u/datanerdke

I built this dashboard to answer one question: how is Airbnb really being used in Cape Town and is it actually affecting the city's neighbourhoods?

The dataset is from Inside Airbnb, an open data project that scrapes Airbnb listings periodically. It covers 26,877 listings in Cape Town.

A few things worth noting:

1. 22,179 listings are entire homes. Only 4,541 are private rooms. The "spare room" use case is a small minority.
2. 16,275 hosts (60.6%) have multiple listings. Some have over 100 entire home listings, which puts them firmly in commercial operator territory.
3. Only 393 listings out of 26,877 have a minimum stay long enough to qualify as longer term rentals. The rest are all short term.
4. Average revenue for an active listing is $97,188 a year at $3,281 a night. That makes long term residential letting financially unattractive for landlords.
5. 10,063 listings recorded zero nights booked in the last 365 days, which raises questions about how many of these are genuine listings.

Built in Tableau. Data from Inside Airbnb.

Interactive version: https://public.tableau.com/views/InsideAirbnbCapeTown/InsideAirbnb?:language=en-US&:sid=&:redirect=auth&:display_count=n&:origin=viz_share_link

I pulled Chicago's food inspection dataset via their public API and filtered down to failed inspections only. A few things stood out.

Subway topping the list makes sense by volume since they have more locations than most chains, but 69 failures is still a lot. Dunkin' Donuts at 33 is a big drop to second. What's interesting is that smaller local chains like Sharks Fish & Chicken and Las Islas Marias are sitting in the top 5 alongside brands with massive corporate compliance teams.

Most of the failed inspections are coming from routine canvass visits, the unannounced scheduled ones, not complaints. 3,128 failures from canvass versus 1,021 from complaints. So the system is catching more through routine checks than through people actually reporting problems.

Over 4,500 of the failed inspections are from high risk establishments. High risk just means places that cook raw meat on site or serve vulnerable populations, basically most sit-down restaurants. Medium risk added 700, low risk only 102.

The one that actually surprised me was schools showing up third in failures by facility type with 633, sitting just behind grocery stores. That's not a food industry problem, that's a different conversation entirely.

Built in Python using pandas and matplotlib. Data is from Chicago's open data portal.