Visualizing Immigration to Canada using a Choropleth Map

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 Chropleth 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

/usr/local/lib/python3.6/dist-packages/folium/folium.py:426: FutureWarning: The choropleth  method has been deprecated. Instead use the new Choropleth class, which has the same arguments. See the example notebook 'GeoJSON_and_choropleth' for how to do this.
  FutureWarning

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. Note: The countries that are denoted in grey color have been excluded from the list.