Visualizing immigration to Canada

Creating a choropleth map of the world depicting immigration from various countries to Canada
visualization
Published

October 31, 2019

A choropleth map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map. It provides an easy way to visualize how a measurement varies across a geographical area or it shows the level of variability within a region.

In this blog post, we will create a choropleth map of the world depicting immigration from various countries to Canada. The dataset is officially collected by the United Nations and contains the international migrant flows to Canada from other countries from the years 1980 to 2013. You can download the dataset here.

# import libraries
import pandas as pd
import numpy as np
import folium
# read the data into a pandas dataframe
df = pd.read_excel('Canada.xlsx', skiprows=range(20), skipfooter=2)
df.head()
Type Coverage OdName AREA AreaName REG RegName DEV DevName 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
0 Immigrants Foreigners Afghanistan 935 Asia 5501 Southern Asia 902 Developing regions 16 39 39 47 71 340 496 741 828 1076 1028 1378 1170 713 858 1537 2212 2555 1999 2395 3326 4067 3697 3479 2978 3436 3009 2652 2111 1746 1758 2203 2635 2004
1 Immigrants Foreigners Albania 908 Europe 925 Southern Europe 901 Developed regions 1 0 0 0 0 0 1 2 2 3 3 21 56 96 71 63 113 307 574 1264 1816 1602 1021 853 1450 1223 856 702 560 716 561 539 620 603
2 Immigrants Foreigners Algeria 903 Africa 912 Northern Africa 902 Developing regions 80 67 71 69 63 44 69 132 242 434 491 872 795 717 595 1106 2054 1842 2292 2389 2867 3418 3406 3072 3616 3626 4807 3623 4005 5393 4752 4325 3774 4331
3 Immigrants Foreigners American Samoa 909 Oceania 957 Polynesia 902 Developing regions 0 1 0 0 0 0 0 1 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
4 Immigrants Foreigners Andorra 908 Europe 925 Southern Europe 901 Developed regions 0 0 0 0 0 0 2 0 0 0 3 0 1 0 0 0 0 0 2 0 0 1 0 2 0 0 1 1 0 0 0 0 1 1 
# dimensions of the data
df.shape
(195, 43)

Preprocessing

Let’s clean up the data.

# remove the unnecessary columns
df.drop(['AREA', 'REG', 'DEV', 'Type', 'Coverage'], axis=1, inplace=True)
# rename the columns for simplicity
df.rename(columns={'OdName':'Country', 'AreaName':'Continent', 'RegName':'Region'}, inplace=True)
# make all column labels of type string for consistency
df.columns = list(map(str, df.columns))

We’ll add a ‘Total’ column to the dataset, which will sum the population from each country throughout the years.

df['Total'] = df.sum(axis=1)

Create a ‘years’ variable which we will use later for plotting.

years = list(map(str, range(1980, 2014)))
# new dimensions of the dataset
df.shape
(195, 39)
df.head()
Country Continent Region DevName 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Total
0 Afghanistan Asia Southern Asia Developing regions 16 39 39 47 71 340 496 741 828 1076 1028 1378 1170 713 858 1537 2212 2555 1999 2395 3326 4067 3697 3479 2978 3436 3009 2652 2111 1746 1758 2203 2635 2004 58639
1 Albania Europe Southern Europe Developed regions 1 0 0 0 0 0 1 2 2 3 3 21 56 96 71 63 113 307 574 1264 1816 1602 1021 853 1450 1223 856 702 560 716 561 539 620 603 15699
2 Algeria Africa Northern Africa Developing regions 80 67 71 69 63 44 69 132 242 434 491 872 795 717 595 1106 2054 1842 2292 2389 2867 3418 3406 3072 3616 3626 4807 3623 4005 5393 4752 4325 3774 4331 69439
3 American Samoa Oceania Polynesia Developing regions 0 1 0 0 0 0 0 1 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 6
4 Andorra Europe Southern Europe Developed regions 0 0 0 0 0 0 2 0 0 0 3 0 1 0 0 0 0 0 2 0 0 1 0 2 0 0 1 1 0 0 0 0 1 1 15 
# download countries geojson file
!wget --quiet https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/world_countries.json -O world_countries.json
    
print('GeoJSON file downloaded!')
GeoJSON file downloaded!

Now that we have the GeoJSON file, let’s create a world map, centered around [0, 0] latitude and longitude values, with an intial zoom level of 2, and using Mapbox Bright style.

world_geo = r'world_countries.json' # geojson file

# create a plain world map
world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')

Create a choropleth map.

# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013
world_map.choropleth(
    geo_data=world_geo,
    data=df,
    columns=['Country', 'Total'],
    key_on='feature.properties.name',
    fill_color='YlOrRd', 
    fill_opacity=0.7, 
    line_opacity=0.2,
    legend_name='Immigration to Canada'
)

# display map
world_map

Immigration to Canada by Country

As per the map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, Great Britain, and the Philippines, followed by Pakistan, the US, and Poland.