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What is Basemap?
The Matplotlib Basemap toolkit is an extension to Matplotlib that provides functionality for creating maps and visualizations involving geographical data. It allows users to plot data on various map projections, draw coastlines, countries and other map features . These are used to handle geographic coordinates seamlessly within Matplotlib.
The below are the key features of Matplotlib Basemap.
Map Projections
Basemap supports various map projections by allowing users to visualize data in different coordinate systems like cylindrical, conic or azimuthal projections. Examples are Mercator, Lambert Conformal Conic and Orthographic projections.
Plotting Geographic Data
Basemap allows the plotting of geographical data such as points, lines, or polygons over maps. Users can overlay datasets onto maps and visualize geographic relationships.
Map Elements
Basemap provides functions to add map elements like coastlines, countries, states, rivers and political boundaries by enhancing the visual context of the maps.
Coordinate Transformation
It facilitates seamless conversion between different coordinate systems such as latitude and longitude to map projection coordinates and vice versa.
Installing Basemap
If we want to work with Basemap of matplotlib library we have to install it in our working environment. The below is the code.
Example
pip install basemap
Note − When we are working with Jupyter notebook then we have to install basemap through the anaconda command terminal.
Output
Basic Workflow with Basemap
The below is the basic workflow of the Basemap of the Matplotlib library. Let’s see each of them in detail for better understanding.
Create a Basemap Instance
Instantiate a Basemap object by specifying the map projection, bounding coordinates and other parameters. To create a Basemap instance using the Basemap toolkit in Matplotlib we can define the map projection and specify the desired map boundaries.
Example
In this example we are generating a basic map with coastlines and country boundaries using the specified Mercator projection and the provided bounding coordinates. We can further customize the map by adding features, plotting data points or using different projections and resolutions based on our requirements.
from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt # Create a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=''merc'', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=''c'') # Draw coastlines and countries map.drawcoastlines() map.drawcountries() # Show the map plt.show()
Output
Plot Data on the Map
In this we use the Basemap methods to draw map features, plot data points or visualize geographical datasets.
Example
In this example we are generating random latitude and longitude points and then uses the map() function to project these coordinates onto the Basemap instance. The scatter() method is then used to plot these projected points on the map as red markers.
from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # Create a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=''merc'', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=''c'') # Draw coastlines and countries map.drawcoastlines() map.drawcountries() # Generate random data (longitude, latitude) for plotting num_points = 100 lons = np.random.uniform(low=-180.0, high=180.0, size=num_points) lats = np.random.uniform(low=-80.0, high=80.0, size=num_points) # Plot the data points on the map x, y = map(lons, lats) # Project the latitudes and longitudes to map coordinates map.scatter(x, y, marker=''o'', color=''red'', zorder=10) # Plotting the data points # Show the map with plotted data plt.title(''Data Points on Map'') plt.show()
Output
Display the Map
We can use Matplotlib library to show() function to display the final map.
Example
Here”s an example demonstrating the usage of Basemap to create a map and plot data points.
from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np # Creating a Basemap instance with a specific projection and bounding coordinates map = Basemap( projection=''merc'', llcrnrlat=-80, urcrnrlat=80, llcrnrlon=-180, urcrnrlon=180, resolution=''c'') # Drawing coastlines, countries, and states map.drawcoastlines() map.drawcountries() map.drawstates() # Generating random data for plotting num_points = 100 lons = np.random.uniform(low=-180.0, high=180.0, size=num_points) lats = np.random.uniform(low=-80.0, high=80.0, size=num_points) data_values = np.random.rand(num_points) * 100 # Random values for data # Plotting data points on the map x, y = map(lons, lats) # Projecting latitudes and longitudes to map coordinates map.scatter(x, y, c=data_values, cmap=''viridis'', marker=''o'', alpha=0.7) # Adding a colorbar to represent the data values plt.colorbar(label=''Data Values'') # Display the map with plotted data plt.title(''Basemap Example with Data Points'') plt.show()
Output
Applications of Matplotlib Basemap
Geospatial Analysis − Analyzing and visualizing geographical data such as climate patterns, population distributions or seismic activities.
Cartography − Creating custom maps for publications, presentations or research purposes.
Data Visualization − Integrating geographical data with other datasets for exploratory data analysis.
Note
Matplotlib Basemap is being phased out in favor of newer geospatial libraries like Cartopy which offer more extensive functionality and better integration with Matplotlib. Cartopy builds on the concepts of Basemap but provides a more modern, flexible and actively developed interface for handling geospatial data within Matplotlib.
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