How to plot world population density using GeoPandas

The goal of GeoPandas is to make spatial data processing easier in Python. It provides high-level functions such as the calculation of area or boundary and basic, choropleth, layered, or interactive plots for multiple geometries and shapes. These capabilities are particularly valuable for visualizing population density, a key factor in urban planning, resource allocation, and understanding demographic patterns. For example, such visualizations help identify overpopulated areas needing infrastructure improvements or sparsely populated regions where resources may be underutilized. GeoPandas can read multiple data formats such as JSON or SHP files. It reads spatial data in the form of GeoSeries or GeoDataFrames representing complex polygons, linestrings, and points to plot geographical areas, paths, or locations.

GeoPandas also enables the projection of geographical data in different coordinate systems (CRS), which is an integral part of spatial data processing. To give you a flavor of plots in GeoPandas, here is a sample boundary plot created by boundary.plot() method of GeoPandas.

Creating a population density map

To create a world population density map, we will use a GeoJSON file containing global population data as a dataset. The dataset contains various attributes for each region, including:

• NAME: The name of the country or region.

• ISO_3_CODE and ISO_2_CODE: ISO country codes for identifying regions.

• AREA: The total area of the region.

• NAME_1: Subregion or additional name information.

• POP2005: Population estimates from the year 2005.

• REGION: The larger region to which the country belongs.

• GMI_CNTRY: Country classification based on Gross National Income (GNI).

• NAME_12: Another naming variant.

• geometry: Geospatial data representing the region’s boundaries.

It is pertinent to note that polygons can consist of hundreds of points and multiple polygons. A snapshot of geometry column is shown below.

Since the DataFrame already contains the population attribute, the area is to be calculated to find the population density of all the countries. To calculate the accurate area of countries, the projection of geometry is to be converted into EPSG:6933EPSG:6933 is a widely used standard for mapping the world. It ensures that country sizes and areas are represented accurately, making it ideal for comparing global data like population density. This projection is commonly chosen for global datasets because it maintains consistent area measurements across the globe. which is an equal area projection to preserve the area of the region on the earth as well as on the flat map.
The following code calculates and plots population density using GeoPandas.

Code explanation

Let’s understand the code above:

• Lines 1–3: Import geopandas, matplotlib.

• Lines 6–7: Read the data using read_file() method of GeoPandas and finally convert the POP2005 column having world population in the year 2005 to float data type.

• Line 8: Use to_crs() and area() methods of GeoPandas to calculate areas of countries after projecting the CRS. It also converts the area from m² to km².

• Lines 9–10: Calculate density by dividing the population by area, it also shows the first few rows of the DataFrame.

• Line 13: Add title to the graph using plt.title().

• Lines 14–17: Create the density plot using plot() method of GeoPandas. It uses the following arguments:

  • cmap represents a color map.

  • linewidth sets the width of boundaries between countries.

  • scheme divides the density attribute into different intervals.

  • legend is set to True to include the legend in the figure.

  • figsize() is needed to define the size of the figure.

  • legend_kwds is used for defining the location and size of the legend.

Note: You can practice the above code in the code playground below. Press the "Run" button and wait for the output tab to show the Jupyter Notebook. Alternatively, you can click the link beside the "Run" button to open the respective Jupyter Notebook in a new tab.