As a map won't work for this years primary, or rather, would look weird as some of the cities/towns didn't have an election this years primary. I chose to use a scatter plot to visualize the change in voter turnout between the 2016 primary election and this years 2020 primary. A teal dot, represents a precinct that has seen an increased turnout from 2016 and a purple dot a decrease in turnout.

Previously, my use of election data was limited to viewing turnout and showing votes for each candidate showing which recieved more votes. This year I used Python & Pandas to find some interesting facts hidden within the primary election data. Things like, which endorsed candidate lost their election, or if mail-in votes effect the outcome from the ballot box on election day.

With the COVID-19 pandemic mail-in voting has seen a huge bump as people are less likely to go to the polls. This post will take A look into the scale of mail-in voting for the 2020 primary compared to previous elections.

Last year I attempted to make a live election graph in Tableau. However, Tableau Public only allows data to automatically update once every 24 hours. Clearly... that didn't work out well, so this year I decided to try again and create my first Python web app using Flask, Bootstrap and AWS.

Using previous election results data from Rhode Island's Board of Elections since 2010, I normalized the data and removed multi-vote races using Python to find the 5 closest single-vote elections for every primary/general each year. I found it a bit surprising how many elections were decided by fewer then 10 votes between the top 2 candidates.

Using reviews from people who stayed at Airbnb locations within Rhode Island. I created wordclouds of the most used words at each location across the top 5 cities/towns with the most airbnb rentals.

I took a look at details around hosts and listings for Airbnb's located in Rhode Island. Specifically, information regarding location, room type, superhost, reviews and price. Using data from insideairbnb.com monthly extracts from Oct. 2018 - May 2020 combined using python.

An interactive drill-down treemap allowing you to view loans under 150k by sector -> subsector -> industry group -> national industry by NAICS code. A table is provided to show all loan-level data, however those under 150k lack a business name, while those over 150k lack a specific loan ammount.