Project by: David Blascyk, Michael Laska, Ingris O'Connor, and Cindy Pendarvis
Go to Minneapolis Scooter Tableau to view the final report.
ETL and Tableau Visualization project to audit the availablity of rental scooters in High-Poverty neighborhoods of Minneapolis, MN.
According to the City of Minneapolis, in order for scooter rental companies to be awarded contracts in 2019, they must ensure adequate numbers of scooters be available in poor neighborhoods.
We chose to gather availability information from Minneapolis Open Data Site and the US Census Bureau to determine whether this criteria had been met.
- Extract and merge data from three sources (Scooter availability, GIS location codes, and zipcode poverty rates) and build database.
- Categorize scooter locations based on type of zipcode (High-Poverty neighborhood, Low-Poverty neighborhood, or Downtown).
- Calculate distribution rates in Jupyter Notebook and then display maps and graphs in Tableau.
Tools
- Python Libraries: Pandas, Numpy, Matplotlib
- Jupyter Notebook
- Tableau
Individual contributions to the project: ETL of data, creation of Tableau dashboards
Snapshot of merged and parsed data:
Tableau Snapshots: