Crafting Multiple Legends for Geopandas Plots

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What will you learn?

In this tutorial, you will delve into the art of creating multiple legends when plotting several layers in geopandas. You will learn how to effectively manage and display distinct legends for various layers within a single geospatial plot. By the end, you’ll be equipped with the skills to enhance your geospatial visualizations with customized legends.

Introduction to Problem and Solution

Managing multiple legends while plotting with geopandas can initially appear challenging due to each layer potentially representing different data types or categories. The solution lies in understanding how matplotlib, the underlying library used by geopandas for plotting, handles legends and layers. This tutorial will guide you through creating individual legend objects for each layer and then merging them into a unified plot that elegantly showcases the complexity of your geospatial data.

To tackle this challenge effectively, we will: – Ensure each layer has its own legend by specifying labels during the plot operation. – Collect all legend handles and labels. – Merge these individual legends into a coherent singular legend using matplotlib’s functionalities.

Let’s dive into the code snippet below to see how this is achieved:

Code

import geopandas as gpd
import matplotlib.pyplot as plt

# Sample GeoDataFrames
gdf1 = gpd.read_file('path_to_your_data_1')
gdf2 = gpd.read_file('path_to_your_data_2')

# Plotting the first layer
fig, ax = plt.subplots()
gdf1.plot(ax=ax, color='blue', label='Layer 1')

# Plotting the second layer on top of the first one
gdf2.plot(ax=ax, color='red', label='Layer 2')

# Collecting handles and labels for each layer's legend
handles, labels = ax.get_legend_handles_labels()

# Displaying a combined legend 
ax.legend(handles=handles[:], labels=labels[:])

plt.show()

# Copyright PHD

Explanation

The code snippet above demonstrates combining different geospatial datasets (gdf1 and gdf2) into a single plot with unique legend entries for each. By plotting these GeoDataFrames on a shared axes object (ax), we ensure they occupy the same space on our figure. Labels assigned during plotting are crucial for generating a unified legend later on. Retrieving handles and labels using ax.get_legend_handles_labels() enables us to construct a composite legend that encapsulates information from all plotted layers. Finally, invoking ax.legend() with collected handles and labels produces a cohesive summary of our visualization elements.

    How do I change colors or markers for my plots?

    You can modify colors by using the color= argument within .plot(), or alter symbols representing data points using marker=.

    Can I add more than two layers?

    Certainly! Simply add another .plot() call onto your axis object (ax) for each additional layer while assigning it a new label.

    What if I want different styles for my legends?

    Matplotlib offers customization options like fontsize or location through parameters in ax.legend(). Explore parameters such as fontsize= or loc= based on your requirements.

    How do I save my plot instead of displaying it?

    To save your figure as an image file instead of displaying it, use plt.savefig(‘filename.png’) before calling plt.show().

    Can this method be applied beyond GeoPandas plots?

    Yes! This technique is rooted in Matplotlib and extends to various types of layered plots beyond just GeoPandas figures.

    Is there a way to automate repetitive plotting tasks?

    Consider defining functions that encapsulate your plotting logic based on dataset inputs to streamline recurring plotting patterns.

    Conclusion

    Mastering multi-layered plots with customized legends enhances your ability to convey intricate geographic dataset details clearly. Leveraging Python libraries such as GeoPandas alongside Matplotlib empowers you to perform insightful spatial analyses supported by visually compelling representations.

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