The Shifting Speeds of Urbanization (1960-2023)

Annual growth rates show when countries urbanized fastest and when they slowed down

30DayChartChallenge

2025

Exploring the dynamic rates of urbanization across eight countries from 1960 to 2023 using World Bank data. This visualization reveals when countries underwent their most rapid urban transitions and when urbanization slowed or reversed.

Published

April 20, 2025

Figure 1: A faceted area chart titled ‘The Shifting Speeds of Urbanization (1960-2023)’ showing annual percentage point changes in urban population for eight countries. Countries are arranged by their maximum rate of urbanization, with Japan showing the highest spike (over 1.5%) around 2000, followed by China with sustained high rates, and Brazil with early high rates that gradually decreased. Nigeria, India, Egypt, the United States, and Germany show more moderate urbanization rates. Negative urbanization periods appear in orange for countries like China, Egypt, and Germany.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  WDI,            # World Development Indicators and Other World Bank Data
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code

# Search for urbanization indicators
urbanization_indicators <- WDIsearch("urban population")
# head(urbanization_indicators)

# Select countries of interest
countries <- c("USA", "CHN", "IND", "BRA", "NGA", "DEU", "JPN", "EGY")

# Get the data
urban_data <- WDI(
  indicator = "SP.URB.TOTL.IN.ZS",    # Urban population (% of total population)
  country = countries,
  start = 1960,
  end = 2024,  
  extra = TRUE  
)

3. Examine the Data

Show code

glimpse(urban_data)
skim(urban_data)

4. Tidy Data

Show code

### |- Tidy ----
# Clean and prepare the initial dataset
urban_data_clean <- urban_data |>
  select(country, year, SP.URB.TOTL.IN.ZS, region) |>
  rename(urban_population_pct = SP.URB.TOTL.IN.ZS) |>
  filter(!is.na(urban_population_pct))

# Calculate annual change rates
urban_change <- urban_data_clean |>
  arrange(country, year) |>
  group_by(country) |>
  mutate(change_rate = c(NA, diff(urban_population_pct))) |>
  filter(!is.na(change_rate)) |> 
  ungroup()

# Calculate maximum change for each country (for ordering)
country_max_change <- urban_change |>
  group_by(country) |>
  summarize(max_change = max(change_rate, na.rm = TRUE)) |>
  arrange(desc(max_change)) |> 
  ungroup()

# Apply the ordering to each country
urban_change <- urban_change |>
  mutate(country = factor(country, levels = country_max_change$country))

# Create separate dataframes for positive and negative changes
urban_change_pos <- urban_change |>
  mutate(y_value = pmax(0, change_rate))

urban_change_neg <- urban_change |>
  mutate(y_value = pmin(0, change_rate))

5. Visualization Parameters

Show code

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "pos_color" = "#1B9E77",
    "neg_color" = "#D95F02"  
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("The Shifting Speeds of Urbanization (1960-2023)",
                          width = 55) 

subtitle_text <- str_wrap("Annual growth rates show when countries urbanized fastest and when they slowed down",
                          width = 85)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 20,
  source_text =  "{ WDI } World Bank data in R" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Axis elements
    axis.title.y = element_text(color = colors$text, size = rel(0.8), margin = margin(r = 10)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major.y = element_line(color = "gray92"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_blank(),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Facet elements
    strip.background = element_rect(fill = "gray90", color = NA),
    strip.text = element_text(face = "bold", size = rel(1), margin = margin(10, 0, 10, 0)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code

### |-  Plot ----
p <- ggplot() +
  # Geoms
  geom_area(
    data = urban_change_pos, 
    aes(x = year, y = y_value), 
    fill = colors$palette[1], alpha = 0.7
  ) +
  geom_area(
    data = urban_change_neg, 
    aes(x = year, y = y_value), 
    fill = colors$palette[2], alpha = 0.7 
  ) +
  geom_line(
    data = urban_change, 
    aes(x = year, y = change_rate), 
    color = "black", 
    linewidth = 0.5
  ) +
  geom_hline(
    yintercept = 0, 
    linetype = "dashed", 
    color = "black", 
    linewidth = 0.5
  ) +
  # Scales
  scale_x_continuous(
    breaks = seq(1960, 2020, by = 20)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Year",
    y = "Change in Urban Population (%)"
  ) +
  # Facet 
  facet_wrap(~ country, ncol = 3)  +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_text(
      size = rel(0.85),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.2,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code

### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 20, 
  width = 8, 
  height = 8
  )

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 WDI_2.7.9       scales_1.3.0   
 [5] skimr_2.1.5     janitor_2.2.0   showtext_0.9-7  showtextdb_3.0 
 [9] sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0  
[13] stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2     readr_2.1.5    
[17] tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.4.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] gifski_1.32.0-1   fansi_1.0.6       pacman_0.5.1      pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     repr_1.1.7        codetools_0.2-20  snakecase_0.11.1 
[21] htmltools_0.5.8.1 yaml_2.3.10       pillar_1.9.0      magick_2.8.5     
[25] commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37     stringi_1.8.4    
[29] labeling_0.4.3    rsvg_2.6.1        rprojroot_2.0.4   fastmap_1.2.0    
[33] grid_4.4.0        colorspace_2.1-1  cli_3.6.3         magrittr_2.0.3   
[37] base64enc_0.1-3   utf8_1.2.4        withr_3.0.2       timechange_0.3.0 
[41] rmarkdown_2.29    ragg_1.3.3        hms_1.1.3         evaluate_1.0.1   
[45] knitr_1.49        markdown_1.13     rlang_1.1.4       gridtext_0.1.5   
[49] Rcpp_1.0.13-1     glue_1.8.0        xml2_1.3.6        renv_1.0.3       
[53] svglite_2.1.3     rstudioapi_0.17.1 jsonlite_1.8.9    R6_2.5.1         
[57] systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in 30dcc_2025_20.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:
- World Bank { World Bank data in R } indicator = Urban population (% of total population) (“SP.URB.TOTL.IN.ZS”)

--- title: "The Shifting Speeds of Urbanization (1960-2023)" subtitle: "Annual growth rates show when countries urbanized fastest and when they slowed down" description: "Exploring the dynamic rates of urbanization across eight countries from 1960 to 2023 using World Bank data. This visualization reveals when countries underwent their most rapid urban transitions and when urbanization slowed or reversed." date: "2025-04-20" categories: ["30DayChartChallenge", "2025"] tags: [ "ggplot2", "timeseries", "urbanization", "World Bank", "WDI", "area chart", "demographic change", "urban development", "global trends", "comparative analysis" ] image: "thumbnails/30dcc_2025_20.png" format: html: toc: true toc-depth: 5 code-link: true code-fold: true code-tools: true code-summary: "Show code" editor_options: chunk_output_type: inline execute: freeze: true cache: true error: false message: false warning: false eval: true # filters: # - social-share # share: # permalink: "https://stevenponce.netlify.app/data_visualizations/30DayChartChallenge/2025/30dcc_2025_20.html" # description: "Day 20 of #30DayChartChallenge: Visualizing how urbanization rates have shifted across countries from 1960-2023, showing the dramatic differences in when and how quickly nations urbanized." # twitter: true # linkedin: true # email: true # facebook: false # reddit: false # stumble: false # tumblr: false # mastodon: true # bsky: true --- ![A faceted area chart titled 'The Shifting Speeds of Urbanization (1960-2023)' showing annual percentage point changes in urban population for eight countries. Countries are arranged by their maximum rate of urbanization, with Japan showing the highest spike (over 1.5%) around 2000, followed by China with sustained high rates, and Brazil with early high rates that gradually decreased. Nigeria, India, Egypt, the United States, and Germany show more moderate urbanization rates. Negative urbanization periods appear in orange for countries like China, Egypt, and Germany.](30dcc_2025_20.png){#fig-1} ### <mark> **Steps to Create this Graphic** </mark> #### 1. Load Packages & Setup ```{r} #| label: load #| warning: false #| message: false #| results: "hide" ## 1. LOAD PACKAGES & SETUP ---- suppressPackageStartupMessages({ pacman::p_load( tidyverse, # Easily Install and Load the 'Tidyverse' ggtext, # Improved Text Rendering Support for 'ggplot2' showtext, # Using Fonts More Easily in R Graphs janitor, # Simple Tools for Examining and Cleaning Dirty Data skimr, # Compact and Flexible Summaries of Data scales, # Scale Functions for Visualization lubridate, # Make Dealing with Dates a Little Easier WDI, # World Development Indicators and Other World Bank Data camcorder # Record Your Plot History ) }) ### |- figure size ---- gg_record( dir = here::here("temp_plots"), device = "png", width = 8, height = 8, units = "in", dpi = 320 ) # Source utility functions suppressMessages(source(here::here("R/utils/fonts.R"))) source(here::here("R/utils/social_icons.R")) source(here::here("R/utils/image_utils.R")) source(here::here("R/themes/base_theme.R")) ``` #### 2. Read in the Data ```{r} #| label: read #| include: true #| eval: true #| warning: false # Search for urbanization indicators urbanization_indicators <- WDIsearch("urban population") # head(urbanization_indicators) # Select countries of interest countries <- c("USA", "CHN", "IND", "BRA", "NGA", "DEU", "JPN", "EGY") # Get the data urban_data <- WDI( indicator = "SP.URB.TOTL.IN.ZS", # Urban population (% of total population) country = countries, start = 1960, end = 2024, extra = TRUE ) ``` #### 3. Examine the Data ```{r} #| label: examine #| include: true #| eval: true #| results: 'hide' #| warning: false glimpse(urban_data) skim(urban_data) ``` #### 4. Tidy Data ```{r} #| label: tidy #| warning: false ### |- Tidy ---- # Clean and prepare the initial dataset urban_data_clean <- urban_data |> select(country, year, SP.URB.TOTL.IN.ZS, region) |> rename(urban_population_pct = SP.URB.TOTL.IN.ZS) |> filter(!is.na(urban_population_pct)) # Calculate annual change rates urban_change <- urban_data_clean |> arrange(country, year) |> group_by(country) |> mutate(change_rate = c(NA, diff(urban_population_pct))) |> filter(!is.na(change_rate)) |> ungroup() # Calculate maximum change for each country (for ordering) country_max_change <- urban_change |> group_by(country) |> summarize(max_change = max(change_rate, na.rm = TRUE)) |> arrange(desc(max_change)) |> ungroup() # Apply the ordering to each country urban_change <- urban_change |> mutate(country = factor(country, levels = country_max_change$country)) # Create separate dataframes for positive and negative changes urban_change_pos <- urban_change |> mutate(y_value = pmax(0, change_rate)) urban_change_neg <- urban_change |> mutate(y_value = pmin(0, change_rate)) ``` #### 5. Visualization Parameters ```{r} #| label: params #| include: true #| warning: false ### |- plot aesthetics ---- colors <- get_theme_colors( palette = c( "pos_color" = "#1B9E77", "neg_color" = "#D95F02" ) ) ### |- titles and caption ---- # text title_text <- str_wrap("The Shifting Speeds of Urbanization (1960-2023)", width = 55) subtitle_text <- str_wrap("Annual growth rates show when countries urbanized fastest and when they slowed down", width = 85) caption_text <- create_dcc_caption( dcc_year = 2025, dcc_day = 20, source_text = "{ WDI } World Bank data in R" ) ### |- fonts ---- setup_fonts() fonts <- get_font_families() ### |- plot theme ---- # Start with base theme base_theme <- create_base_theme(colors) # Add weekly-specific theme elements weekly_theme <- extend_weekly_theme( base_theme, theme( # Axis elements axis.title.y = element_text(color = colors$text, size = rel(0.8), margin = margin(r = 10)), axis.text = element_text(color = colors$text, size = rel(0.7)), # Grid elements panel.grid.minor = element_blank(), panel.grid.major.x = element_blank(), panel.grid.major.y = element_line(color = "gray92"), # Legend elements legend.position = "plot", legend.title = element_blank(), legend.text = element_text(family = fonts$text, size = rel(0.7)), # Facet elements strip.background = element_rect(fill = "gray90", color = NA), strip.text = element_text(face = "bold", size = rel(1), margin = margin(10, 0, 10, 0)), # Plot margins plot.margin = margin(t = 10, r = 20, b = 10, l = 20), ) ) # Set theme theme_set(weekly_theme) ``` #### 6. Plot ```{r} #| label: plot #| warning: false ### |- Plot ---- p <- ggplot() + # Geoms geom_area( data = urban_change_pos, aes(x = year, y = y_value), fill = colors$palette[1], alpha = 0.7 ) + geom_area( data = urban_change_neg, aes(x = year, y = y_value), fill = colors$palette[2], alpha = 0.7 ) + geom_line( data = urban_change, aes(x = year, y = change_rate), color = "black", linewidth = 0.5 ) + geom_hline( yintercept = 0, linetype = "dashed", color = "black", linewidth = 0.5 ) + # Scales scale_x_continuous( breaks = seq(1960, 2020, by = 20) ) + # Labs labs( title = title_text, subtitle = subtitle_text, caption = caption_text, x = "Year", y = "Change in Urban Population (%)" ) + # Facet facet_wrap(~ country, ncol = 3) + # Theme theme( plot.title = element_text( size = rel(1.8), family = fonts$title, face = "bold", color = colors$title, margin = margin(t = 5, b = 5) ), plot.subtitle = element_text( size = rel(0.85), family = fonts$subtitle, color = colors$subtitle, lineheight = 1.2, margin = margin(t = 5, b = 15) ), plot.caption = element_markdown( size = rel(0.6), family = fonts$caption, color = colors$caption, lineheight = 0.65, hjust = 0.5, halign = 0.5, margin = margin(t = 10, b = 5) ), ) ``` #### 7. Save ```{r} #| label: save #| warning: false ### |- plot image ---- save_plot( p, type = "30daychartchallenge", year = 2025, day = 20, width = 8, height = 8 ) ``` #### 8. Session Info ::: {.callout-tip collapse="true"} ##### Expand for Session Info ```{r, echo = FALSE} #| eval: true #| warning: false sessionInfo() ``` ::: #### 9. GitHub Repository ::: {.callout-tip collapse="true"} ##### Expand for GitHub Repo The complete code for this analysis is available in [`30dcc_2025_20.qmd`](https://github.com/poncest/personal-website/blob/master/data_visualizations/TidyTuesday/2025/30dcc_2025_20.qmd). For the full repository, [click here](https://github.com/poncest/personal-website/). ::: #### 10. References ::: {.callout-tip collapse="true"} ##### Expand for References 1. Data Sources: - World Bank { World Bank data in R } [indicator = Urban population (% of total population) ("SP.URB.TOTL.IN.ZS")](https://github.com/vincentarelbundock/WDI) :::