China Urbanisation & Large cities

Main Chinese Cities data

We want to add on the map the main Chinese cities with their population. There are multiple sources for such data but most of them requires intensive data processing. This will be the topic of another article. For this post, let’s use available data in R and the world.cities data set of the packages maps.

data("world.cities")
## Get cities of China
mainCitiesOfChina <- world.cities[world.cities$country.etc == "China",]
## convert population in millions
mainCitiesOfChina$pop <- mainCitiesOfChina$pop / 1000000

Let’s have a look on the data.

# we use kable to generate an html table with nice formatting
knitr::kable(head(mainCitiesOfChina[mainCitiesOfChina$pop > 2,],4)) %>%
  kable_styling(
    bootstrap_options = c(
      "striped"
      , "hover"
      , "condensed"
      , "responsive"
      )
    ) %>% scroll_box(width = "100%")

Provinces, urbanisation rate choropleth

We should map the urbanization rate from the provincePopulation dataframe with province of the maps defined by ChinaLevel1. Let’s first check that all provinces are covered and names are matching.

a <- unique(provincePopulation[provincePopulation$variable == "Urbanisation.rate",]$Region)
b <- unique(ChinaLevel1$id)
all(a %in% b)

## [1] TRUE

This looks good, we can proceed and plot the map.

## get large version of the urbanisation dataset
provincesUrbanisation <- dcast(provincePopulation, Year + Region ~ variable, value.var = "value")
## Merge polygons and associated urbanisation data in one data frame by id (name of the province in chinese) and Region - Only for year 2014
urbanisationChoropleth <- merge(ChinaLevel1, provincesUrbanisation[provincesUrbanisation$Year == "2014",], by.x = "id", by.y = "Region")
rm(ChinaLevel1)

## Create the ggplot using standard approach
## group is necessary to draw in correct order, try without to understand the problem
g <- ggplot(
        urbanisationChoropleth,
        aes(x = long, y = lat, fill = Urbanisation.rate, group = group)
        )
g <- g + scale_fill_continuous(na.value = "grey80", low = "#ffdddd", high = "#ff3311", name = "Urbanization %")
## projected shadow
g <- g + geom_polygon(aes(x = long + 0.7, y = lat - 0.5), color = "grey50", size = 0.2, fill = "grey50")
## Province boundaries
g <- g + geom_polygon(color = "white", size = 0.2)
## to keep correct ratio in the projection
g <- g + coord_map()
g <- g + labs(title = "China - Urbanisation rate")
print(g)

Adding Largest cities

Then we just need to add another layer with bubbles for each cities larger than 2 millions inhabitants.

## Theme configuration, as simple a possible
r <- g + theme_bw() + theme(axis.text = element_blank(),
              axis.title = element_blank(),
              axis.ticks = element_blank(),
              legend.text = element_text(size = rel(0.7))
              )
## Bubles for cities, we superpose two type of geom_point, circles an disks with alpha
r <- r + geom_point(
        data = mainCitiesOfChina[mainCitiesOfChina$pop > 2,],
        aes(x = long, y = lat, size = pop, fill = NULL, group = NULL ),
        colour = "blue", alpha = 1, pch = 1
        )
r <- r + geom_point(
        data = mainCitiesOfChina[mainCitiesOfChina$pop > 2,],
        aes(x = long, y = lat, size = pop, fill = NULL, group = NULL ),
        colour = "blue", alpha = 0.2, pch = 16
        )
r <- r + scale_size_area(max_size = 12, breaks = c(2,5,10), name = "Population in millions")
## Add city names, hjust is a small adjustment for better readability
r <- r + geom_text(
        data = mainCitiesOfChina[mainCitiesOfChina$pop > 2,],
        aes(x = long, y = lat, label = name, size = 0.5, group = NULL, fill = NULL, hjust = -0.18),
        show.legend = FALSE
        )
r <- r + guides(colour = guide_legend(nrow = 2), size =  guide_legend(order = 1, nrow = 1))
r <- r + labs(title = "China urbanization rate and cities above 2 millions pop.")
print(r)

Source code

The source code of this post is available on github

Session information

sessionInfo()

## R version 3.6.1 (2017-01-27)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.6 LTS
## 
## Matrix products: default
## BLAS:   /home/travis/R-bin/lib/R/lib/libRblas.so
## LAPACK: /home/travis/R-bin/lib/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] dplyr_0.8.3      kableExtra_1.1.0 knitr_1.26       maps_3.3.0      
## [5] maptools_0.9-9   sp_1.3-2         ggplot2_3.2.1    reshape2_1.4.3  
## 
## loaded via a namespace (and not attached):
##  [1] tidyselect_0.2.5  xfun_0.11         purrr_0.3.3       lattice_0.20-38  
##  [5] colorspace_1.4-1  vctrs_0.2.0       htmltools_0.4.0   viridisLite_0.3.0
##  [9] yaml_2.2.0        rlang_0.4.2       pillar_1.4.2      foreign_0.8-72   
## [13] glue_1.3.1        withr_2.1.2       lifecycle_0.1.0   plyr_1.8.5       
## [17] stringr_1.4.0     rgeos_0.5-2       munsell_0.5.0     blogdown_0.17.1  
## [21] gtable_0.3.0      rvest_0.3.5       mapproj_1.2.6     codetools_0.2-16 
## [25] evaluate_0.14     labeling_0.3      highr_0.8         Rcpp_1.0.3       
## [29] readr_1.3.1       scales_1.1.0      backports_1.1.5   webshot_0.5.2    
## [33] farver_2.0.1      hms_0.5.2         digest_0.6.23     stringi_1.4.3    
## [37] bookdown_0.16     grid_3.6.1        tools_3.6.1       magrittr_1.5     
## [41] lazyeval_0.2.2    tibble_2.1.3      crayon_1.3.4      pkgconfig_2.0.3  
## [45] zeallot_0.1.0     xml2_1.2.2        assertthat_0.2.1  rmarkdown_1.18   
## [49] httr_1.4.1        rstudioapi_0.10   R6_2.4.1          compiler_3.6.1