In this developer journey, we will use PixieDust running on IBM Data Science Experience to analyze traffic data from the city of San Francisco.

Data is claimed to be the most valuable commodity in the world. At IBM, we want you to take advantage of your data – manipulate it, visualize it, and understand it so you can make actionable insights. This is why we are especially excited to introduce PixieDust traffic analysis to add to our journey collection.

The journey leverages the IBM Data Science Experience (DSX) and PixieDust and PixieApps. DSX is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (such as RStudio, Jupyter Notebook, Spark, etc.) to collaborate, share, and gather insights from their data. DSX is powered by IBM Bluemix®. PixieDust is an open source helper library that works as an add-on to Jupyter Notebook to improve the user experience of working with data. It also fills a gap for users who have no access to configuration files when a notebook is hosted on the cloud.

In the journey, developers will use PixieDust features to quickly and easily create charts, graphs, and maps, using open datasets from the city and county of San Francisco. By integrating with Mapbox GL, a developer can introduce a dynamic map that utilizes the data in the Jupyter Notebook to aid in visualization. We also include an introduction to PixieApps, which are Python classes used to write UI elements for your analytics, running directly in a Jupyter Notebook. Our introduction showcases how to create map layers for new datasets that can be toggled via user input. The details will be sufficient to allow a developer to create their own PixieApps based on individual use cases.

Get started with our PixieDust traffic analysis journey today. You’ll find code, step-by-step instructions, architecture diagrams, and more. We hope you are taking advantage of the world’s most valuable commodity.

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