Travel planning website TripHappy has developed a cluster analysis tool to help identify the best neighborhoods to stay in when visiting cities around the world. The tool analyses the neighborhood ratings from a number of hotel and homestay listing websites. It identifies the hotels in a city with the best neighborhood ratings and then finds clusters of hotels with the best neighborhood ratings based on locational proximity.
Using the results of this cluster analysis TripHappy is able to provide Google Maps for cities around the world which show you the best neighborhoods in which to stay. The maps also show you the locations of hotels within these neighborhoods and nearby points of interest that you can visit on your trip.
You can test how closely you agree with TripHappy's results by seeing which neighborhoods it identifies as the best - in locations that you are familiar with. For example in London it identities areas in Westminster and West London as the best neighborhoods (which if you are a tourist probably are good neighborhoods to be based in). In New York many of the best neighborhoods identified by TripHappy are in Mid Town (again neighborhoods which make a good central base for tourists).
You can read more about the TripHappy clustering analysis tool on the TripHappy Blog. This form of clustering analysis could be applied to other types of interactive maps. For example a real-estate map could identify the best neighborhoods in which to live (based on crime, school ratings, restaurant ratings etc,) If you want to get started building your own clustering analysis the TripHappy blog post provides a little information on the clustering algorithm they used to identify the best neighborhoods for tourists to stay.
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