Small analysis module (python)

You work for a maritime logistics company that requires efficient tracking and analysis of vessels in real-time. The company operates a fleet of ships that transport goods across the globe. They want to leverage vessel tracking data to optimize route planning, monitor vessel performance, and developing near real time flow of different types of commodities.

This case study is a small module of overall development process.

You are provided with a data set (MasterData.xlsx) that contains vessel movement data for a small region/polygon of sea.

This data has data starting from Jan 2023- March 2023.

Below is the image of the polygon for your reference:  

Column Names:

Date: Vessel tracking date

Vessel_ID: Unique seven-digit number assigned to each vessel. For each vessel there is only one Vessel_ID.

Latitude & Longitude: Location/position of the vessel on a given date

DFT : Distance between vessel’s keel and the waterline

Tasks:

1) Create three more data sets from the master data set based on the below given polygons (Polygon3, Polygon2 and Polygon1). 

Location and corresponding names of the polygons mentioned in the below image:

Sample code for getting data for specific polygons along with coordinates for all three polygons are given in the python notebook.

2) Find out list of vessels present in “Polygon3” in last week of March 2023 has entered into “Polygon2” in past 15-20-30 days.

(Note: Create a function/module such a way that you can repeat the same process starting February 2023 to last week of |March and get list of vessels by each week.)

3) For each vessel that entered “Polygon2” find out when they entered the polygon and when they exited (Entry date and Exit Date)

4) Total time spent by each vessel within “Polygon2” 

5) Find out during the time in “Polygon2” whether vessel DFT has changed or not?

6) Calculate time taken by each vessel to travel from “Polygon2” to “Polygon3”

Repeat the above tasks for “Polygon1”.

Create a process/function such a way that it can be used for “Polygon1” and similar such cases.

Finally summarize your analysis and mention any anomalies or potential issues that you identified in the dataset.