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Everything is herehttps://colab.research.google.com/drive/1gr3cP2BYJz7Wtt9mNEH5FO0VTGnwzzQz?usp=sha

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 You are required to turn in this completed notebook and a Python's script that runs on the Hadoop cluster.

Please read the whole instruction

.You will need to complete Task2 from C to L. Where it says [TODO] and TO_BE_COMPLETED fill all the code snippet in each cell from C to L where it says TO_BE_COMPLETED so that it gives the expected output as shows after each cell from C to L. You might not see the exact number as it shown in the expected output but the format and structure has to be same. I have provided 2 data file to run all the codes and you can download those running cell block B. at the end of the Task 2 it will generate 9 output csv files. Plot those csv files using the code provided on the cell "Line Plot". Your 9 output files should generate 9 plots showing at the end of the notebook. Your plot should be similar the one is shown at the end.

Please use apache Spark RDD using Python.Most of the codes are provided you will need to complete where it says TO_BE_COMPLETED.This is a big data analysis problem and try to optimize your code such a way so that when i will take all your scripts and run it on a server using 10GB of csv file it should run in 2 minutes.

We still use the Safegraph data to better understand how NYC response to the COVID-19 pandemic. If you have any doubts about the data, please consult SafeGraph's documentation for Places Schema and Weekly Pattern.

Problem Description

To assess the food access problem in NYC before and during the COVID-19 pandemic, we would like to plot the visit patterns for all food stores (including restaurants, groceries, deli's, etc.) such as the one shown below.


 
 
[ ]
 

A visualization of Visit Pattern for all food stores in NYC

The solid lines show the median patterns and the transparent areas show the standard deviation across stores for 2019 and 2020.

 
 

 
 
 
 

However, we suspect that the visit patterns may vary across different type of stores. Our hypothesis is that we have changed our shopping behavior during the pandemic. For example, we visit Fast Food and Whole Saler restaurants more often comparing to full service restaurants and typical supermarkets. In particular, we are interested in the following store categories with their NAICS codes:

  • Big Box Grocers452210 and 452311
  • Convenience Stores445120
  • Drinking Places722410
  • Full-Service Restaurants722511
  • Limited-Service Restaurants722513
  • Pharmacies and Drug Stores446110 and 446191
  • Snack and Bakeries311811 and 722515
  • Specialty Food Stores445210445220445230445291445292, and 445299
  • Supermarkets (except Convenience Stores)445110

 
 
[ ]
 

The plot above was created by the linePlot() function (defined later), which takes a Panda's DataFrame consisting of 5 columns as follows.

 
 

 
 
 
 
  • year: column is used for showing the trend line category (orange or blue).
  • date: denotes the day of the year for each data point, for which we project to to year 2020. We chose 2020 as the base year because it is a leap year and would have all possile dates (i.e. month + day combination). The actual date for the data point would be month and day from date combined with the year in year.
  • median: is used to draw the solid line describing the median visit counts across all stores for that date.
  • low: the lower bound of the "confidence interval". In our plot, it is the median minus the standard deviation but will be kept at 0 or above.
  • high: the higher bound of the "confidence interval". In our plot, it is the median plus the standard deviation but will be kept at 0 or above.

NOTES

  • low and high value will be used to create the transparent area that we see in the plot.
  • lowmedianhigh should be computed not only for stores that had visits but also for all stores in Core Places that fit the category. As we learned previously, restaurants with no visits will not be reported in the Weekly Pattern data set.
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