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Neil Brown
Neil Brown

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How I Mapped the Entire City of Chicago Using Python

#python

Ever wondered what it takes to visualize a sprawling city like Chicago on your screen — using nothing but code? As someone passionate about data, maps, and automation, I set out to map Chicago using Python. This post breaks down how I did it, tools I used, and how you can replicate this for any city.

Whether you're a Python developer, a data analyst, or just a curious explorer — this is for you.

Tools & Libraries I Used

To keep things open-source and efficient, I used the following Python libraries:

  • geopandas – for spatial data manipulation
  • matplotlib – for visualizing the map
  • contextily – for adding background tiles
  • osmnx – to download and plot street networks from OpenStreetMap
  • shapely – for geometric operations

You can install all dependencies using pip:

pip install geopandas matplotlib contextily osmnx shapely
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Step-by-Step: Mapping Chicago

1. Get the Boundary of Chicago
Using OSMnx, I first downloaded the polygon boundary of Chicago.

import osmnx as ox

# Fetch the city boundary
city = ox.geocode_to_gdf("Chicago, Illinois, USA")
city.plot()
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This gave me an accurate geographic outline of the city — ready to be layered with roads, buildings, and other data.

2. Download Chicago’s Street Network
I wanted to see the complete street grid, so I pulled data for all drivable roads.

G = ox.graph_from_place("Chicago, Illinois, USA", network_type="drive")
ox.plot_graph(ox.project_graph(G))
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Want bike paths, footways, or public transport? Just change network_type.

3. Convert to GeoDataFrame
To perform analysis or plot on layers, I converted the network to a GeoDataFrame.

gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)
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Now, I had each road as a line geometry I could filter, color, or export.

4. Add a Basemap for Context
To make it visually appealing, I added a tile basemap using contextily.

import contextily as ctx
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(12,12))
gdf_edges.plot(ax=ax, linewidth=0.5, color="black")
ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron, crs=gdf_edges.crs.to_string())
plt.title("Street Network of Chicago", fontsize=15)
plt.axis('off')
plt.show()
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This resulted in a beautiful, modern-looking map with the road layout of Chicago overlaid on real map tiles.

**Bonus: **Mapping Points of Interest
I went a step further and fetched places like hospitals, schools, and parks:

tags = {'amenity': ['school', 'hospital', 'library']}
pois = ox.features_from_place("Chicago, Illinois, USA", tags)
pois.plot(figsize=(10,10), color='green', markersize=5)
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This allowed me to visualize where critical services are concentrated or missing.

Exporting as an Image or GeoJSON
You can save your map as a high-res PNG or even a GeoJSON for use in web maps:

gdf_edges.to_file("chicago_streets.geojson", driver="GeoJSON")
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What I Learned

Mapping Chicago taught me:

  • OpenStreetMap + Python = Unlimited Potential
  • Python makes geospatial data accessible even without a GIS degree
  • Visualizing urban data helps uncover patterns you can’t see on spreadsheets

Want to Map Your City?

You can replace "Chicago, Illinois, USA" with any city name. Try:

"New York City, New York, USA"
"Los Angeles, California, USA"
"Delhi, India"
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This method is universal, free, and incredibly fun.

Final Thoughts

Mapping the whole of Chicago using Python wasn’t just a technical challenge — it was an eye-opener into how accessible geographic data is today. With just a few lines of Python, you can map, analyze, and visualize entire cities.

I plan to explore more — traffic flow, zoning data, green space distribution — and if you're into urban data, you should too.

Feel free to fork the code, modify it, and build your own custom city maps! Looking for a Top SEO company in chicago for small scale business? Check out Nubiz Solutions.

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