Mapping the Whole of New York Using Python

When it comes to working with data, few projects feel as satisfying — and as ambitious — as mapping an entire city. And if there’s one city that deserves to be mapped in all its complexity, it’s New York.
From its iconic boroughs to its endless streets, New York offers a perfect dataset playground for Python enthusiasts, urban planners, or anyone curious about geospatial analysis.

In this blog, I’ll walk you through how to map the whole of New York using Python — step by step. We’ll talk about the tools, the data, and some cool tricks to visualize and analyze NYC’s sprawling geography.

Why Map New York with Python?

Python has emerged as the go-to language for data science and geospatial work.
Thanks to libraries like GeoPandas, Folium, Shapely, and Matplotlib, mapping cities is now something you can do on your laptop — no fancy GIS software needed.

Mapping New York can help you:

Visualize population density by borough or neighborhood.

Understand urban infrastructure — roads, subway lines, parks.

Build interactive maps for real estate, tourism, or delivery apps.

Analyze patterns like crime hotspots, noise complaints, or Airbnb listings.

What You’ll Need

Before we start, here’s a quick checklist:

• Python 3.x installed on your machine.
• A basic understanding of Pandas and Matplotlib.
• Libraries: geopandas, folium, matplotlib, shapely.
• Open-source geospatial data (shapefiles, GeoJSON).

You can install the needed packages using pip:
pip install geopandas folium matplotlib shapely


Finding New York City Data

Good data is key. For New York, you have many sources:

• NYC Open Data (https://opendata.cityofnewyork.us/)
• US Census Bureau — TIGER/Line shapefiles.
• OpenStreetMap (OSM) — with tools like osmnx for easy extraction.
• NYC Planning Open Data Portal

For this example, we’ll use a shapefile of NYC borough boundaries.

Let’s Get Mapping

Load the Data
`import geopandas as gpd

Load NYC borough boundaries shapefile

nyc = gpd.read_file('https://data.beta.nyc//dataset/borough-boundaries.geojson')

print(nyc.head())`

Basic Plot
Let’s see the map:
nyc.plot()
Boom — you just mapped NYC! This is the simplest map you can build.

Add Some Style
Let’s add colors and labels:

`
import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 10))
nyc.plot(ax=ax, color='lightblue', edgecolor='black')

Add labels

for idx, row in nyc.iterrows():
plt.annotate(s=row['boro_name'], xy=(row['geometry'].centroid.x,
row['geometry'].centroid.y),
horizontalalignment='center',
fontsize=12, fontweight='bold')

plt.title("New York City Boroughs")
plt.show()
`
Make it Interactive with Folium
Static maps are cool, but interactive maps are cooler.

`import folium

Get NYC centroid

nyc_center = [40.7128, -74.0060]

Create a Folium map

m = folium.Map(location=nyc_center, zoom_start=10)

Add boroughs

folium.GeoJson(nyc).add_to(m)

m.save('nyc_map.html')
Open the nyc_map.html in your browser — you’ve got an interactive map!
`
Go Deeper: Add Layers
Want to add subway lines, crime data, or public parks?
Load more shapefiles or GeoJSONs and add them to the same map.

For example, using osmnx:

`import osmnx as ox

Download street network

G = ox.graph_from_place('New York City, New York, USA', network_type='drive')

Plot

ox.plot_graph(ox.project_graph(G))`

What’s Next?

Now that you know the basics, you can:

• Visualize heatmaps of taxi trips.
• Plot school zones or election districts.
• Combine demographic data with spatial boundaries.
• Build beautiful, shareable maps for clients or research.

The possibilities are endless once you have the data and Python on your side.

Final Thoughts

Mapping New York — or any city — with Python is more than a fun project.
It’s a gateway into the world of geospatial data science. Whether you’re building a portfolio, a business app, or an academic project, mastering Python mapping libraries gives you a big advantage.

If you’d like, I can share a complete Jupyter Notebook version of this tutorial — just drop me a comment!

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