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Geopandas Data Visualization Guide

Geopandas extends pandas to enable spatial operations and geographic visualizations. Here's a comprehensive guide to creating maps and spatial visualizations with Geopandas.

Table of Contents

  1. Basic Map Plotting
  2. Choropleth Maps
  3. Point Maps
  4. Heatmaps
  5. Line Maps
  6. Interactive Maps
  7. Customizing Maps

Basic Map Plotting

Simple Boundary Plot

Purpose: Visualize geographic boundaries
Best for: Showing regions/countries/states

import geopandas as gpd

# Load example dataset
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))

# Plot countries
ax = world.plot(figsize=(10, 6))
ax.set_title("World Countries")

Multiple Layers

Purpose: Combine different geographic layers
Best for: Contextual mapping

cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))

ax = world.plot(color='lightgrey', edgecolor='black', figsize=(12, 8))
cities.plot(ax=ax, color='red', markersize=5)
ax.set_title("World Map with Major Cities")

Choropleth Maps

Basic Choropleth

Purpose: Color regions by attribute values
Best for: Thematic mapping of statistics

ax = world.plot(column='pop_est', 
                legend=True,
                figsize=(12, 8),
                cmap='OrRd',
                scheme='quantiles')
ax.set_title("World Population Estimate")

Custom Classification Schemes

Purpose: Control how data is binned
Best for: Highlighting specific value ranges

ax = world.plot(column='gdp_md_est',
                legend=True,
                figsize=(12, 8),
                cmap='Blues',
                scheme='natural_breaks',
                k=5)
ax.set_title("World GDP Estimate (Natural Breaks)")

Point Maps

Scatter Plot on Map

Purpose: Show point data with geographic context
Best for: Event locations, site analysis

ax = world.plot(color='lightgrey', figsize=(12, 8))
cities.plot(ax=ax, 
           column='name', 
           markersize=cities['pop_max']/1000000,
           cmap='viridis',
           legend=True)
ax.set_title("World Cities by Population")

Bubble Map

Purpose: Show magnitude with point size
Best for: Comparing quantitative values at points

ax = world.plot(color='lightgrey', figsize=(12, 8))
cities.plot(ax=ax, 
           markersize=cities['pop_max']/500000,
           color='red',
           alpha=0.5)
ax.set_title("City Population Bubble Map")

Heatmaps

Density Heatmap

Purpose: Show point density
Best for: Hotspot analysis

import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable

fig, ax = plt.subplots(figsize=(12, 8))
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)

world.plot(ax=ax, color='lightgrey')
cities.plot(ax=ax, 
           markersize=1,
           color='darkred',
           alpha=0.3)
ax.set_title("City Density Heatmap")

Line Maps

Route Visualization

Purpose: Show paths/routes/networks
Best for: Transportation analysis

# Example with NYC taxi data
routes = gpd.read_file('taxi_routes.geojson')
nyc = gpd.read_file('nyc_boundary.geojson')

ax = nyc.plot(color='lightgrey', figsize=(12, 8))
routes.plot(ax=ax, linewidth=0.5, alpha=0.3, color='blue')
ax.set_title("Taxi Routes in NYC")

Interactive Maps

Folium Integration

Purpose: Create interactive web maps
Best for: Exploratory analysis

import folium
from folium.plugins import HeatMap

m = folium.Map(location=[20, 0], zoom_start=2)

# Add GeoJSON layer
folium.GeoJson(world).add_to(m)

# Add points
for idx, row in cities.iterrows():
    folium.CircleMarker(
        location=[row.geometry.y, row.geometry.x],
        radius=3,
        color='red',
        fill=True
    ).add_to(m)

m.save('interactive_map.html')

Customizing Maps

Advanced Styling

Purpose: Create publication-quality maps
Best for: Final presentation

fig, ax = plt.subplots(figsize=(15, 10))

# Base map
world.plot(ax=ax, 
           color='#f0f0f0', 
           edgecolor='#999999', 
           linewidth=0.5)

# Highlight specific countries
world[world['continent'] == 'Africa'].plot(
    ax=ax, 
    color='#2ca25f', 
    edgecolor='black'
)

# Add cities
cities.plot(ax=ax, 
            color='red', 
            markersize=5, 
            alpha=0.7)

# Customize
ax.set_title("Africa with Major Cities", fontsize=16)
ax.set_axis_off()
plt.tight_layout()

Adding Map Elements

Purpose: Enhance map readability
Best for: Professional maps

import contextily as ctx

fig, ax = plt.subplots(figsize=(12, 8))

# Plot your geodataframe
nyc.plot(ax=ax, alpha=0.5, edgecolor='k')

# Add basemap
ctx.add_basemap(ax, crs=nyc.crs, source=ctx.providers.Stamen.TonerLite)

# Add scalebar
from mpl_toolkits.axes_grid1.anchored_artists import AnchoredSizeBar
scalebar = AnchoredSizeBar(ax.transData,
                          10000,  # 10km
                          '10 km', 
                          'lower right',
                          pad=0.5,
                          color='black',
                          frameon=False,
                          size_vertical=1)
ax.add_artist(scalebar)

ax.set_title("New York City with Basemap")
ax.set_axis_off()

Key Geopandas Features

  1. CRS Support: Handle different coordinate reference systems
  2. Spatial Joins: Combine data based on geographic relationships
  3. Geometric Operations: Buffers, intersections, unions
  4. Attribute Queries: Filter data using spatial predicates
  5. Integration: Works with matplotlib, folium, plotly, and more

To install required packages: 

pip install geopandas matplotlib folium contextily

Geopandas provides the foundation for geographic data science in Python, combining pandas' data manipulation with geographic operations and visualization capabilities.