Information Visualization Parking Space

As a city manager for a mid-size city, you must be able to examine patterns and trends to highlight organizational performance and support organizational strategic planning. One of the ways that is done is through analyzing the statistics. Still, just presenting the numbers is not always the most efficient way to present your analysis.

Scenario
You are a city manager for a mid-size city that is anticipating increases in population and auto traffic as new industries move into the downtown area. Parking spaces are already hard to find and traffic congestion can be problematic from commuters and events like concerts and sports. Before considering adding additional parking, you think it is possible to use existing parking more effectively through a smart parking app that identifies parking space availability in different parking lots throughout the city in real time.

In this assessment, you will demonstrate your skill in information visualization when you present your recommendations to the City Council members who are responsible for deciding whether the city invests in resources to set in motion the smart parking space app.

Preparation
Review the parking space usage file.

Select any 2 parking lots. For each one, review the scatter plot showing the occupancy rate at each time stamp during the week of 11/20/2022 –11/26/2022. Identify whether occupancy rates are time dependent. If so, identify the times that seem to experience the highest occupancy rates.

Research “smart cities” to provide guidance and support for your presentation.

Create a 10- to 12-slide information visualization presentation including voice-over or screencast video. Include the following in your presentation:

Outline the rationale and goals of the project.
Analyze the box plot charts showing the occupancy rates for each day of the week and interpret the results.
Analyze the box plot charts showing the occupancy rates for each parking lot and interpret the results.
Choose 2 scatter plot charts showing occupancy rate against the time of day over the course of the week and interpret the results.

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