This was some code I wrote for CSCI-6250 Frontiers of Network Science course at RPI in Fall 2022 taught by Boleslaw Szymanski. I wanted to see how human mobility patterns change based on precipitation. This was inspired by the paper "Understanding individual human mobility patterns," written by Marta C. González, César A. Hidalgo, and Albert-László Barabási.
Here's the abstract for the final paper I wrote for the class:
The goal of the experiment was to determine how precipitation affects individual human mobility. I used Google’s Mobility dataset from 2020 in Rensselaer County, which tracked the average percent change of an individual’s mobility compared to a baseline from the first five weeks of 2020. I combined this dataset with the past precipitation dataset from the National Oceanic and Atmospheric Administration (NOAA), which recorded the total amount of rain on each day of 2020 from Troy 6.9 NE, a weather station in Troy NY.
I determined that the percent change of mobility usually decreases on days with a nonzero amount of precipitation. This is especially true for parks; people are less likely to go outside on rainy or snowy days. The only exception for the decrease in mobility is in residential areas. People are more likely to stay indoors at home on rainy days. One note here is that the dataset was taken during the quarantine in 2020, so this does not necessarily represent a typical mobility pattern. In the future, it would be beneficial to see how the pandemic affected human population mobility in the long term.
