NYC Taxi Data Analysis with Google BigQuery

Urban transportation generates huge datasets daily. Analyzing NYC taxi rides helps uncover important mobility patterns like ride volumes, popular pickup and drop-off locations, fare trends, and seasonal variations. Traditional data processing tools struggle to query such massive datasets quickly. Using Google BigQuery, a serverless cloud data warehouse, you can run lightning-fast SQL queries on terabytes of taxi trip data to uncover valuable insights in minutes.

With BigQuery, you can efficiently query NYC Taxi and Limousine Commission (TLC) datasets — analyzing millions of ride records. You can find peak demand periods, map popular pickup zones, study ride distances and trip durations, and even model surge pricing patterns. Visualizing results with Google Data Studio or Looker Studio creates stunning dashboards to present your insights on urban transportation trends.

You start by connecting to the NYC Taxi public datasets hosted on Google BigQuery. Using SQL queries, you analyze different attributes like trip distance, pickup/drop-off locations, fare amounts, and trip times. You can perform aggregations, joins, and geospatial analysis to identify trends. Visualizations like heatmaps, time series graphs, and fare histograms make the findings engaging and easily understandable for stakeholders.

• Connect to BigQuery public datasets and locate the NYC Taxi dataset (trip records for yellow, green, and for-hire vehicles).
• Write SQL queries to find busiest pickup times, popular routes, surge pricing patterns, and revenue distributions.
• Analyze temporal (hourly, daily, seasonal) trends in ride volumes, trip distances, and trip durations.
• Visualize insights using Google Data Studio, Looker Studio, or export to Tableau/Power BI for dynamic dashboards.
• Deploy results into a story-driven dashboard to present transportation insights professionally.

• NYC Taxi Trip Data on BigQuery
• Getting Started with Google BigQuery
• Building Dashboards with Data Studio