Now

Fun With Flags... MAPS!

Fun with maps

Cologne data

attributed_cologne <-
  sf::read_sf("./data/attributed_cologne.shp")  
charger_cologne <- sf::read_sf("./data/charger_cologne.shp")
streets_cologne <- sf::read_sf("./data/streets_cologne.shp")Copy Code

What makes a good map?

What makes a good map?

The choice is Yours: R Packages for Mapping

As always, R offers several ways to map spatial data, and the provided packages are various. What is out there? Just a few:

• base R graphics package: mapdata
• mobile-friendly interactive maps: leaflet
• interactive and static thematic maps based on shapefiles:
• tmap
• mapview

Our Choice Today

Today, we'll concentrate on the package tmap

• very intuitive and makes "good" decisions for us
• the syntax is very similar to ggplot2*

Source

*A wide-known 'secret' is that tmap creates a map based on ggplot2, so ggplot2-specific graphics manipulations will work as well.

First Map: Cologne Electric Car Data

library(tmap)
tm_shape(attributed_cologne) +
  tm_fill("ecar")Copy Code

tmap In a Nutshell

# define and introduce every (new) 
# geospatial data object
tm_shape() +
  # choose at least one building block as 
  # 'aesthetic layer'
  # for polygon layer choose from:
  tm_fill() + # polygons without borders
  tm_polygons() +  # polygons with borders
  tm_borders() + # only borders of polygons
  # for line layer choose:
  tm_lines() +
  # for point layer choose:
  tm_dots() +
  tm_bubbles() +
  # for raster layer choose
  tm_raster() +
  tm_rgb() +
  ...
# for all of them:
?'tmap-element'Copy Code

tmap In a Nutshell: Polygon Layer

tm_shape(attributed_cologne) +
  tm_fill()
tm_shape(attributed_cologne) +
  tm_polygons()
tm_shape(attributed_cologne) +
  tm_borders()Copy Code

tmap In a Nutshell: Line and Point Layer

tm_shape(streets_cologne) +
  tm_lines()
tm_shape(charger_cologne) +
  tm_dots()Copy Code

tmap In a Nutshell: Put It All Together

We can map the geometric attributes as single layers, but we can also layer our map and stack the layers on each other.

tm_shape(attributed_cologne) +
  tm_polygons() +
  tm_shape(streets_cologne) +
  tm_lines() +
  tm_shape(charger_cologne) +
  tm_dots(col = "red")Copy Code

Add Some Information

After we took care of our geometric types, we want to add some information to our data. The inner construction of each building block of tm_elements is the same.

1. Define the variable of interest first by stating the column name.
2. Add a name for the legend title and color palette, adjust the legend and scales ...

Choosing an Attribute

tm_shape(attributed_cologne) +
  tm_polygons("ecar")Copy Code

Choosing a Color Palette

tm_shape(attributed_cologne) +
  tm_polygons(
    "ecar",
    palette = "RdPu",
    title = "Electric Cars",
    style = "kmeans"
  )Copy Code

Re-Placing the Legend

tm_shape(attributed_cologne) +
  tm_polygons(
    "ecar",
    palette = "RdPu",
    title = "Electric Car",
    style = "kmeans"
  ) +
  tm_layout(
    legend.outside = TRUE
  )Copy Code

Compasses & Scale Bars

tm_shape(attributed_cologne) +
  tm_polygons("ecar") +
  tm_scale_bar() +
  tm_compass()Copy Code

tm_shape(attributed_cologne) +
  tm_polygons("ecar") +
  tm_scale_bar(position = "left") +
  tm_compass(type = "4star")Copy Code

What's Left?

Getting Interactive (as an Alternative To mapview::mapview())!

ecar_map <-
tm_shape(attributed_cologne) +
  tm_borders(col = "black") +
  tm_polygons("ecar")
tmap_mode("view")
ecar_mapCopy Code

Facets I

 tm_shape(attributed_cologne) +
    tm_polygons(c("cdu", "spd", "greens", "afd", "left", "fdp")) +
    tm_facets( ncol = 3)Copy Code

Facets II

attributed_cologne %>%
  dplyr::mutate(city_area = as.integer(substr(id, 1, 1))) %>% 
  tm_shape(.) +
    tm_polygons(c("ecar")) +
    tm_facets(by = "city_area", nrow = 2) +
    tm_layout(panel.labels = c("1 Innenstadt","2 Rodenkirchen","3 Lindenthal",
                                "4 Ehrenfeld", "5 Nippes","6 Chorweiler",
                                "7 Porz", "8 Kalk", "9 Mülheim"))Copy Code

Change Style

tm_shape(attributed_cologne) +
    tm_polygons("ecar") +
  tm_style("classic")Copy Code

# other available styles are: "beaver", "bw", "watercolor" ,
# "white", "gray", "natural", "cobalt", "col_blind", "albatross",Copy Code

Color Vision Deficiencies: Standard Palette

Created with the package colorBlindness

Color Vision Deficiencies: Viridis Palette

Created with the package colorBlindness

Animation

tm_animate allows you to create animated maps and visualize changes in spatial data, e.g. over time or attributes. You can customize the animation's duration, frame rate, timeline sliders, and transition effects.

vote_gif <- 
  attributed_cologne %>% 
   tidyr::pivot_longer(., 
    cols = c("cdu", "spd", "greens", "afd", 
             "left", "fdp"),  # Select all columns to pivot
    names_to = "party_id",  # New column name for party identifier
    values_to = "voteshare") # New column name for vote shares %>% 
    tm_shape(.) +
    tm_polygons(c("voteshare")) +
    tm_facets(along = "party_id")  
tmap_animation(vote_gif, 
               filename = "./content/img/vote_gif.gif", 
               delay=150)Copy Code

Save Maps

tmap offers support for various output formats, including:

• Static Images: PNG, JPEG, TIFF, etc.
• Interactive Web Maps: HTML, JavaScript (using packages like leaflet).
• Animated Maps: GIF, MP4, etc.

You can also control width and height, dpi, etc.

# save regular map
tmap_save(ecar_map, filename = "ecar_map.png")
# save as interactive map
tmap_save(ecar_map, filename = "ecar_map.html" )Copy Code

Note On Mapping Responsible

Maps can shape narratives

• Decisions on which projection you use (remember the true size projector?),
• the segment of the world you choose,
• and the colors and styles you add have a strong influence.

Example: Kenneth Field's blog post

More Resources

If you want to dive deeper into mapping with tmap check out:

• Introduction by Thomas Lo Russo
• Blogpost by Hollie Zevross

And if you want to get some inspiration, keep an eye out for the #30DayMapChallenge on Twitter. Repository of Last Year's Challenge here.

Map Examples: E-Mobility

Instead of placing maps next to each other, we can also explore spatial correlations by adding layers. Another benefit of layering: We can make our map more realistic and aesthetic.

For example, we can add a background layer of neighboring countries, a layer of German cities, or the German states' borders for orientation. Each layer can visualize another attribute of our map.

For today, we want to add a layer of charging stations to the electric car map.

E-Mobility and Charging Stations

# map e-car share
ecar_map <- 
  tm_shape(german_districts_enhanced) + 
  tm_polygons(col = 
            "ecar_share",
          title = 
            "Electric Car Share",
          palette = "viridis",
          n=10,
          style = "jenks"
          ) +
  tm_layout(main.title = 
              "Electric Cars in Germany",
            main.title.color = 
              "black" ) +
  tm_legend(legend.outside = 
              TRUE,
            legend.outside.position = 
              "left")
ecar_mapCopy Code

E-Mobility and Charging Stations

# map the charging stations on the e-car map
ecar_chargers_map <- 
  tm_shape(german_districts_enhanced) + 
  tm_polygons(col = 
            "ecar_share",
          title = 
            "Electric Car Share",
          palette = "viridis",
          n = 10,
         style = "jenks"
          ) +
  tm_layout(main.title = 
              "Electric Cars in Germany",
            main.title.color = 
              "black" ) +
  tm_legend(legend.outside = 
              TRUE,
            legend.outside.position = 
              "left") +
  # Add charging point data
  tm_shape(chargers_sf) + 
  tm_dots("power_kw", 
          col = 
            "lightgrey",
          alpha = 0.1,
          title.size = 
            "Power of Charging Station (kW)") 
ecar_chargers_mapCopy Code

German Federal Election 2021

The Corresponding Code

# With Credits to Nils Jungmann
## District Shapefile
german_districts <- 
  # load data
  sf::read_sf("./data/VG250_KRS.shp") %>% 
  # transform to correct crs
  sf::st_transform(crs = 3035) %>% 
  # subsetting land area of Germany
  dplyr::filter(GF == 4) %>% 
  # only keep necessary cols
  dplyr::select(AGS,geometry) 
## BL Shapefile
german_states <-
  sf::read_sf("./data/VG250_LAN.shp") %>%  
  # transform to correct crs
  sf::st_transform(crs = 3035) %>% 
  # subsetting land area of Germany
  dplyr::filter(GF == 4) %>% 
  # only keep necessary cols
  dplyr::select(AGS,geometry) 
## German Federal Election Results 21 
### Abfrage bundeswahlleiter, 28.02.22
### Wahlergebnisse nach kreisfreien Städten und Landkeisen
### https://www.bundeswahlleiter.de/bundestagswahlen/2021/ergebnisse/weitere-ergebnisse.html
btw21_krs <- 
  # load data
  read.csv2("./data/btw2021kreis_zweitstimme.csv",
                   header= T,
                   fill = T,
                   sep= ";",
                   dec = ",",   
                   colClasses=c("character","character",rep("numeric",44)),
                   encoding = "UTF-8", 
                   strip.white = TRUE) %>% 
  # correct name first col 
  dplyr::rename(., AGS = 1)  %>% 
  # compute vote shares
  dplyr::mutate(
    AGS = stringr::str_pad(.$AGS, 5, pad = "0"),
    turnout = ((Ungültige + Gültige)/Wahlberechtigte) *100,
    union_shr = ((CDU + CSU) / Gültige) * 100,
    afd_shr = (AfD / Gültige) * 100,
    fdp_shr = (FDP / Gültige) * 100,
    spd_shr = (SPD / Gültige) * 100,
    linke_shr = (DIE.LINKE / Gültige) * 100,
    green_shr = (GRÜNE / Gültige) * 100,
    sonstige_shr = (rowSums(.[14:46])/Gültige) * 100) %>% 
  # select necessary vars
  dplyr::select(AGS,turnout, ends_with(c("_shr"))) %>% 
  # remove mean over all districts
  dplyr::filter(. , AGS != "00000") %>% 
  # aggregate east and west berlin
  dplyr::mutate(. , AGS = ifelse(AGS == "11100" | AGS == "11200", 
                                 "11000", AGS)) %>%
  dplyr::group_by(AGS) %>% 
  dplyr::summarise(across(everything(), list(mean))) %>% 
  dplyr::rename_with(~stringr::str_remove(., '_1'))
## correct for district that was reformed in 2022
btw21_krs <-
  btw21_krs %>% 
  dplyr::filter(., AGS == "16063") %>% 
  dplyr::mutate( AGS = "16056") %>% 
  dplyr::bind_rows(btw21_krs,.)
## join election results to shapefile
german_districts_btw <- 
  german_districts %>% 
  dplyr::left_join(. , btw21_krs, by = "AGS") 
# Prepare for mapping: Dataset for each party plus color
union <-
  german_districts_btw %>% 
  dplyr::select(union_shr, geometry) %>% 
  dplyr::mutate(party = "Union",
         color = "#030303") %>% 
  dplyr::rename(party_shr = union_shr)
spd <-
  german_districts_btw %>% 
  dplyr::select(spd_shr, geometry) %>% 
  dplyr::mutate(party = "SPD",
         color = "#FF0000") %>% 
  dplyr::rename(party_shr = spd_shr)
fdp <-
  german_districts_btw %>% 
  dplyr::select(fdp_shr, geometry) %>% 
  dplyr::mutate(party = "FDP",
         color = "#FFFF00") %>% 
  dplyr::rename(party_shr = fdp_shr)
gruene <-
  german_districts_btw %>% 
  dplyr::select(green_shr, geometry) %>% 
  dplyr::mutate(party = "Gruene",
         color = "#66CD00") %>% 
  dplyr::rename(party_shr = green_shr)
linke <-
  german_districts_btw %>% 
  dplyr::select(linke_shr, geometry) %>% 
  dplyr::mutate(party = "Linke",
         color = "#8A2BE2") %>% 
  dplyr::rename(party_shr =linke_shr)
afd <-
  german_districts_btw %>% 
  dplyr::select(afd_shr, geometry) %>% 
  dplyr::mutate(party = "AfD",
         color = "#1E90FF") %>% 
  dplyr::rename(party_shr = afd_shr)
# bind the datasets
party_shares <- rbind(union, spd, fdp, gruene, linke, afd)
# plot
## nested dataset
by_party <- party_shares %>% 
  dplyr::group_by(party, color) %>% 
  tidyr::nest()
## add list of party colors for figures
party_colors <- c("#030303", "#FF0000", "#FFFF00", "#66CD00",
                  "#8A2BE2", "#1E90FF")
# custom party color palettes
parties <- c("Union", "SPD", "FDP", "GRÜNE", "LINKE", "AfD")
party_palette_fcts <- purrr::map(party_colors, ~ colorRampPalette(c("#FFFFFF",.x)))
names(party_palette_fcts) <- paste0(parties,"_palette")
union_palette <- party_palette_fcts[[1]](10)
spd_palette <- party_palette_fcts[[2]](10)
fdp_palette <- party_palette_fcts[[3]](10)
gruene_palette <- party_palette_fcts[[4]](10)
linke_palette <- party_palette_fcts[[5]](10)
afd_palette <- party_palette_fcts[[6]](10)
# labels for vote shares
vs_labels <- c("0 bis 5", "5 bis 10", "10 bis 15", "15 bis 20", "20 bis 25",
               "25 bis 30", "30 bis 35", "35 bis 40", "40 bis 45", "45 bis 50")
# maps
# maps
uni_btwshare_map <- tm_shape(subset(party_shares, party == "Union")) +
  tm_polygons("party_shr",
              title = "CDU/CSU",
              palette = union_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T, 
            frame = F)
spd_btwshare_map <- tm_shape(subset(party_shares, party == "SPD")) +
  tm_polygons("party_shr",
              title = "SPD",
              palette = spd_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T,
            frame = F)
fdp_btwshare_map <- tm_shape(subset(party_shares, party == "FDP")) +
  tm_polygons("party_shr",
              title = "FDP",
              palette = fdp_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T, 
            frame = F)
gru_btwshare_map <- tm_shape(subset(party_shares, party == "Gruene")) +
  tm_polygons("party_shr",
              title = "Grüne",
              palette = gruene_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T,
            frame = F)
lin_btwshare_map <- tm_shape(subset(party_shares, party == "Linke")) +
  tm_polygons("party_shr",
              title = "DIE LINKE",
              palette = linke_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T, 
            frame = F)
afd_btwshare_map <- tm_shape(subset(party_shares, party == "AfD")) +
  tm_polygons("party_shr",
              title = "AfD",
              palette = afd_palette,
              labels = vs_labels,
              style = "fixed",
              n = 10,
              breaks = c(0,5,10,15,20,25,30,35,40,45,50),
              border.alpha = 0) +
  tm_shape(german_states) +
  tm_polygons("AGS",
              alpha = 0,
              border.col = "lightgrey",
              legend.show = F) +
  tm_layout(legend.title.size = .8,
            legend.outside =  T, 
            frame = F)
# combine maps
all_btwshare_map <- tmap_arrange(uni_btwshare_map, spd_btwshare_map, fdp_btwshare_map,
                                 gru_btwshare_map, lin_btwshare_map, afd_btwshare_map,
                                 ncol = 3)  
all_btwshare_map
# tmap_save(all_btwshare_map, filename = "party_vote_shares_district_map.eps", dpi = 600)Copy Code