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 in an effort 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 or not 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 motorists can access to find parking space availability in different parking lots throughout the city.

Using the OccupancyRate and Day chart: Is the median occupancy rate approximately the same throughout the week? If not, which days have lower median occupancy rates? Which days have higher median occupancy rates? Is this what you expected?

Using the OccupancyRate and LotCode chart: Do all parking lots experience approximately equal occupancy rates? Are some parking lots more frequented than others? Is this what you expected?

Using the Lot Occupancy Rate charts: Are occupancy rates time dependent? If so, which times seem to experience the highest occupancy rates? Is this what you expected?