Visualizing NYC Airbnb Data

Posted on Apr 29, 2020

I created an R shiny app visualizing data for Airbnb listings in New York City. I used data from Inside Airbnb

My focus was to design something to help a user determine the best neighborhood to stay in New York, by observing several factors such as: price, location, property type, number of listings. Because each person has unique preferences, I wanted to provide different options, and allow users to decide to their own liking.

Link to my shiny app.

My shiny app consists of three main tabs: 

  • Map - This is an interactive map that displays the average price of listings for each neighborhood. Light blue circles indicate more affordable  areas, while dark purple circles indicate the most expensive. As expected, upper Manhattan is much cheaper than lower Manhattan. However, for those who are determined to stay in lower Manhattan, good deals can be found! If you're looking to stay in Battery Park, expect to pay $414/night (average price of listings), while you can stay in the Financial District for $226/night and it's only a short walk away.
  • Where to stay - This tab presents a maximum of 10 recommended neighborhoods that fits the user input criteria. There are two visualizations to help users compare various neighborhoods: the bubble plot, and the map. The bubble plot is designed to help a user identify affordable neighborhoods with an abundance of listings, that are also well reviewed, while the map is to illustrate the trade off in location.
  • Summaries - other data visualizations providing more detailed information about each neighborhood.

Future work:

I plan to update the summaries tab so that a user can get a side by side comparison of two neighborhoods they are interested in. 

About Author

Jessie Wang

Jessie is a graduate from the University of California, Santa Barbara with a degree in Actuarial Science. Upon graduation, she joined UnitedHealth Group as an actuary where she gained a wide array of experience in the healthcare industry....
View all posts by Jessie Wang >

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