Scenario: A city’s administration isn’t driven by the goal of maximizing revenues or profits but instead looks at
improving the quality of life of its residents. Many American cities are confronted with high traffic and
congestion. Finding parking spaces, whether in the street or a parking lot, can be time consuming and
contribute to congestion. Some cities have rolled out data-driven parking space management to reduce
congestion and make traffic more fluid.
You’re a data analyst working for a mid-size city that has anticipated significant increments in population and
car traffic. The city is evaluating whether it makes sense to invest in infrastructure to count and report the
number of parking spaces available at the different parking lots downtown. This data would be collected and
processed in real-time, feeding an app that motorists can access to find parking space availability in different
parking lots throughout the city.
LotCode: A unique code that identifies the parking lot
LotCapacity: A number with the respective parking lot capacity
LotOccupancy: A number with the current number of cars in the parking lot
TimeStamp: A day/time combination indicating the moment when occupancy was measured
Day: The day of the week corresponding to the TimeStamp
Insert a new column, OccupancyRate, recording occupancy rate as a percentage with one decimal. For
instance, if the current LotOccupancy is 61 and LotCapacity is 577, then the OccupancyRate would be
reported as 10.6 (or 10.6%).
Using the OccupancyRate and Day columns, construct box plots for each day of the week. You can use Insert
Sample Solution