Learn Basic Spatial Operations | Introduction to Geospatial Data

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Working with geospatial data often begins with the ability to filter, select, and visualize features based on their attributes or spatial properties. Using the geopandas library, you can perform these basic spatial operations efficiently. Filtering allows you to focus on features of interest within a larger dataset, while plotting helps you interpret the spatial patterns and relationships visually.

To filter spatial data, you commonly use boolean indexing and the .loc accessor in geopandas. This enables you to select rows that meet specific criteria, such as all features with a certain attribute value.


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import geopandas as gpd
import matplotlib.pyplot as plt

# 1. Load the dataset using a direct URL to the public Natural Earth GeoJSON
world_url = "https://raw.githubusercontent.com/nvkelso/natural-earth-vector/master/geojson/ne_110m_admin_0_countries.geojson"
world = gpd.read_file(world_url)

# 2. Filter: Select only countries in South America
south_america = world[world['CONTINENT'] == 'South America']

# 3. Plotting
ax = world.plot(color='lightgray', edgecolor='white', figsize=(10, 6))

# Overlay South American countries in green
south_america.plot(ax=ax, color='forestgreen', edgecolor='black')

# Customize the map
plt.title("South America Highlighted on World Map", fontsize=14)
plt.axis('off')  # Hide the lat/long grid lines for a cleaner look

# Show the plot
plt.show()

After filtering your data, visualizing it on a map is an essential step in geospatial analysis. geopandas integrates seamlessly with matplotlib, allowing you to create rich, informative maps. You can customize the colors of features based on attribute values and add legends to make your plots more meaningful.


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import matplotlib.pyplot as plt

# Plot all world countries in light gray
ax = world.plot(color='lightgray', edgecolor='white', figsize=(10, 6))

# Overlay South American countries in green with a legend
south_america.plot(ax=ax, color='forestgreen', edgecolor='black', label='South America')

# Add a title and legend
plt.title("Countries of South America")
plt.legend()
plt.show()

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Section 1. Chapter 4