But be mindful that the colors might not correspond to the point sizes since we use a continuous scale and the two features can vary independently of each other. Next, we use the size as the main aesthetics feature and select a color value from our palette.Īnother option would be to define the color as the aesthetics feature and use the whole palette. Mapping = aes (x = long, y = lat, label = NAME_LATN ) ,įinally, we are ready to create the bubble map! We also use polygons in the background though some of you may want to remove this line. For simplicity’s sake, we transform it to thousands and define the limits and breaks. So, instead of population per square kilometer we use population count. This helps us reduce the bias caused by different area size in choropleth maps where big regions tend to steal our attention. Every circle is going to be centered on their respective region to show population size. In our case, we will use circles to represent regions. Similar to choropleth maps, bubbles visualize the size of geographic coordinates or larger geographic units. Keyheight = unit ( 1.15, units = "mm" ) ,Īnother common way to display population density is a bubble map. In this tutorial, we first summon several essential packages: tidyverse and sf for spatial analysis and data wrangling package classInt will help us construct fine breaks based on equally sized data points cartogram package assists us in creating pretty cartogram maps while rayshader is instrumental for making spike maps, which we have already used here finally, we’ll use giscoR and eurostat library to import Eurostat boundaries and data into R. We start off with several standard techniques such as polygons, points and cartogram and conclude with coooler alternatives such as the dot-density plot and spike map.īuckle up, this is going to be a long one! Load packages These data are organized on the regional level, allowing us to chart out the within-country variation. If I had a penny for every time this popped up in my feed, I’d be filthy rich □.Īnyways, we’ll use Eurostat’s latest population data on the NUTS3 level (caveat reader, it’s not what you think) to map the population of Germany□□. Some would even argue that nearly every map is a population map. Mapping population comes in different forms and it’s one of the most popular social media topics. In this tutorial, I’ll show you 6 easy ways to map population with R. In the coming years, the growth will be concentrated in 9 countries in Africa, Americas and Asia. Our world has reached 8 billion people last November, according to the United Nations! □ And this report expects the world to become a host to nearly 10 billion people by 2050, and over 11 billion in 2100. 6 easy ways to map population density in R Janu| Milos Popovic
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