Philippa Markovics / Apr 16 2019

World Population Density

This notebook shows the world’s population density in 2017 as choropleth map. It is based on a data set provided (as CSV file) by the World Bank which is uploaded here:

World Population Density.csv

Cleaning the Data

In a first step, the R programming language and tidyverse library are used to prepare the CSV file provided by the World Bank for plotting.

library("tidyverse")
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The CSV’s first 4 lines are skipped because they contain meta information that is irrelevant to the visualization.

data <- read_csv(
World Population Density.csv
, skip=4)
data
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Looking at the data reveals there are plenty of NA values that need to be removed and the data set will be reduced to include only what the plot needs:

data <- data %>%
	select(c(`Country Name`, `Country Code`, `2017`)) %>%
	rename(Density="2017") %>%
	drop_na()
data
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Once the data is in a useful form, it will be saved out to a new CSV file to be used for visualization using Python and plotly later. Saving it to /results will put it into Nextjournal’s content-addressed storage and makes the file available to other programming language runtimes, like Python:

write.csv(data, file="/results/cleaned.csv")
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Population Density Map

Finally, Python and pandas are used to read in our cleaned CSV as dataframe. plotly takes care of rendering an interactive choropleth map:

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import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import pandas as pd

df = pd.read_csv(
cleaned.csv
)
data = [go.Choropleth(
	locations = df['Country Code'],
  z = df['Density'],
  zmin = 0.0,
  zmax = 300.0,
  colorscale = 'Reds',
	text = df['Country Name'],
  marker = go.choropleth.Marker(
  	line = go.choropleth.marker.Line(color = 'rgb(180,180,180)', width = 0.5)
  ),
  colorbar = go.choropleth.ColorBar(
    title = 'People per sqkm'
  ),
)]
layout = go.Layout(
  geo = go.layout.Geo(
		showframe = False,
		showcoastlines = False,
		projection = go.layout.geo.Projection(type = 'mercator')
  ),
  margin = go.layout.Margin(
    l = 0, r = 0, b = 0, t = 0, pad = 0
  )
)

go.Figure(data = data, layout = layout)
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