Google Correlate expands to 49 additional countries

Posted by Matt Mohebbi, Software Engineer

In May of this year we launched Google Correlate on Google Labs. This system enables a correlation search between a user-provided time series and millions of time series of Google search traffic. Since our initial launch, we've graduated to Google Trends and we've seen a number of great applications of Correlate in several domains, including economics (consumer spending, unemployment rate and housing inventory), sociology and meteorology. The correspondence of gas prices and search activity for fuel efficient cars was even briefly discussed in a Fox News presidential debate and NPR recently covered correlations related to political commentators.

Health has always been an area of particular interest to our team (Matt Mohebbi, Julia Kodysh, Rob Schonberger and Dan Vanderkam). Correlate was inspired by Google Flu Trends and many of us worked on both systems. So we were very excited when the BioSense division at the CDC published a page which shows correlations between some of their national trends in patient diagnosis activity and Google search activity. With just three years of weekly data, relevant search terms are surfaced. For example, the time series for bloody nose surfaces "bloody snot" and "blood in snot".

While these terms shouldn't come as a surprise, there are others which are more interesting, including searches related to static electricity, dry skin, and red cheeks. Of course, correlation is not causation but we hope that Correlate can be used as a method for researchers to generate new hypotheses with their data.

To help researchers outside the United States, we're pleased to announce support for 49 additional countries in Google Correlate. It's now possible to see correlations like "snorkeling" in Australia, "cherry blossoms" in Japan , and "beer garden" in Germany. We look forward to seeing what new correlations researchers can find with this data!

Reading tea leaves in the tourism industry: A Case Study in the Gulf Oil Spill

Posted by Hyunyoung Choi and Paul Liu, Senior Economists

A few years ago, our in-house economists, Hal Varian and Hyunyoung Choi, demonstrated how to “predict the present” with monthly visitor arrivals to Hong Kong. We took this idea further to see if search queries could predict the future. If users start to research their travel plans some weeks or months in advance, then intuitively shouldn’t we be able to extend "predicting the present" into "predicting the future?" We decided to test it out by focusing on a region whose tourism was recently severely impacted: Florida’s gulf coast.

With the travel industry still in the midst of recovering from a deep recession, the Gulf Oil spill had the potential to do significant economic damage. Our case study on the Gulf Oil spill helped find useful insight into people’s future travel plans to Florida; in fact, we found that travel search queries actually were good predictors for trips to Florida, and destinations within Florida, about 4 weeks later.

The results we saw surprised us. Google Insights for Search suggested that at least with respect to hotel bookings (using data from Smith Travel Research, Inc.), the aggregate effect of the oil spill was modest on Florida travel, since travelers tended to shift their destinations from the affected regions on the west coast to the east coast or central regions of Florida. In particular, hotel bookings for affected areas along the Gulf coast were 4.25% less than predicted, and unaffected areas along the Atlantic coast were 4.89% greater than predicted.

You can read the full case study here or try your own hand at predicting the future!