DATA VISUALIZATION: CHOROPLETH INTERACTIVE MAP WITH PYTHON USING FOLIUM

Adeoluwa Adeboye — Sun, 26 Apr 2020 00:30:39 +0000

WHAT IS A CHOROPLETH MAP

A choropleth map is a map which uses differences in shading, colouring, or the placing of symbols within predefined areas to indicate the average values of a particular quantity in those areas (from oxford dictionary) as seen in the image below:

In this article, I will try to walk you through the process of creating a choropleth map with python using the folium package. Visualization with choropleth map helps to give insight to data and is visually attractive.
I will divide the processes into four short steps which will be followed sequentially.

STEP 1: SOURCING FOR SHAPEFILE IN .KML FORMAT

Inorder to generate a map visualization with folium we require a shapefile of the area of the world map we want to work with.
In this article I will be creating a visualization of the Nigeria map area, so the need to source for a shapefile in .KML format of the Nigeria map area having polygon coordinates of each state.
There are a lot of sites out there that simply supply this resource for free, for this visualization I got my shape file from IGISMAP, they also offer shapefiles for other countries.

STEP 2: CONVERSION OF SHAPEFILE TO A GEOJSON

Moving on from step 1, let us convert the .KML file to a Geojson file.
I used an online cloud resource to convert my .KML to a Geojson file, the name of the online resource is MYGEODATA.

STEP 3: INTEGRATION OF OUR GEOJSON WITH THE DATA

From this step forward we get hands on with some Python codes.
We start by installing Python Pandas and GeoPandas packages to help us integrate the data we need to visualize into the geojson file.
In this article I am visualizing the COVID-19 confirmed cases distribution across the states of Nigeria using data gotten from the NCDC site (note: data might not be up to date by the time you are reading this publication).
The Jupyter notebook containing the well commented code for this step can be found in my GitHub Repo.
Below is a screenshot of the final dataframe after integrating the COVID-19 data with the Geojson file using Pandas and Geopandas.

STEP 4: GENERATION OF INTERACTIVE CHOROPLETH MAP WITH FOLIUM

To the final step which is the visualization of the choropleth map with Folium.
We will be using the folium.Choropleth module, so we need to install the Folium package for this using conda install /pip install.
The Jupyter notebook containing the well commented code for this step can be found in my GitHub Repo.
Below is a screenshot of our generated Choropleth interactive map.

PARTING WORDS...

Other forms of map visualization are possible using the Folium package check out the Folium documentation for more info on that.
The generated map can be saved as an .html file which can be embedded to a webpage.
I hope you find this article helpful and insightful, if so kindly leave a star on my GitHub Repo.