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Introduction

This artile will walk through visualizing the fluctuations of Airbnb busineess affected by COVID-19 pandemic. Intuitively, we might have a rough guess what the curve will be looking like, however, I thought this would be interesting to practice both data processing and data visulization when timeseries attribute involved, what’s more important, to better explore the data and express the insight to a wide variety of audience in a more approachable manner, an interactive visulization helps.

Data

The dataset was downloaded form InsideAirbnb, and I am working on Hawaii data, the result mainly display a span of two year from July, 2019 to August, 2021.

However, these are bulletpoints:

• To add new posts, simply add a file in the _posts directory that follows the convention YYYY-MM-DD-name-of-post.ext and includes the necessary front matter. Take a look at the source for this post to get an idea about how it works.
• another point
• that’s it

However, these entries should not be styled:

1. an entry
2. another entry
3. that’s another entry

Jekyll also offers powerful support for code snippets:

def print_hi(name)
  puts "Hi, #{name}"
end
print_hi('Tom')
#=> prints 'Hi, Tom' to STDOUT.

An interesting fact on the economy of modern America

Pictures look like this:

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#jekyll #dash now officially supports both, dark and light theming. Enjoy!https://t.co/4evp9pX2Ws pic.twitter.com/vOQCZjGKic

— 〽️ɪɢᴜᴇʟ (@bitbrain_) August 27, 2019