Last year the New York City Taxi and Limousine Commission released a massive dataset of pickup and dropoff locations, times, payment types, and other attributes for 1.2 billion trips between 2009 and 2015. The dataset is a model for municipal open data, a tool for transportation planners, and a benchmark for database and visualization platforms looking to test their mettle.

MapD, a GPU-powered database that uses Mapbox for its visualization layer, made it possible to quickly and easily interact with the data. Mapbox enables MapD to display the entire results set on an interactive map. That map powers MapD’s dynamic dashboard, updating the data as you zoom and pan across New York.

See the full MapD blog post to learn how it computed and visualized the dataset.

To show how travel corresponds to regional retail activity – a common business intelligence use case in real estate, finance, and advertising – MapD spatially joined the pickup and dropoff locations to every store within 30 meters.

Say you’re a big box retailer scouting a new store location. You can use MapD and Mapbox to view taxi trips to and from your existing stores, see where potential customers live, and identify underserved areas. You can also visualize how these trips have changed over time to understand retail trends.

As an example, here’s every trip from a Target store in NYC.

Database solutions like MapD aren’t just making queries faster, they’re making it possible to ask new types of questions. Mapbox puts these questions and their answers on a map that’s as fast as your data.

Need fast maps to identify outliers, find high-performing territories, or spot new opportunities? Drop us a line at