Introduction to Choropleth Maps
A choropleth map displays various data in different regions to visualize geographical data on a region-by-region basis. Due to significant progress in geospatial analysis, the market now offers various map plotting techniques. In this blog, we will study choropleth maps and plot them using the GeoPandas Python library.
Geospatial analysis#
Geospatial analysis examines, interprets, and manipulates geographic data and information associated with specific locations on the Earth’s surface. It consists of various techniques and methodologies for understanding spatial patterns, relationships, and trends within geographic data.
Geopandas library#
GeoPandas is an open-source Python library that extends the capabilities of pandas, a widely used data manipulation library, to handle geospatial data more efficiently. It provides a user-friendly and powerful interface for working with geospatial datasets.
GeoPandas helps read, write, visualize, and analyze geographic data in various formats, such as shapefiles, GeoJSON, Geospatial Data Abstraction Library (GDAL) formats, and more. GeoPandas allows seamless integration with data analysis workflows, making combining geospatial data with non-spatial data and performing complex spatial operations easier.
We can easily use this library with just one command:
pip install geopandas
Let’s learn about choropleth maps, their types, and their advantages.
Choropleth maps#
Choropleth originates from combining two Greek words: choros, which signifies region, and plethos, which means multitude. This type of map displays various data in different regions to visualize geographical data on a region-by-region basis. These maps represent data using different colors or shading patterns to indicate the variation in a specific variable across geographic areas, such as countries, states, provinces, counties, or other administrative divisions.
Let’s create our first choropleth map in Python:
import geopandas as gpdimport matplotlib.pyplot as pltworld_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))fig, ax = plt.subplots(figsize=(10, 6))variable = "pop_est"cmap = "viridis"world_map.plot(column=variable, cmap=cmap, linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)ax.set_ylabel('Latitude')ax.set_xlabel('Longitude')plt.show()
Line 1: We import the
geopandaslibrary.Line 2: We import the
pyplotmodule from thematplotliblibrary, which is used for creating plots and visualizations.Line 4: We retrieve the file path for the built-in Natural Earth dataset called
naturalearth_lowresingeopandasto the variableworld_mapwhich contains low-resolution geometries and attributes of countries. Thegpd.read_filemethod reads the data from the file specified in the argument (in this case, the Natural Earth dataset). It returns a GeoDataFrame, a specialized data structure in GeoPandas for handling geospatial data.Line 6: We define the variable to be displayed on the choropleth map. In this case, it is set to
"pop_est", representing the population estimate attribute in the dataset.Line 7: We define the colormap to be used for the choropleth map. The term
"viridis"represents the perceptually uniform sequential colormap.Line 8: We plot the choropleth map using the GeoDataFrame
world_map.column=variable: Specifies the column in the GeoDataFrame that contains the data to be visualized. Here, it is set to the value of thevariable, which is"pop_est".cmap=cmap: Sets the colormap for the choropleth map.linewidth=0.8: Sets the width of the boundary lines between the polygons on the map.ax=ax: Specifies the axes on which to plot the map. In this case, it uses theaxcreated earlier in Line 5 withplt.subplots.edgecolor='0.8': Sets the color of the boundary lines between the polygons.
Here is the result of the aforementioned code:
Categorical choropleth map#
To create a categorical choropleth map with legends, we’ll use a modified version of the world map data that contains categorical data, such as regions or categories for different countries. For this example, we’ll use the Natural Earth dataset again, but create a new column called Category to represent the categories for each country.
Here’s the code to create a categorical choropleth map with legends:
import geopandas as gpdimport matplotlib.pyplot as pltworld_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))categories = {'United States': 'North America','Canada': 'North America','Brazil': 'South America','China': 'Asia','India': 'Asia','Australia': 'Oceania','France': 'Europe','South Africa': 'Africa',}world_map['Category'] = world_map['name'].map(categories)fig, ax = plt.subplots(figsize=(20, 10))variable = 'Category'cmap = 'Set1'world_map.plot(column=variable, cmap=cmap, linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)ax.set_ylabel('Latitude')ax.set_xlabel('Longitude')plt.show()
Lines 5–14: We define a dictionary named
categories. It maps country names to their respective categories or regions.Line 16: We add a new column
Categoryto theworld_mapGeoDataFrame. It maps the values in thenamecolumn (country names) to the categories defined in thecategoriesdictionary and assigns the corresponding category to each country.
The output of this code will be the following map:
Remember, these maps are versatile tools that can uncover insights with just a glance and can be employed in a wide range of real-world scenarios.
Let's explore an application scenario for choropleth maps.
Case Study: Visualizing the global COVID-19 vaccination rate#
In response to the COVID-19 pandemic, governments worldwide launched vaccination campaigns to curb the spread of the virus. To assess the progress of these campaigns, we are required to generate a choropleth map that visualizes the hypothetical COVID-19 vaccination rates by country. Let’s have a look at the code:
import geopandas as gpdimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltworld = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))# Create dummy vaccination data for all countriesnp.random.seed(42) # For reproducibilityvaccination_data = {'Country': world['name'],'Vaccination Rate (%)': np.random.randint(0, 100, len(world)) # Generate random values}vaccination_df = pd.DataFrame(vaccination_data)merged_data = world.merge(vaccination_df, left_on='name', right_on='Country')fig, ax = plt.subplots(figsize=(12, 8))variable = 'Vaccination Rate (%)'cmap = 'YlGnBu'merged_data.plot(column=variable, cmap=cmap, linewidth=0.8, ax=ax, edgecolor='0.8', legend=True)ax.set_ylabel('Latitude')ax.set_xlabel('Longitude')plt.show()
Line 10: We set the random seed for reproducibility, ensuring that random numbers generated are consistent across runs.
Lines 11–14: A dictionary is created containing two keys:
CountryandVaccination Rate (%). TheCountrykey gets values from thenamecolumn of theworldDataFrame. TheVaccination Rate (%)and key is populated with random integer values (between 0 and 100) generated usingnp.random.randint()for the same length as theworldDataFrame.
The output of this code will be the following map:
Conclusion #
This blog has introduced geospatial analysis, GeoPandas library, and the exciting world of choropleth maps. Our exploration dived deeper into the details of choropleth maps and explored the diverse scenarios in which they apply.
If you want to learn more about choropleth maps, look no further! Check out the exciting new courses available on the Educative platform:
Free Resources
