Exploring Air Quality
Looking More Closely at a Region of Interest
Now that you have had a chance to explore the air quality data, you can check out how a data scientist might do this. Data scientists often do their work in interactive programming environments, where they keep their notes, code, and analysis all together in one document. As an example, we have created one of these documents, a Jupyter Notebook, for you.
With the notebook, you can look more closely at a single region. Choose a region of interest to you. Decide on ways to describe and display the data from these regions to a local council. You can even see how a data scientist might bring in data from another source to give more context to their analysis.
To guide you, Melisa, a UC Berkeley Data Science student, has recorded a video walkthrough for running the notebook. Follow Melisa while she describes how to use the notebook.
Getting started with Jupyter Notebooks
Jupyter Notebooks contain explanatory text, code chunks, and outputs like data summaries and data visualizations. In this notebook, some code chunks are already in place so you can follow along and run the code chunks in order. There are also ways to interact with the code to tailor your analysis to what interests you. Best of all, if you know how to code in Python, you can add your own code chunks. To get started with the notebook, click the link below.
Note: As Melisa said, clicking on the link launches a new tab with the Notebook file on a free service called Binder. There are two things to keep in mind about Binder:
- It can take a few minutes to load the page as it searches for free resources, so be patient.
- The files and your computing session will not be saved, so if you want to save your work be sure to download the file, or print to a pdf file.
After you have opened and run the commands in the notebook, you can interact with the maps, and you can see a second dataset in an additional video. In this second video Maham will explain how to use the interactive tools in the notebooks and how to map the AQI measurement with a dataset of local schools.