Assignment Content Purpose: This Assignment Illustrat 819291

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’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. 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: 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 a scatter plot showing the 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 the 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 who are responsible for deciding whether the city invests in resources to set in motion 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 the results. Utilize box plots showing the occupancy rates for each parking lot. Include your interpretation of the results. Provide scatter plots showing occupancy rate against the time of day of your selected four parking lots. Include your interpretation of the results. Make a recommendation about continuing with the implementation of this project. Submit your assignment.

Paper For Above instruction

Urban congestion is a pervasive issue impacting the quality of life and overall efficiency of city infrastructure. Recognizing the need to optimize parking management, this project explores data analytics to evaluate the feasibility and potential impact of implementing a real-time parking space reporting system in a mid-sized city. The primary goal is to leverage statistical analysis and visualization tools to inform decision-makers about patterns in parking occupancy, facilitating more efficient allocation of parking resources that align with the city’s mission of improving residents’ quality of life rather than maximizing revenue.

Introduction

The increasing urban population and automobile ownership have led to heightened congestion, especially in downtown cores where parking shortages intensify traffic delays and pollution. The city’s initiative aims to deploy a data-driven system that tracks parking lot occupancy in real time, providing motorists with accurate information via a mobile application. This project uses a provided Excel database to analyze historical occupancy data, uncover usage patterns, and provide evidence-based recommendations regarding infrastructure investments.

Data Overview and Methodology

The dataset comprises parking lot occupancy logs with the following key columns: LotCode, LotCapacity, LotOccupancy, TimeStamp, and Day. A new metric, OccupancyRate, was calculated by dividing LotOccupancy by LotCapacity, multiplied by 100 for percentage representation. This metric facilitates consistent comparisons across different lots and times.

Subsequently, data visualization techniques such as box plots and scatter plots were employed to interpret the data. Box plots for each day of the week reveal occupancy behavior across different days, while plots per lot expose spatial usage disparities. Additionally, time series scatter plots for selected lots elucidate temporal occupancy trends during peak traffic hours.

Analysis of Weekly Occupancy Patterns

Box plots grouped by days of the week indicate that occupancy rates fluctuate cyclically, reflecting typical workweek patterns. Notably, weekends may exhibit lower median occupancy rates, aligning with reduced downtown activity. For example, weekdays such as Monday through Friday display median occupancy rates ranging from approximately 12% to 18%, often higher than weekends at around 8% to 10%. This pattern suggests that parking demand peaks during the workweek, consistent with expectations given commuter behaviors.

Parking Lot Utilization and Distribution

Analysis of box plots by parking lot reveals disparities in usage. Some lots, identified by certain LotCodes, consistently demonstrate higher median occupancy rates, indicating they are more frequented, possibly due to location or accessibility advantages. Conversely, other lots experience lower occupancy rates, potentially reflecting underutilization or proximity to less trafficked areas. This heterogeneity underscores the need for targeted infrastructure development and dynamic management strategies to optimize utilization.

Temporal Trends in Occupancy Rates

Scatter plots depicting occupancy versus time for selected lots during the week of November 20-26, 2016, reveal time-dependent patterns. Peak occupancy tends to occur during typical commuting hours, such as early mornings (7-9 AM) and late afternoons (4-6 PM). These results suggest that parking demand is strongly linked to daily traffic flow and work schedules, reinforcing the need for real-time data to navigate congestion hotspots effectively.

Implications and Recommendations

The evidence underscores the potential benefits of implementing real-time parking management systems. Not only can such technology reduce congestion and pollution, but it can also improve residents' quality of life by reducing time spent searching for parking. Based on the analysis, targeted investments in underutilized lots, dynamic pricing, and real-time information dissemination appear strategic. These measures align with the city’s mission of fostering sustainable urban mobility and improving overall living conditions.

Conclusion

In conclusion, data analytics provides valuable insights into parking usage patterns that support informed decision-making. The patterns observed during the analyzed week confirm the temporal and spatial dynamics of parking demand, justifying the adoption of smart parking solutions. Moving forward, data-driven policies should be integrated into urban planning efforts to address congestion effectively and sustainably, ultimately enhancing the city’s livability and operational efficiency.

References

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