Visualizing NYC Taxi Trends with Kepler.gl

In my last post, I walked through how to clean and prepare NYC taxi data with Python — merging multiple years of trip data, resolving zone IDs into boroughs, and engineering useful time features like hour, day_of_week, and timestamp. This time, I’m taking that cleaned dataset (kepler_zone_heatmap_detailed.csv) and building a visual story with it using Kepler.gl.

Here’s how I set it up.

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1. What’s in the Data?

This dataset includes hourly pickup counts grouped by NYC taxi zones, already joined with centroid coordinates. Each row represents a zone snapshot, and columns include:

• Latitude, Longitude: Coordinates for the zone center

• trip_count: Number of pickups at that location and time

• hour, day_of_week, day, day_of_year: Temporal granularity for filtering

  • day stands for day of month (e.g. comparing traffic on December 25th compared to December 1st)

• timestamp: Rounded datetime used for animation/slider

• Zone, Borough: For labeling or aggregation later

This isn’t raw GPS data — it’s zone-level aggregation, which makes visualization both faster and more interpretable.

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2. Uploading to Kepler.gl

• Go to kepler.gl/demo and browse for or click and drag the kepler_zone_heatmap_detailed.csv into the pop-up dialogue

• Upload kepler_zone_heatmap_detailed.csv

• In my experience, Kepler.gl does not auto-detect Latitude and Longitude. You’ll have to manually confirm or set those in the layer config.

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3. Creating the Heatmap (Point Layer Style)

While Kepler offers multiple layer types, I found better clarity using a Point Layer instead of a Heatmap. Here's how I configured it:

• Layer Type: Point

• Fill Color: Set based on trip_count

• Color Gradient: I changed the scale from white to red — this gave better contrast for spotting high-demand zones

• Radius: Adjust for visibility based on zoom; I used 10 — which is the default

The result makes it much easier to distinguish heavily trafficked areas like Midtown Manhattan or JFK Airport during rush hour.

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4. Enabling Time Playback

To get temporal playback:

• Go to the Filters tab

• Add a filter on timestamp

• Click on the "time playback" button to enable animation

• You can also add filters for day_of_week or hour to explore weekday vs weekend patterns

This let me visualize how demand shifts over time — morning rush in Midtown, late-night clusters in nightlife zones, etc.

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5. Getting a Sharper Export

To export a high-quality map snapshot:

1. Click the three-dot menu > Export Image

2. Choose resolution (I used 2x for clarity) and aspect ratio (16:9 works great for blog banners)

If that’s not to your liking, you could always take a screenshot. I prefer to take screenshots (which the pictures above are screenshots) since you can zoom into the map.

The issue with exporting a snapshot is the fact that it is harder to tell which points has changed and which ones have not. Kepler.gl has an option to export the map you created as a HTML file in which other users can access it. However, the dataset I produced made a 300MB resulting file, which is large for web users to view.

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6. What’s Next?

While Kepler.gl is rudimentary, I’ve got a clear visual of when and where taxi demand spikes. But I want to take it a step further — combining this with trip profitability (fare + tips) and estimated travel time or congestion to recommend optimal pickup zones at any given hour. Think of it like a driver-facing AI assistant.

More on that in the next post.

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Want to explore this yourself? The dataset’s light enough to run locally, and tools like Kepler.gl make this type of exploration a breeze without needing any frontend code.

Let me know what visual trends you notice — especially if you find hidden hotspots I missed.