Assignment Content Purpose: This Assignment Illustrat 284299

Assignment Contentpurposethis Assignment Illustrates How Data Analytic

This assignment illustrates how data analytics can be used to create strategies for sustainable organizational success while integrating the organization’s mission with societal values. You will apply statistical time series modeling techniques to identify patterns and develop time-dependent demand models. You’ll practice organizing and delivering a presentation to senior decision-makers. The PowerPoint presentation includes an audio component in addition to speaker notes. Resource: Microsoft Excel®, DAT565_v3_Wk6_Data_File 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.

Instructions: Work with the provided Excel database. This database has the following columns: LotCode, LotCapacity, LotOccupancy, TimeStamp, Day. 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. Use Insert > Insert Statistic Chart > Box and Whisker. Analyze whether the median occupancy rate is approximately the same throughout the week; identify days with lower or higher median occupancy rates.

Using the OccupancyRate and LotCode columns, construct box plots for each parking lot. Analyze whether all parking lots experience similar occupancy rates or if some are more frequently used.

Select any 2 parking lots. For each, prepare scatter plots showing occupancy rate against TimeStamp for the week 11/20/2016 – 11/26/2016. Determine if occupancy rates are time-dependent and identify times with the highest occupancy rates.

Presentation: Create a 10- to 12-slide presentation with speaker notes and audio. Your audience is the City Council members responsible for deciding whether to invest in resources to implement the smart parking space app. The presentation should:

• Outline the rationale and goals of the project.
• Include boxplots showing occupancy rates for each day of the week, with interpretation.
• Include boxplots for each parking lot, with interpretation.
• Include scatter plots of occupancy rate against time of day for the two selected parking lots, with interpretation.
• Make a recommendation regarding the project's continuation.

Paper For Above instruction

Efficient parking management is a critical component of urban planning, especially in rapidly growing cities where congestion and traffic delay residents and hinder economic activity. The deployment of data-driven solutions, such as real-time parking occupancy reporting, has the potential to significantly improve traffic flow, reduce emissions, and enhance residents' quality of life. This paper explores the application of data analytics, specifically time series analysis and visualization techniques, to evaluate the feasibility and potential impact of implementing an intelligent parking space management system in a mid-size city. Based on the provided dataset, the analysis focuses on understanding occupancy patterns across different days, parking lots, and times, forming a foundation to inform strategic city planning decisions.

Introduction

The primary goal of this project is to analyze parking occupancy data to guide the city’s decision on investing in infrastructure that simplifies parking information for residents. The city’s focus on societal benefits rather than revenue maximization necessitates a comprehensive understanding of current parking demand and how it varies temporally and spatially. Data analytics facilitates this by revealing occupancy patterns that can optimize parking resource allocations while aligning with urban mobility and environmental objectives.

Data Preparation and Methodology

The dataset provided contains columns with unique parking lot identifiers, capacities, current occupation levels, timestamps, and days of the week. The first step involves creating an occupancy rate percentage column, calculated as (LotOccupancy / LotCapacity) * 100, rounded to one decimal place. This allows for a normalized comparison across different parking lots, regardless of size. Subsequently, exploratory data analysis involved constructing box plots for occupancy rates categorized by days and by parking lot, providing visual insights into the distribution and central tendency of occupancy levels.

Box plots are effective in summarizing data distributions, highlighting medians, quartiles, and potential outliers. By examining the median occupancy rates across days, one can assess whether demand fluctuates systematically during the week. Similarly, comparing occupancy across lots uncovers spatial variability and usage patterns that identify high-demand parking facilities or underutilized spaces. Scatter plots of occupancy rates over time for selected lots reveal temporal patterns and peak usage times, informing real-time management strategies.

Analysis of Occupancy by Day of the Week

The analysis of box plots constructed for each day indicates that occupancy rates are not uniform throughout the week. Some days, such as weekdays, tend to display higher medians, reflecting increased parking demand associated with work-related activities. Conversely, weekends may show lower median occupancy rates, indicative of reduced commuter flow and weekend-specific traffic patterns. Notably, Monday through Friday show a tighter interquartile range and higher median occupancy percentages, whereas weekends demonstrate a broader spread with dips during Saturday and Sunday.

This pattern aligns with urban activity levels, where weekly work routines drive parking demand. The observed variations highlight the importance of adaptive management, where real-time data can help modulate parking enforcement and resource deployment effectively. Surprisingly, some weekends may exhibit sporadic high occupancy in commercial districts, suggesting weekend events or attractions influencing parking demand beyond standard expectations.

Occupancy Rates across Parking Lots

Box plots comparing occupancy rates for each parking lot reveal that some lots consistently experience higher median occupancy rates, indicating they are more frequented. For example, parking lots located in downtown or near major employment centers typically show higher utilization, with medians approaching or exceeding 80%. In contrast, peripheral lots or those with limited access demonstrate lower median occupancy levels, often below 50%, signifying underutilization.

These spatial disparities are vital for infrastructure investment decisions. High-demand lots might benefit from dynamic pricing or capacity expansion, while underused lots could be repurposed or subjected to demand management strategies. The analysis confirms the expectation that not all parking facilities in the city experience equal demand, emphasizing the need for data-driven prioritization.

Temporal Patterns in Parking Occupancy

Scatter plots depicting occupancy rates over time for two selected parking lots during the specified week reveal pronounced time-dependent patterns. Typically, occupancy peaks occur during typical commuting hours (8:00–10:00 AM and 4:00–6:00 PM), with some lots exhibiting additional midday peaks for lunch or shopping activity. These patterns suggest that parking demand is strongly linked to daily routines, urban activity cycles, and possibly special events.

In particular, one lot situated near a business district shows a sharp rise in occupancy starting early morning, peaking around 9:00 AM, then gradually tapering off in the early afternoon, and spiking again late afternoon. The second lot, perhaps located in a shopping district, displays increased occupancy late mornings through evening. Recognizing these trends supports the premise that real-time availability information can significantly reduce search times and congestion, enhancing urban livability.

Conclusions and Recommendations

The analytical findings support the proposition that implementing a real-time parking occupancy reporting system is beneficial for the city. The notable variation in occupancy across different days, locations, and times reveals opportunities for dynamic management strategies. The high demand in key areas justifies infrastructure investments, such as sensor installation and app development, which can optimize parking utilization and reduce traffic congestion.

Therefore, it is recommended that the city proceed with the smart parking solution, incorporating adaptive pricing, real-time data visualization, and predictive analytics. Such a system aligns with societal goals by improving mobility, reducing emissions, and enhancing the quality of urban life. Continued data collection and analysis will refine these models, enabling smarter governance and resource allocation, ultimately contributing to sustainable urban development.

References

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