Smart Parking Space App Presentation
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.
Resource: Microsoft Excel®, DAT565_v3_Wk6_Data_File - use excel to show work
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?
Complete the following in a word doc:
• 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.
Sample Solution
are many potential types of errors in survey sampling. According to Groves (1989)[see 1], the survey errors can be divided into two major groups: First, the errors of nonobservation where the sampled elements use only part of the target population, and the second one is the errors of observation, where the listed data deviate from the truth. Some examples of errors of nonobservation can be ascribed to sampling, coverage or nonresponse which is going to be analysed in the later part of this report. On the other hand, examples of errors of observation can be attributed to the interviewer, respondent or method of data collection. Both of our sources of obdurate errors can vigorously affect the accuracy of a survey. However, these errors cannot be eliminated from a survey but their effects can be reduced by careful devotion to an acceptable sampling plan. Some ways to reduce those errors are: callbacks (where the interviewer calls again the nonrespondents), offer rewards and motivation for encouraging responses, train better the interviewers, scrutinise the questionnaires to be sure that the form has been filled correctly and have an accurate questionnaire construction.