GeoPandas is a Python library that extends the capabilities of pandas to handle geospatial data. It allows us to easily work with geographic data structures and create informative visualizations. In this tutorial, we’ll explore how to use GeoPandas to visualize the boroughs of New York City.
One of the great features of GeoPandas is its ability to create visualizations directly, without needing to use Matplotlib explicitly. While GeoPandas uses Matplotlib under the hood, it provides a simplified interface for creating maps and spatial visualizations. This means we can create all the visualizations in this tutorial using methods that are purely available within GeoPandas, making our code more concise and easier to understand.
Let’s start by importing the necessary libraries and loading our dataset:
import geopandas as gpd
import geodatasets
# Get the path to the dataset
path = geodatasets.get_path('ny.bb')
# Load the NYC boroughs dataset
nyc = gpd.read_file(path)
Here, we’re using a built-in dataset from the geodatasets library, which contains the boundaries of New York City’s boroughs. Let’s take a look at our data:
nyc
This GeoDataFrame contains information about each borough, including its name, area, and geometric boundaries.
1. Basic Plot
Let’s create our first visualization – a simple plot of the NYC boroughs:
# Basic plot nyc.plot();
This basic plot displays the geographical outlines of New York City’s boroughs, each shaded in a uniform blue color. By default, when we don’t specify any color options, GeoPandas uses a single color (blue in this case) to fill all polygons. This provides a simple, clear visualization of the geographic boundaries.
2. Customizing Colors
We can also opt to customize the colors of our plot for a more visually appealing map:
# Plot with customized colors and edge colors nyc.plot(color='lightblue', edgecolor='black');
In this version, we’ve set all boroughs to a light blue color with black borders, creating a cleaner, more unified look.
3. Choropleth Map
A choropleth map is a type of thematic map where areas are shaded or patterned in proportion to the measurement of a statistical variable that represents an aggregate summary of a geographic characteristic within those areas. Let’s create a choropleth map based on the area of each borough:
# Plot a specific column (example with area) nyc.plot(column='Shape_Area', cmap='viridis', legend=True);
This visual is a choropleth map of New York City’s boroughs, where each borough is colored differently to represent their varying sizes. The ‘viridis’ colormap is used, with lighter colors representing larger areas.
4. Categorical Plot
Finally, let’s create a categorical plot that assigns a unique color to each borough:
# Categorical plot by borough name with legend in top left
nyc.plot(column='BoroName', legend=True,
legend_kwds={'loc': 'upper left'},
cmap='Set3', edgecolor='black', linewidth=0.5);
In this plot, each borough is assigned a unique color based on its name. We’ve customized the plot further by:
- Placing the legend in the upper left corner to avoid overlapping with the map
- Using the ‘Set3’ colormap for distinct, visually pleasing colors
- Adding black borders to clearly delineate the boroughs
Conclusion
In this tutorial, we’ve explored how to use GeoPandas to create various visualizations of geospatial data. We started with a basic plot and progressively added more features and customizations. GeoPandas makes it easy to create informative and visually appealing maps with just a few lines of code.
These techniques can be applied to any geospatial dataset, allowing you to create maps of cities, countries, or any other geographic areas. As you become more comfortable with GeoPandas, you can explore more advanced features like spatial operations, custom projections, and interactive maps.
Remember, effective data visualization is key to understanding and communicating geospatial information. With GeoPandas, you have a powerful tool at your disposal to explore and present geographic data in Python.
