Plotting Geospatial Data using GeoPandas

GeoPandas is a powerful open-source Python library that extends the functionality of Pandas to support spatial/geographic operations. It brings the simplicity of pandas to geospatial data and makes it easy to visualize and analyze geographical datasets with minimal code. GeoPandas combines several libraries:

• pandas for data handling
• shapely for geometry operations
• fiona for file access
• matplotlib for plotting
• pyproj for coordinate reference systems

Installation

You can install the required libraries using pip:

pip install geopandas
pip install matplotlib
pip install numpy
pip install pandas

Alternatively, platforms like Google Colab or Jupyter Notebook often have many of these pre-installed or allow for quick installation within cells.

Getting Started

Step 1: Import Required Modules

Step 2: Load Sample GeoSpatial Dataset

GeoPandas provides sample datasets for practice. One of the most commonly used is the world map dataset, which includes country-level geographic and economic information.

Output

Explanation: This loads the built-in world dataset containing country-level geometries and attributes like population, GDP and continent. head() displays the first few records.

Plotting the World Map

GeoPandas makes geospatial plotting simple. Using the built-in world dataset and the .plot() method, we can quickly visualize country boundaries:

Output

Explanation: A simple .plot() call visualizes country boundaries. Adding a title with Matplotlib improves clarity.

Analyze and Filter Dataset

To focus on a specific region, we can filter the dataset. Here, we extract only Asian countries and visualize their GDP estimates (gdp_md_est) using a red color gradient:

Explanation: This filters the dataset to only include Asian countries, then plots them using a red color gradient based on GDP (gdp_md_est). The legend=True adds a scale.

Visualizing World Population Estimates

To visualize population estimates (pop_est), we can use the cmap property to apply color gradients—lighter shades indicate lower values, darker shades indicate higher ones:

Output:

Explanation: This uses the pop_est column to apply a color gradient—automatically mapping population estimates to shades of blue (default).

Enhanced Visualization with Axes and Styling

The default population plot lacks visual clarity. We improve it by:

• Enlarging the figure,
• Plotting a black world map for contrast,
• Overlaying population data with a red color map.

Output:

Explanation: First, a larger figure is created. A base black world map provides contrast. Then, population data is overlaid using a red color map (cmap='Reds') to improve visual clarity.

Add a Color Bar Using Axes Divider

To improve map readability, we add a color bar to show the data scale. Using the facecolor parameter (e.g., light blue) enhances visual contrast. We then apply make_axes_locatable from mpl_toolkits.axes_grid1 to create a side space for the color bar.

Output:

Explanation: This enhances the map with a clear color bar indicating population ranges. make_axes_locatable creates space for it, while a light blue background improves aesthetics. Red shades represent population estimates, with a legend showing intensity levels.