Building the Perfect Beach Search Engine

November 11, 2012
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(This article was originally published at Data Mining: Text Mining, Visualization and Social Media, and syndicated at StatsBlogs.)

Currently, as I've mentioned in previous posts, beaches are a strangely under-served segment of the local search space. Searches on Google and Bing for beaches are fielded by entities such as resorts and restaurants that happen to be matches for certain beach related terms. If you search for 'beaches in kauai' you will get hits for beach resorts, etc.

There is plenty of content about beaches, from the many dedicated locale sites to general travel related community sites (like Trip Advisor) and editorial sites (like Fodor's). In addition, there are a number of resources that aggregate structural data about beaches. These include open data resources like GeoNames and GNIS but also proprietary resources like Foursquare.

Unfortunately, there is nothing that brings all these things together. There is not product which provides an aggregate view of the set of beaches or the collection of things said or otherwise reported about them.

With an upcoming trip to Hawai'i at the end of the year, I wanted to make sure I was getting the best value for my travel dollars. I've build a prototype beach search engine which provides the following.

  • a partly curated set of beach data covering approximately 12, 000 international beaches
  • aggregation of beach related content
  • search funtionality (so you can search for kid friendly beaches that offer good snorkeling)
  • summarization of Flickr images so that an impression of what it's like to be at the beach can be formed

I believe there is plenty of potential for such a system. I've already found some hidden beaches that I wasn't aware of at our destination that I'm excited to check out when we get there. My goal is to make the system public in the next few weeks (my trip will be a forcing function for this!).

For now, here is a screen shot of part of the experience.

Beachgeek



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