Project-2-Healthy-Neighborhoods

This dashboard demonstrates how to integrate Flask with HTML and JavaScript code to visualize and explore metrics for helping to choose a neighborhood that meets healthy lifestyle requirements. This project originally required to run on a Flask server. For future use to view the project, this version of the site has been convert to not need the Flask server. The original repo can be viewed here: https://github.com/nbullerds/Project-2-Healthy-Neighborhoods.

View revised dashboard on on GitHub pages: https://klharp.github.io/Healthy-Neighborhoods-Dashboard/templates/

Languages and libraries used:

• Python
• Pandas
• Flask
• SQLAlchemy
• JSON / GeoJSON
• D3
• Javascript
• HTML
• CSS
• Bootstrap
• Leaflet
• Plotly
• AnyChart

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Directions

Please clone this repository and then do the following:

1. Peruse the files and the project directory structure in the cloned directory to get familier with the project methodology. Note the use of the templates and static folders.
2. In the Project-2-Healthy-Neighborhoods/static/js/ folder, create a file called config.js
3. Go to https://www.mapbox.com/ and sign up for an API key. Copy that API key and place in in the config.js file in a variable called const API_KEY = "PUT YOUR KEY HERE"; and save the file.
4. In the Project-2-Healthy-Neighborhoods folder, create a file called config.py.
5. Assign your pgAdmin username to a variable called username, like so: username = [pgAdmin username].
6. Assign your pgAdmin password to a variable called password, like so: password = [pgAdmin password].
7. Assign your gkey (google API key to a variable called gkey, like so: gkey = [google places API key], then save config.py. If you don't have a google API key, see information here on how to get one https://developers.google.com/maps/documentation/places/web-service/get-api-key
8. In pgAdmin, create a new database called MSP_Neighborhoods.
9. In the MSP_Neighborhoods database, run NeighborhoodsTableSchema.sql, found in the \schema\ folder, to generate tables for storing Neighborhood and Places data.
10. Open Project 2 ETL notebook.ipynb in Jupyter Notebook
11. Run all the cells. This will store all data needed for the site within the MSP_Neighborhoods database. This may take a few minutes to complete.
12. Run python app.py to launch a flask server connected to the MSP_Neighborhoods database.
13. Observe that the Flask server starts and tells you which port it's running on. Don't close this window.
14. With the Flask server running, enter this address in your Chrome browser: http://127.0.0.1:5000/. You'll see that it loads the index page, allowing you to navigate the website with the data from MSP_Neighborhoods.

Project Team

• Nick Buller
• Kerry Harp
• Sofanit Mengesha