How to Use Contextily for High-Quality Geospatial Mapping in Python

Geospatial data visualization is an integral part of the current data science ecosystem, which consists of GIS (Geographic Information Systems), urban planning, environmental studies, transportation modeling, and location intelligence. While the spatial analytic capabilities of several of the available Python libraries (e.g., GeoPandas and Matplotlib) allow for relatively easy access to spatial analysis, producing professional-quality maps often requires the integration of high-resolution basemaps.

This is where Contextily comes in.

Contextily allows Python developers and GIS professionals to use web-based basemaps from web services like OpenStreetMap, CartoDB, Stamen, Esri, and other XYZ tile-based services directly within their Matplotlib and GeoPandas workflows. In particular, by retrieving and aligning tiles according to their projected coordinates, Contextily allows for an overall enhancement of geospatial output visual appeal and ability to be interpreted.

Contextily for High-Quality Geospatial Mapping in Python

What Is Contextily?

Contextily is a Python library developed to provide web map tiles (as basemaps) for geospatial visualizations.

Seamless integration with:

• GeoPandas

• Matplotlib

• Rasterio

• Shapely

• PyProj

In using it, you are able to retrieve map tiles from various online datasets and place them in the correct position with respect to your spatial data, giving you the means to produce high-quality maps with little coding effort.

Key functionality:

• High-resolution basemap

• Automatic download of tiles

• Support for per-tile CRS transformation

• Support for multiple tile providers

• Offline storage of map tiles

• Integration with GeoPandas workflow

• Support for custom XYZ tiles

Why Use Contextily?

Without Contextily, GeoPandas maps often appear as plain vector plots with little geographic context.

For example:

gdf.plot()

This generates a basic geometry visualization but lacks roads, landmarks, terrain, and other reference features.

With Contextily:

import contextily as ctx

ax = gdf.plot(figsize=(10, 10))
ctx.add_basemap(ax)

The result is a professional map enriched with real-world geographic context.

Benefits include:

• Enhanced spatial interpretation

• Better stakeholder communication

• Improved map aesthetics

• Publication-ready visualizations

• Reduced GIS software dependency

https://www.youtube.com/watch?v=otQLbBE6Ouc

How to Use Contextily for High-Quality Geospatial Mapping in Python

Installing Contextily

Install Contextily using pip:

pip install contextily

For Conda users:

conda install -c conda-forge contextily

Install supporting geospatial libraries:

pip install geopandas rasterio pyproj shapely

Verify installation:

import contextily as ctx

print(ctx.__version__)

Understanding Coordinate Reference Systems (CRS)

One of the most important concepts when working with Contextily is coordinate reference systems.

Most online basemaps use:

EPSG:3857

Also known as:

• Web Mercator

• Pseudo-Mercator

• Spherical Mercator

GeoPandas datasets may use:

EPSG:4326

Which stores:

• Latitude

• Longitude

Before adding a basemap, convert your GeoDataFrame:

gdf = gdf.to_crs(epsg=3857)

Check CRS:

print(gdf.crs)

Expected output:

EPSG:3857

Failure to align CRS values is the most common cause of basemap rendering issues.

Creating Your First Contextily Map

Load Geospatial Data

import geopandas as gpd

gdf = gpd.read_file("cities.geojson")

Convert CRS:

gdf = gdf.to_crs(epsg=3857)

Plot data:

import matplotlib.pyplot as plt

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

gdf.plot(
    ax=ax,
    color="red",
    markersize=50
)

Add basemap:

import contextily as ctx

ctx.add_basemap(ax)

Display map:

plt.show()

Using Different Basemap Providers

Contextily supports multiple tile providers through the xyzservices ecosystem.

OpenStreetMap

ctx.add_basemap(
    ax,
    source=ctx.providers.OpenStreetMap.Mapnik
)

Best for:

• General mapping

• Road networks

• Urban visualization

CartoDB Positron

ctx.add_basemap(
    ax,
    source=ctx.providers.CartoDB.Positron
)

Best for:

• Data dashboards

• Analytical maps

• Minimalist cartography

CartoDB Dark Matter

ctx.add_basemap(
    ax,
    source=ctx.providers.CartoDB.DarkMatter
)

Best for:

• Dark-themed dashboards

• Interactive visualizations

• Data overlays

Adding High-Resolution Basemaps

For publication-quality outputs:

ctx.add_basemap(
    ax,
    source=ctx.providers.CartoDB.Positron,
    zoom=15
)

Export at high DPI:

plt.savefig(
    "map.png",
    dpi=300,
    bbox_inches="tight"
)

For scientific journals:

dpi=600

is often recommended.

Custom Tile Servers

Contextily supports custom XYZ services.

Example:

ctx.add_basemap(
    ax,
    source="https://tile.openstreetmap.org/{z}/{x}/{y}.png"
)

This enables integration with:

• Internal GIS servers

• Enterprise map services

• Specialized cartographic layers

Working with GeoPandas and Contextily

A common workflow combines:

• GeoPandas

• Contextily

• Matplotlib

Example:

fig, ax = plt.subplots(
    figsize=(14, 10)
)

gdf.plot(
    ax=ax,
    column="population",
    cmap="viridis",
    legend=True
)

ctx.add_basemap(
    ax,
    source=ctx.providers.CartoDB.Positron
)

plt.show()

This creates a thematic choropleth map with geographic context.

Optimizing Performance

Large datasets can impact rendering performance.

Reduce Geometry Complexity

gdf["geometry"] = gdf.geometry.simplify(
    tolerance=10
)

Limit Map Extent

ax.set_xlim(
    xmin,
    xmax
)

ax.set_ylim(
    ymin,
    ymax
)

Contextily is a key component of the Python geospatial ecosystem for building visually attractive, high-quality maps, and forms an integral part of your GeoPandas and Matplotlib workflows for embedding third-party web-based basemaps into those workflows. As such, it allows users such as data scientists, GIS analysts, researchers, and engineers to enhance their basic spatial plots with professional-looking cartographic products.

Whether you are creating dashboards for urban analytics, monitoring systems for the environment, transportation models, location intelligence platforms, or visualizations for scientific purposes, you will be able to use Contextily to add a geographic context to your geospatial visualizations in an efficient and easily scalable manner. If you also have a good understanding of CRS management and can use the optimised zoom levels and high-quality exported files that Contextily supports, then you can produce enterprise-class geospatial visualisations starting with basic vector-based visualisations and transforming them into mapping solutions that can be used for reports, presentations, publications, and production applications.

To learn more about Contextily and its geospatial capabilities, click here.

For more information or any questions regarding Contextily, please don't hesitate to contact us at

Email: info@geowgs84.com

USA (HQ): (720) 702–4849

GeoWGS84AI

(A GeoWGS84 Corp Company)

https://www.geowgs84.ai

https://www.geowgs84.com/services/deep-learning-with-geospatial-data

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