Redmine

We've installed a JupyterHub/JupyterLab system at https://jupyter.civicknowledge.com/. You can log in with your Redmine account credentials, and do data analysis without installing anything.

For more details, see the blog post at:

https://www.sandiegodata.org/2018/07/jupyterlab-for-library-data-projects/

Cheng Li created a graph of the precipitation and river flow rate in a Jupyter Notebook, similar to
Neal Jander's flow rate Shiny App.

Additionally, I've created a Jupyter Notebook for exploring the TDML measurements from CEDEN. This notebook will give you some insight in to the structure of the dataset and how to use it, since I have found good documentation.

For Cheng's notebook, you'll need to download the data first; look for the links for "Precipitation" and "Flow Rate". On the TGML notebook, the data is loaded from our data repository, so you can download the notebook and run it immediately.

Shiny is a web application platform for R which makes it really easy to create web-based data visualizations. Neal Jander used the data for flow rate though Mission Valley in this basic Shiny app.

Suggested next step: add a trace for the total amount of rainfall in the previous 24 hour, or add markers for coliform counts at a downstream test location.

https://njander.shinyapps.io/ShinyWater2/

http://www.sdcoastkeeper.org/learn/swimmable/san-diego-water-quality

Top Five Things We’ve Learned from Water Quality Monitoring

Here are a few demonstration notebooks that show how to access Metapack data and do basic geographic analysis. They are all located in the new Knowledge Base website.

• A basic example of how to access Metapack packages.
• Detailed example of using Census data ( ACS ) from Census Reporter.
• Using ACS Census data and the FFEIC CRA Loans aggregate file to analyze tracts.

These notebooks don't actually address the client's questions, but they do point to the kinds of analysis we can do.