https://github.com/michaelcurrin/water-crisis-dash

Reporting on Twitter trending data related to the Cape Town water crisis or Corona
https://github.com/michaelcurrin/water-crisis-dash

flask python python3 trending trends twitter water water-resources webserver

Last synced: 11 days ago
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Reporting on Twitter trending data related to the Cape Town water crisis or Corona

• Host: GitHub
• URL: https://github.com/michaelcurrin/water-crisis-dash
• Owner: MichaelCurrin
• Created: 2018-02-25T18:15:08.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-07-13T07:14:07.000Z (4 months ago)
• Last Synced: 2024-10-11T19:36:31.479Z (27 days ago)
• Topics: flask, python, python3, trending, trends, twitter, water, water-resources, webserver
• Language: Python
• Homepage: https://michaelcurrin.pythonanywhere.com/
• Size: 90.8 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 14
• Metadata Files:
  • Readme: README.md

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README

        
# Water Dashboard

> Reporting on Twitter trending data related to the Cape Town water crisis (Python3 [Flask](http://flask.pocoo.org/) web server)

There is only one web page currently on this server. It is viewable at [michaelcurrin.pythonanywhere.com](https://michaelcurrin.pythonanywhere.com). On a request, web server queries a SQLite database that contains trending topic data retrieved from the [Twitter API](https://dev.twitter.com/docs). That data is added on a daily schedule using my [twitterverse](https://github.com/MichaelCurrin/twitterverse) repo, which runs as a separate application and can be used to fetch either high-level trending topics or individual tweet data.

The data which is shown on the server is a filtered view of all the trending data which has been stored in my database over a few months. Therefore it is easy to broaden or narrow the query to displaying relevant data around the Cape Town water crisis, or to focus the query on a different topic in South Africa. It is also easy to broaden the query to other English-speaking locations for which I have daily data, since I retrieve data for many countries and cities in areas like United States and Europe.

## Documentation

- [Installation](/docs/installation.md)

- [Usage](/docs/usage.md)

## Resources

- [Flask](https://flask.palletsprojects.com/en/1.1.x/) website or [Flask](https://flask-doc.readthedocs.io/en/latest/) read-the-docs site. This is the web server.

- [Flask caching](https://flask-caching.readthedocs.io/en/latest/). This reduces queries to the database by caching the view. Built on [Flask-Cache](https://pythonhosted.org/Flask-Cache/).

## Future development

I have plans to expand the visualizations on the server using something like one of these:

- the Python [dash](https://plot.ly/products/dash/) library

- JavaScript's [D3](https://d3js.org/) library.