Choropleth Maps

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From the course by IBM

Data Visualization with Python

101 ratings

IBM

Data Visualization with Python

101 ratings

Course 2 of 4 in the Specialization Applied Data Science

"A picture is worth a thousand words". We are all familiar with this expression. It especially applies when trying to explain the insight obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data. One of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way. Learning how to leverage a software tool to visualize data will also enable you to extract information, better understand the data, and make more effective decisions. The main goal of this Data Visualization with Python course is to teach you how to take data that at first glance has little meaning and present that data in a form that makes sense to people. Various techniques have been developed for presenting data visually but in this course, we will be using several data visualization libraries in Python, namely Matplotlib, Seaborn, and Folium.

From the lesson

Advanced Visualizations and Geospatial Data

In this module, you will learn about advanced visualization tools such as waffle charts and word clouds and how to create them. You will also learn about seaborn, which is another visualization library, and how to use it to generate attractive regression plots. In addition, you will learn about Folium, which is another visualization library, designed especially for visualizing geospatial data. Finally, you will learn how to use Folium to create maps of different regions of the world and how to superimpose markers on top of a map, and how to create choropleth maps.

Introduction to Folium2:35

Maps with Markers2:21

Choropleth Maps4:21

Meet the Instructors

Alex Aklson
Ph.D., Data Scientist
IBM Developer Skills Network

In this video, we will learn how to create a special type of map called

choropleth map with Folium. I'm sure that most of you have seen maps similar to

this one and this one. These are what we call choropleth maps. So what is 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, such as population density or per capita income.

The higher the measurement the darker the color. The map to the left is a

choropleth map of the world showing infant mortality rate per 1000 births.

The darker the color the higher the infant mortality rate. According to the

map, African countries have very high infant mortality rates with some of them

reporting a rate that is higher than 160 per 1000 births.

Similarly, the map to the right is a choropleth map of the US showing

population per square mile by state. Again the darker the color the higher

the population. According to the map, states in the eastern part of the US

tend to be more populous than states in the western part, with California being

an exception. In order to create a choropleth map of a region of interest,

Folium requires a Geo JSON file that includes geospatial data of the region.

For a choropleth map of the world, we would need a Geo JSON file that lists

each country along with any geospatial data to define its borders and

boundaries. Here is an example of what the Geo JSON file would include about

each country. The example here pertains to the country Brunei. As you can see, the

file includes the country's name, it's ID, geometry shape, and the coordinates that

define the country's borders and boundaries. So let's see how we can

create a choropleth map of the world like this one showing immigration to

Canada. Before we go over the code to do that, let's do a

quick recap of our dataset. Recall that each row represents a country and

contains metadata about the country such as where it is located geographically

and whether it is developing or developed. Each row also contains

numerical figures of annual immigration from that country to Canada from 1980 to

2013. Now let's process the data and let's add

an extra column which represents the cumulative sum of annual immigration from

each country from 1980 to 2013. So for Afghanistan for example,

it is 58,639, total, and for Albania it is 15,699,

and so on. And let's name our dataframe df_canada.

So now that we know how our data is stored in the dataframe,

df_canada, let's see how we can generate a choropleth map of the

world showing immigration to Canada. We should be experts now in creating world

maps with Folium so let's go ahead and create a world map but this time let's

use the mapbox bright tiles set. The result is a nice world map displaying

the name of every country. Now to convert this map into a choropleth map, we first

define a variable that points to our Geo JSON file. Then we apply the choropleth

function to our world map and we tell it to use the columns "Country" and "Total" in

our df_canada dataframe, and to use the country names to look up the

geospatial information about each country in the Geo JSON file. And there

you have it. A choropleth map of Canada showing the intensity of immigration

from different countries worldwide. In the lab session, we explore choropleth

maps in more details, so please make sure to complete this module's lab session. And

with this, we conclude our video on choropleth maps.

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