trafficAccidents

Visualize and analyze San Francisco traffic accidents using a Jupyter Notebook, PixieDust, and PixieApps

In this developer journey we will use PixieDust running on IBM Data Science Experience (DSX) to analyze traffic data from the City of San Francisco. DSX is an interactive, collaborative, cloud-based environment where data scientists, developers, and others interested in data science can use tools (e.g., RStudio, Jupyter Notebooks, Spark, etc.) to collaborate, share, and gather insight from their data.

When the reader has completed this journey, they will understand how to:

• Use Jupyter Notebooks to load, visualize, and analyze data
• Run Notebooks in IBM Data Science Experience
• Leverage PixieDust as a python notebook helper
• Build a dashboard using PixieApps
• Fetch data from City of San Francisco Open Data
• Create an interactive map with Mapbox GL

The intended audience for this journey is application developers and other stakeholders who wish to utilize the power of Data Science quickly and effectively.

Included Components

• IBM Data Science Experience: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.

Featured technologies

• Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

• PixieDust Python helper library for python notebooks

• PixieApps: Python library used to write UI elements for analytics, and run them directly in a Jupyter notebook.

• Mapbox GL: JavaScript library that uses WebGL to render interactive maps.

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