TRAVEL COMES WITH NUMEROUS UNDERLYING COSTS THAT AREN’T EASY TO WEIGH.
And as a daily multi-modal commuter, I wanted to understand how the underlying costs of my transit everyday decisions. So I tracked my every move and documented the time and financial costs of my available options.
In visualizing the data, it became clear that my decision-making was only partially driven by money--and that there was opportunity for big financial losses or gains, depending on how well I planned ahead.
OVER THE COURSE OF A WEEK, I DOCUMENTED EVERY TRAVEL DECISION I MADE.
I wanted to understand the underlying opportunity cost of my actions and seek out the most cost effective and time efficient options for my everyday travel.
Along the way, I learned that we analyze data can change the way we understand and learn from it, and the simple act of reading data can be incredibly challenging. Working with time and date-based datasets in Python quickly taught me what an uphill battle it can be to translate data from mere numbers into beautiful, legible visuals. Wrangling spreadsheets is 99% of the work!