xtern-python

First, I read the dataset with pandas and put it into a scatterplot. I shaded the values by power level to see a distribution, but with over 25,000 scooters, the data overlap is obvious. I used a 2D density map to see that the most popular scooter locations are centered at approximately (.9, .75) and (.12, .18). I am not sure if it would be smart to use Dijkstra's algorithm for finding a shortest path, and how to modify it for power level efficiency. It is very odd that the charging truck is at coordinate (20.19, 20.19), and 25,000+ scooters can fit in values less than coordinate (1.5, 1.5). As of 2018, the city of Austin, Texas, had over 11,000 scooters. That is the 11th largest city in the U.S. by population, so it is reasonable to assume this graph represents at least two large cities. If we take the size of Austin, 305.06 square miles, and multiply it by 2.33 (25668/11000), we can say our dataset encompasses about 711 square miles. We can say our dataset is approximately 1.5x1.5 units, giving .1 units ~ 1.77 miles, and the truck can get to our city range in about 6.6 hours.