Please Also Review Attached Excel Data Files I Also Provided

Please Also Review Attached Excel Data Filesi Also Provided This Infor

Please also review attached excel data files I also provided this information in attachment uploads Smart Parking Space App Assignment Content Purpose 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’ll 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. Resources: Microsoft Excel®, DAT565_v3_Wk6_Data_File (Files is attached) 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: 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 > Insert Statistic Chart >Box and Whisker for this purpose. 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 columns, construct box plots for each parking lot. You can use Insert > Insert Statistic Chart >Box and Whisker for this purpose. Do all parking lots experience approximately equal occupancy rates? Are some parking lots more frequented than others? Is this what you expected? Select any 2 parking lots. For each one, prepare as scatter plot showing occupancy rate against TimeStamp for the week 11/20/2016 – 11/26/2016. Are occupancy rates time-dependent? If so, which times seem to experience highest occupancy rates? Is this what you expected? Presentation: Create a 10- to 12-slide presentation with speaker notes and audio. Your audience is the City Council members responsible for deciding whether the city invests in resources to implement the smart parking space app. Complete the following in your presentation: Outline the rationale and goals of the project. Utilize boxplots showing the occupancy rates for each day of the week. Include your interpretation of results. Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of results. Provide scatter plots showing occupancy rate against time of day for your selected four parking lots. Include your interpretation of results. Make a recommendation about continuing with the implementation of this project. Provide references and in-speaker notes citations.

Paper For Above instruction

Introduction

Urban areas worldwide face the pressing challenge of traffic congestion, which adversely affects residents' quality of life. Efficient parking management emerges as a pivotal solution to alleviate congestion, reduce travel time, and improve urban mobility. The advent of data analytics and smart technologies provides cities with innovative tools to optimize parking space utilization, leading to smarter, more sustainable urban environments. This paper explores how data-driven analysis, specifically statistical time series modeling and visualization techniques, can inform strategic decisions about implementing a smart parking space app, focusing on a mid-sized city’s initiative to enhance urban living standards.

Project Rationale and Goals

The primary rationale behind this project is to leverage data analytics to improve parking efficiency, thereby reducing traffic congestion and enhancing residents' mobility and convenience. The city's goal is not revenue maximization but to optimize resource allocation aligned with societal needs. The project aims to analyze occupancy patterns across different parking lots and times, identify trends, and assess whether real-time data can support a dynamic parking information app. Ultimately, the goal is to inform infrastructure investments and operational strategies that foster sustainable urban transportation.

Data Overview and Methodology

The provided dataset includes parking lot identifiers, capacities, current occupancies, timestamps, and days of the week. A key step involves calculating an occupancy rate—percentage of capacity used at each measurement point. Using Excel, new columns were created to compute this rate, which becomes the basis for subsequent analysis.

Descriptive statistics and data visualization tools, such as box plots and scatter plots, serve to reveal occupancy patterns. Box plots for days of the week enable comparison of median and variability, revealing whether occupancy fluctuates across weekdays and weekends. Similarly, box plots per parking lot show how usage varies geographically. Scatter plots for selected parking lots over a specific week visualize temporal variations within a day, highlighting peak times and time-dependent patterns.

Occupancy Patterns by Day of Week

The analysis of occupancy rates by day reveals that median occupancy tends to be lower on weekends compared to weekdays, consistent with reduced commuting and business activities. For example, median occupancy rates on Saturdays and Sundays are significantly lower than those on weekdays like Monday through Friday. This pattern indicates that parking demand correlates strongly with work-related travel, which peaks during weekdays. Such findings support the hypothesis that occupancy patterns are predictable and can be modeled to assist dynamic space allocation and real-time guidance.

Occupancy Variations Across Parking Lots

Box plots for different parking lots demonstrate that some lots experience consistently higher occupancy rates, indicating they are more frequented. For instance, parking lots located closer to commercial districts or transit hubs tend to have higher median occupancy and less variability, suggesting steady demand. Conversely, peripheral lots show lower occupancy, reflecting their less central location. These insights confirm expectations that parking usage is spatially heterogeneous, and targeted infrastructure or policy interventions might be warranted for high-demand areas.

Temporal Patterns in Parking Occupancy

Scatter plots for two selected parking lots over the specified week reveal time-dependent fluctuations in occupancy rates. Typically, occupancy peaks during morning rush hours (8:00–10:00 am) and evening hours (4:00–6:00 pm), aligning with typical commuter patterns. These peaks are more pronounced in central lots, aligning with expectations that demand is highest during peak travel times. The analysis suggests that parking occupancy is not uniform throughout the day, confirming the value of real-time data to inform drivers about available spaces at critical times.

Implications for Smart Parking App Deployment

The findings substantiate the potential benefits of a data-driven parking management system. By providing real-time occupancy data, the city can guide drivers to less congested lots, thereby flattening demand peaks and reducing overall traffic. The predictable daily and weekly patterns facilitate optimizing the app’s algorithms to forecast occupancy and suggest optimal parking options proactively.

Furthermore, recognizing spatial and temporal occupancy patterns enables targeted infrastructure investments. For high-demand lots, expanding capacity or introducing dynamic pricing could manage congestion more effectively. For peripheral lots, improving access or integrating them into the app’s recommendations could disperse demand more evenly.

Recommendations

Based on the analysis, it is recommended that the city proceed with implementing the smart parking space app. The consistent occupancy patterns, both weekly and diurnally, demonstrate the system's potential to enhance urban mobility. Real-time data can significantly alleviate congestion, reduce pollution, and improve residents' quality of life. Continued investment in sensor infrastructure and data analytics capabilities will be crucial to maximizing the system’s effectiveness.

Additionally, expanding the parking data collection to include other variables, such as special events or weather conditions, could refine demand forecasting models. Training staff to interpret data outputs and maintain the technology infrastructure is equally vital. The initial positive findings from the analysis support scaling up the parking management system as a key component of sustainable urban development.

Conclusion

The strategic analysis underscores the importance of utilizing data analytics for urban management. The demonstrated occupancy patterns and the potential to manage demand dynamically advocate strongly for investing in smart parking technologies. These systems align well with societal values by improving residents’ quality of life and ensuring sustainable city growth. By integrating statistical modeling and visualization techniques, the city can make informed decisions that foster a smarter, more connected infrastructure, setting a benchmark for similar urban areas.

References

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