "Rent in 2008",
    alpha = 0.5
  ) +
  tm_basemap(
    server = "OpenStreetMap",
    alpha = 0.5
  )
## switch back to plotting mode
tmap_mode("plot")

+−
Rent in 2008
0 to 500
500 to 1,000
1,000 to 1,500
1,500 to 2,000
2,000 to 2,500
2,500 to 3,000
Missing
Leaflet | © OpenStreetMap contributors
52 / 56

Choropleth maps with sf and tmap: NYC examples

  • How to do small multiples in tmap?
53 / 56

Choropleth maps with sf and tmap: NYC examples

  • How to do small multiples in tmap?

    • tmap_arrange(): like cowplot::plot_grid(); arrange multiple maps into a grid
54 / 56

Choropleth maps with sf and tmap: NYC examples

  • How to do small multiples in tmap?

    • tmap_arrange(): like cowplot::plot_grid(); arrange multiple maps into a grid

    • tm_facets(): like facet_grid() or facet_wrap(); facet areas(polygons) by some variables

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  )

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_layout(frame = FALSE)

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_layout(frame = FALSE) ->
  rentNYC
55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "forhis08",
  border.col = "white",
  title = "Hispanic population in 2008 (%)"
  )

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "forhis08",
  border.col = "white",
  title = "Hispanic population in 2008 (%)"
  ) +
  tm_layout(frame = FALSE)

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "forhis08",
  border.col = "white",
  title = "Hispanic population in 2008 (%)"
  ) +
  tm_layout(frame = FALSE) ->
  hisNYC
55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "pubast00",
  palette = "Blues",
  border.col = "white",
  title = "% of households receiving \nassistance in 2000"
  )

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "pubast00",
  palette = "Blues",
  border.col = "white",
  title = "% of households receiving \nassistance in 2000"
  ) +
  tm_layout(frame = FALSE)

55 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp) + tm_polygons(
  col = "pubast00",
  palette = "Blues",
  border.col = "white",
  title = "% of households receiving \nassistance in 2000"
  ) +
  tm_layout(frame = FALSE) ->
  pubastNYC
55 / 56

Choropleth maps with sf and tmap: NYC examples

tmap_arrange(rentNYC, hisNYC, pubastNYC, nrow = 1)

56 / 56

Choropleth maps with sf and tmap: NYC examples

## categorize hispanic pop levels
labels <- c("Low hisp pop",
            "Mid hisp pop",
            "High hisp pop")
56 / 56

Choropleth maps with sf and tmap: NYC examples

## categorize hispanic pop levels
labels <- c("Low hisp pop",
            "Mid hisp pop",
            "High hisp pop")
nyc_shp
Simple feature collection with 55 features and 34 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 913037.2 ymin: 120117 xmax: 1067549 ymax: 272751.4
Projected CRS: NAD83 / New York Long Island (ftUS)
First 10 features:
   bor_subb                       name code               subborough forhis06
1       501                North Shore  501              North Shore  37.0657
2       502                 Mid-Island  502               Mid-Island  27.9822
3       503                South Shore  503              South Shore  10.7019
4       401                    Astoria  401                  Astoria  52.0961
5       402       Sunnyside / Woodside  402       Sunnyside/Woodside  62.7242
6       403            Jackson Heights  403          Jackson Heights  68.4834
7       404          Elmhurst / Corona  404          Elmhurst/Corona  69.5219
8       405 Middle Village / Ridgewood  405 Middle Village/Ridgewood  39.4185
9       406   Forest Hills / Rego Park  406   Rego Park/Forest Hills  58.5388
10      407      Flushing / Whitestone  407      Flushing/Whitestone  59.4432
   forhis07 forhis08 forhis09 forwh06 forwh07 forwh08 forwh09 hhsiz1990 hhsiz00
1   34.0317  27.3767  29.3091 13.2540 11.8768 11.1788 11.1459    2.7146  2.7338
2   18.1193  24.0452  31.1566 20.0616 19.8575 22.4870 17.0371    2.8233  2.7176
3   12.1404   9.6890  14.6638 10.3060 12.7699  9.3561 10.2830    3.0547  2.8497
4   53.9585  54.6968  47.8050 38.3658 35.6551 32.1289 34.6578    2.4279  2.4995
5   69.3969  67.0897  58.2963 37.0512 31.9057 32.3264 33.8794    2.4646  2.6287
6   68.5405  66.5080  69.1580 34.3999 38.2428 38.1470 26.5347    2.8081  3.1650
7   66.8795  69.3414  64.3005 40.3935 51.6376 45.9578 48.3542    2.9690  3.1784
8   35.9546  36.2750  35.0181 37.8097 34.0156 33.0695 33.0816    2.5174  2.6606
9   52.6183  42.3878  47.9795 43.7395 47.5873 45.1205 39.8293    2.0631  2.1532
10  53.4747  53.2531  57.9868 26.5802 25.6864 21.3858 23.5204    2.6302  2.6818
   hhsiz02 hhsiz05 hhsiz08 kids2000 kids2005 kids2006 kids2007 kids2008
1   2.7412  2.8010  2.6983  39.2995  43.3788  38.4022  41.5213  39.8390
2   2.5405  2.6228  2.5749  36.2234  35.7630  36.9081  37.6798  37.2447
3   2.6525  2.6121  2.6483  39.7362  42.5232  40.3577  40.3797  40.4820
4   2.3032  2.3227  2.2746  28.4592  27.2223  25.2556  24.8911  22.0364
5   2.5300  2.4993  2.4766  29.8808  28.6841  28.1440  26.3675  29.9032
6   2.9108  2.8599  2.8604  41.6335  40.2431  39.3135  37.4414  39.4144
7   2.8740  3.1064  3.2223  41.8224  35.8002  40.2321  44.0976  37.2912
8   2.7540  2.7750  2.7007  35.0385  36.6091  37.7748  37.6190  30.9096
9   2.1504  2.1932  2.1512  21.9464  21.4119  24.2723  23.7545  24.7718
10  2.5351  2.5668  2.5857  31.5298  27.7413  28.5853  28.1564  31.1019
   kids2009 rent2002 rent2005 rent2008 rentpct02 rentpct05 rentpct08 pubast90
1   40.3169      800      900     1000   21.1119   24.8073   28.5344 47.32913
2   37.8176      650      800      950   32.3615   27.2584   27.9567 35.18232
3   35.3880      750      775      800   23.0547   20.4146   18.1590 23.89404
4   17.9996     1000     1100     1400   25.6022   26.7685   28.0467 80.53393
5   26.2156     1000     1000     1400   18.8079   22.6752   21.3009 75.51687
6   39.0377      910     1000     1100   34.0156   34.8050   27.1032 66.64228
7   43.3207      896     1001     1300   26.7381   34.3842   28.8255 77.53080
8   35.4524      800      900     1100   41.2035   40.6256   36.2956 59.01702
9   21.9552     1000     1035     1400   11.9766   10.4943   11.5520 64.16213
10  28.3184     1000     1100     1200   23.9394   23.3487   22.6715 53.62965
   pubast00  yrhom02   yrhom05  yrhom08                       geometry
1  6.005791 10.80507 12.127853 11.54743 MULTIPOLYGON (((962498.9 17...
2  2.287034 15.24125 15.183111 14.68212 MULTIPOLYGON (((928296.9 16...
3  1.350208 12.70425 12.972280 13.56149 MULTIPOLYGON (((932416.3 14...
4  5.204510 12.83917 13.377513 12.54464 MULTIPOLYGON (((1010873 226...
5  2.974139 15.38766 12.518789 12.66691 MULTIPOLYGON (((1011647 216...
6  5.332569 12.64923 12.580355 11.96598 MULTIPOLYGON (((1014790 220...
7  6.029230 11.55498  9.828084 10.73805 MULTIPOLYGON (((1018353 205...
8  3.951290 13.49841 12.616178 11.19165 MULTIPOLYGON (((1013477 206...
9  3.111417 13.40156 13.375477 12.74230 MULTIPOLYGON (((1026974 207...
10 2.795721 14.01126 12.806067 12.84800 MULTIPOLYGON (((1034251 230...
56 / 56

Choropleth maps with sf and tmap: NYC examples

## categorize hispanic pop levels
labels <- c("Low hisp pop",
            "Mid hisp pop",
            "High hisp pop")
nyc_shp %>%
  mutate(forhis08_cat =
           cut(forhis08,
               breaks = 3,
               labels = labels))
Simple feature collection with 55 features and 35 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 913037.2 ymin: 120117 xmax: 1067549 ymax: 272751.4
Projected CRS: NAD83 / New York Long Island (ftUS)
First 10 features:
   bor_subb                       name code               subborough forhis06
1       501                North Shore  501              North Shore  37.0657
2       502                 Mid-Island  502               Mid-Island  27.9822
3       503                South Shore  503              South Shore  10.7019
4       401                    Astoria  401                  Astoria  52.0961
5       402       Sunnyside / Woodside  402       Sunnyside/Woodside  62.7242
6       403            Jackson Heights  403          Jackson Heights  68.4834
7       404          Elmhurst / Corona  404          Elmhurst/Corona  69.5219
8       405 Middle Village / Ridgewood  405 Middle Village/Ridgewood  39.4185
9       406   Forest Hills / Rego Park  406   Rego Park/Forest Hills  58.5388
10      407      Flushing / Whitestone  407      Flushing/Whitestone  59.4432
   forhis07 forhis08 forhis09 forwh06 forwh07 forwh08 forwh09 hhsiz1990 hhsiz00
1   34.0317  27.3767  29.3091 13.2540 11.8768 11.1788 11.1459    2.7146  2.7338
2   18.1193  24.0452  31.1566 20.0616 19.8575 22.4870 17.0371    2.8233  2.7176
3   12.1404   9.6890  14.6638 10.3060 12.7699  9.3561 10.2830    3.0547  2.8497
4   53.9585  54.6968  47.8050 38.3658 35.6551 32.1289 34.6578    2.4279  2.4995
5   69.3969  67.0897  58.2963 37.0512 31.9057 32.3264 33.8794    2.4646  2.6287
6   68.5405  66.5080  69.1580 34.3999 38.2428 38.1470 26.5347    2.8081  3.1650
7   66.8795  69.3414  64.3005 40.3935 51.6376 45.9578 48.3542    2.9690  3.1784
8   35.9546  36.2750  35.0181 37.8097 34.0156 33.0695 33.0816    2.5174  2.6606
9   52.6183  42.3878  47.9795 43.7395 47.5873 45.1205 39.8293    2.0631  2.1532
10  53.4747  53.2531  57.9868 26.5802 25.6864 21.3858 23.5204    2.6302  2.6818
   hhsiz02 hhsiz05 hhsiz08 kids2000 kids2005 kids2006 kids2007 kids2008
1   2.7412  2.8010  2.6983  39.2995  43.3788  38.4022  41.5213  39.8390
2   2.5405  2.6228  2.5749  36.2234  35.7630  36.9081  37.6798  37.2447
3   2.6525  2.6121  2.6483  39.7362  42.5232  40.3577  40.3797  40.4820
4   2.3032  2.3227  2.2746  28.4592  27.2223  25.2556  24.8911  22.0364
5   2.5300  2.4993  2.4766  29.8808  28.6841  28.1440  26.3675  29.9032
6   2.9108  2.8599  2.8604  41.6335  40.2431  39.3135  37.4414  39.4144
7   2.8740  3.1064  3.2223  41.8224  35.8002  40.2321  44.0976  37.2912
8   2.7540  2.7750  2.7007  35.0385  36.6091  37.7748  37.6190  30.9096
9   2.1504  2.1932  2.1512  21.9464  21.4119  24.2723  23.7545  24.7718
10  2.5351  2.5668  2.5857  31.5298  27.7413  28.5853  28.1564  31.1019
   kids2009 rent2002 rent2005 rent2008 rentpct02 rentpct05 rentpct08 pubast90
1   40.3169      800      900     1000   21.1119   24.8073   28.5344 47.32913
2   37.8176      650      800      950   32.3615   27.2584   27.9567 35.18232
3   35.3880      750      775      800   23.0547   20.4146   18.1590 23.89404
4   17.9996     1000     1100     1400   25.6022   26.7685   28.0467 80.53393
5   26.2156     1000     1000     1400   18.8079   22.6752   21.3009 75.51687
6   39.0377      910     1000     1100   34.0156   34.8050   27.1032 66.64228
7   43.3207      896     1001     1300   26.7381   34.3842   28.8255 77.53080
8   35.4524      800      900     1100   41.2035   40.6256   36.2956 59.01702
9   21.9552     1000     1035     1400   11.9766   10.4943   11.5520 64.16213
10  28.3184     1000     1100     1200   23.9394   23.3487   22.6715 53.62965
   pubast00  yrhom02   yrhom05  yrhom08                       geometry
1  6.005791 10.80507 12.127853 11.54743 MULTIPOLYGON (((962498.9 17...
2  2.287034 15.24125 15.183111 14.68212 MULTIPOLYGON (((928296.9 16...
3  1.350208 12.70425 12.972280 13.56149 MULTIPOLYGON (((932416.3 14...
4  5.204510 12.83917 13.377513 12.54464 MULTIPOLYGON (((1010873 226...
5  2.974139 15.38766 12.518789 12.66691 MULTIPOLYGON (((1011647 216...
6  5.332569 12.64923 12.580355 11.96598 MULTIPOLYGON (((1014790 220...
7  6.029230 11.55498  9.828084 10.73805 MULTIPOLYGON (((1018353 205...
8  3.951290 13.49841 12.616178 11.19165 MULTIPOLYGON (((1013477 206...
9  3.111417 13.40156 13.375477 12.74230 MULTIPOLYGON (((1026974 207...
10 2.795721 14.01126 12.806067 12.84800 MULTIPOLYGON (((1034251 230...
    forhis08_cat
1   Low hisp pop
2   Low hisp pop
3   Low hisp pop
4  High hisp pop
5  High hisp pop
6  High hisp pop
7  High hisp pop
8   Mid hisp pop
9   Mid hisp pop
10 High hisp pop
56 / 56

Choropleth maps with sf and tmap: NYC examples

## categorize hispanic pop levels
labels <- c("Low hisp pop",
            "Mid hisp pop",
            "High hisp pop")
nyc_shp %>%
  mutate(forhis08_cat =
           cut(forhis08,
               breaks = 3,
               labels = labels)) ->
  nyc_shp2
56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
    frame = FALSE,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
    frame = FALSE,
    panel.label.bg.color = NA,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
    frame = FALSE,
    panel.label.bg.color = NA,
    frame.lwd = 0,
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
    frame = FALSE,
    panel.label.bg.color = NA,
    frame.lwd = 0,
    panel.label.size = 1.5
  )

56 / 56

Choropleth maps with sf and tmap: NYC examples

tm_shape(nyc_shp2) + tm_polygons(
  col = "rent2008",
  palette = "BrBG",
  border.col = "white",
  title = "Rent in 2008"
  ) +
  tm_facets(
    by = "forhis08_cat",
    drop.units = TRUE,
    free.coords = FALSE,
  ) +
  tm_layout(
    legend.outside.size = 0.135,
    frame = FALSE,
    panel.label.bg.color = NA,
    frame.lwd = 0,
    panel.label.size = 1.5
  ) +
  tm_shape(nyc_shp2) +
  tm_borders(col = "grey75", lty = 2)

56 / 56

Introduction

  • A long rant on space: What do we mean when we say visualizing "spatial data"?
2 / 56
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