🚲 NYC CitiBike Usage Analysis (BigQuery + Tableau Dashboard)

This project analyzes and visualizes NYC CitiBike usage trends using Google BigQuery for data extraction and transformation, and Tableau for interactive dashboards. The analysis combines trip data, geographic details, and weather data to uncover urban mobility patterns.

🔗 View Tableau Dashboard

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🧰 Tools & Technologies Used

• Google BigQuery – SQL-based data extraction, aggregation and transformation
  • Query available in the other file named: 'NYC Citi Bikes.sql'
• Public Datasets:
  • bigquery-public-data.new_york_citibike.citibike_trips
  • bigquery-public-data.noaa_gsod.gsod20*
• Custom ZIP Code Table – Joined from cyclistic-stations-bi.us_geo.zip_codes
• Tableau – Dashboard creation, interactive filtering, mapping
• Mapbox – Used within Tableau for geo-boundary mapping

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🧠 Project Overview

The project explores:

• Monthly and seasonal ride trends
• Usage trend differences Subscribers and Customers
• High-volume ZIP codes and neighborhoods
• Route patterns across boroughs
• Weather’s impact on ride volume

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🔍 Process Thinking

1. Data Aggregation via SQL in BigQuery

A complex SQL query was written to:

• Join citibike trips with ZIP code geographies using ST_WITHIN
• Enrich trips with weather data from Central Park (wban = 94728)
• Map coordinates to boroughs and neighborhoods
• Round trip durations to 10-minute bins for aggregation

Resulting dataset from the query was exported from BigQuery and imported into Tableau.

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2. Data Modeling & Calculations

• Created trip_count, trip_minutes, and aggregated KPIs
• Weather fields used: temp, wdsp, prcp
• All aggregations done in SQL to reduce Tableau load

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3. Dashboard Design in Tableau

• Used filters for Usertype, Neighborhood, and Time
• Enabled map-driven interactivity
• Used clean, color-coded layouts with consistent labeling

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📊 Key Dashboards

📈 Summer Trend Analysis

• Monthly stacked bars comparing user types

🔥 Trip Count Heatmap

• Heatmap of ZIPs by month, colored by trip volume

🧭 Start-End Dive Table

• Matrix of start/end neighborhoods with average trip time

🗺️ Borough Mapping

• Map of boroughs and their activity volume

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🧑‍💻 Author

Built by Usman Khalid
This project is part of the Google BI Specialization – Data Visualization Capstone

🔖 Tags

#BigQuery #SQL #Tableau #CitiBike #NYC #UrbanMobility
#GeospatialAnalysis #PublicDatasets #GoogleBISpecialization #DataVisualization