Mobility-Project

The relationship between mobility and covid-19 during QI of 2020 was investigated. We wanted to figure out whether subsequent shutdowns have affected people’s mobility in different cities and countries around the world.

How to Run Code

1). Copy and paste this link https://covid19-mobility.herokuapp.com/ into your browser.

Technologies Used:

Pandas, Python, Flask, SQL, Postgres, HTML, JavaScript, CSS, Plotly, Git, Github

Data

• Mobility data was extracted from Apple in a csv. Mobility scores were given out of 100 depending on how frequently transit options (walking,driving,biking), would be searched in Apple Maps.

• Covid-19 data was extracted from WHO also in csv format.

Data cleaning, manipulation, and application development process:

-Mobility data was manipulated in pandas to be in first normal form.

• Mobility data was inserted into a Postgres database.
• A connection was established in Flask to serve up the mobility data
• to our javascript file
• A route to our html was established using Flask
• Graphs were created using plotly to demonstrate mobility trends over time in various countries

Screenshots

Displays a comparison of mobility scores among various countries over time. We used plotly to create the graphs because we wanted the user to have dynamic interactions in the application.

Conclusions

• Mobility scores have decreased over time
• During the early phased shutdowns, transit peaks would occur on weekends as opposed to weekdays.