Archived | Analyze traffic data from the city of San Francisco

Archived content

Archived date: 2019-07-18

This content is no longer being updated or maintained. The content is provided “as is.” Given the rapid evolution of technology, some content, steps, or illustrations may have changed.


Built for anyone using data to create Jupyter Notebooks and other artifacts, this pattern shows the power of the open source helper library PixieDust. With PixieDust, hosted on IBM Watson™ Studio, a developer or other user can quickly create charts, graphs, and tables without complex code, in an interactive and dynamic manner. In addition, PixieApps are used to embed UI elements directly in the Jupyter Notebook. Given an open source data provider like the city of San Francisco’s DataSF Open Data, PixieDust and IBM Watson Studio can empower the user to analyze and share data visualizations.


DataSF Open Data provides hundreds of data sets from the city and county of San Francisco. In this pattern, we demonstrate how to incorporate open data into a Jupyter Notebook hosted on IBM Watson Studio, and how to quickly and easily create graphs and charts using PixieDust. We then use PixieApps to create UI elements that can be run directly in the Jupyter Notebook.

When you have completed this pattern, you will understand how to:


traffic prediction analysis

  1. Load the provided notebook onto the IBM Watson Studio platform.
  2. DataSF Open Data traffic info is loaded into the Jupyter Notebook.
  3. The notebook analyzes the traffic info.
  4. You can interactively change charts and graphs.
  5. A PixieApp dashboard is created and can be interacted with.


Find the detailed steps for this pattern in the README. The steps show you how to:

  1. Sign up for Watson Studio.
  2. Create the Spark Service.
  3. Create the notebook.
  4. Run the notebook.
  5. Analyze the results.
  6. Save and share.
Scott D’Angelo
David Taieb