The aim of this project is to map tourists’ perceptions of different urban areas through data retrieved from vacation rental platform Airbnb.
After their stay, Airbnb guests score their feeling about the neighbourhood using a star-based rating system. The aggregated rating of each Airbnb listing is publicly accessible, and given the widespread expansion of this platform, a large amount of data is available for the most visited cities. When overlaid on a map of the city, the data reveals interesting geographic patterns and exposes subjective perceptions on safety, upkeep or convenience.
This project has been featured in Inverse, Lifehack, Next City, Geoawesomeness, Maps Mania, newspapers El País and Tages Anzeiger, and articles by Peter Murray and Florence Broderick on the topic of smart cities. An image made from these maps for European cities made it to the front page of Reddit. Nathan Yau followed on creating analogous maps for US cities in his site Flowing Data.
The raw data for this project has been retrieved from Inside Airbnb and visualized using CARTO. Each point in the map represents an Airbnb listing, and the colour code indicates its location rating: ● 5 stars, ● 4.5 stars, ● 4 stars, ● 3.5 stars, ● 3 stars, ● 2.5 stars, ● < 2.5 stars. Points that appear faded denote listings with anonymized address, which may be in a range of < 150 metres from the location shown in the map.
Direct links to fullscreen maps in alphabetical order (* updated in July 2018): Amsterdam, Antwerp, Athens, Austin, Barcelona, Basque Country*, Beijing*, Berlin, Bordeaux*, Bristol*, Brussels, Chicago*, Copenhagen, Dublin*, Edinburgh, Geneva, Hong Kong, Istanbul*, Lisbon*, London, Los Angeles, Lyon*, Madrid, Mallorca, Manchester, Melbourne, Milan*, Montreal, Naples*, New Orleans, New York, Oslo*, Paris, Porto*, Prague*, Puglia*, Rio de Janeiro*, Rome, San Diego, San Francisco, Seville*, Sicily*, Stockholm*, Sydney, Taipei*, Toronto*, Venice, Vienna.
