On his nationally syndicated radio show, Mark Levin discussed the BigGovernment.com investigative report revealing an e-mail from author Joe McGinniss detailing how most of the allegations made in his book about Sarah Palin were unsubstantiated.
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Google is likely to face more privacy-related challenges on the heels of news today that the company’s Street View vehicles have collected more than just the location of WiFi access points.
According to a CNET article today, Google collected those WiFi access points along with the location of client devices including mobile phones and desktop computers.
The cars were supposed to collect the locations of Wi-Fi access points. But Google also recorded the street addresses and unique identifiers of computers and other devices using those wireless networks and then made the data publicly available through Google.com until a few weeks ago.The French data protection authority, known as the Commission Nationale de l’Informatique et des Libertés (CNIL) recently contacted CNET and said its investigation confirmed that Street View cars collected these unique hardware IDs.
Of the dozens of countries that have (or still are) investigated Google’s WiFi data collection, France is the only one to issue a fine. In the US, the FTC closed its inquiry without penalties last year.
But those investigations apparently focused only on the data transferred via access points and made no mention of the collection of device locations. It seems likely that privacy watchers and government officials will call for further investigations based on this latest development.
Google declined to comment to CNET for their report.
Postscript: After our article was published, a Google spokesperson contacted us to share a statement that has also been shared with CNET:
Location-based services provide tremendous value to consumers and the economy. In order to provide these location services, Google and many other companies detect nearby, publicly available signals from Wi-Fi access points and cell towers and use this data to quickly approximate a rough position. This can be done by using information that is publicly broadcast, including that list of Wi-Fi access points you see when you use the “join network” option on your computer and the access point’s MAC address.Related Topics: Google: Legal | Google: Street View | Legal: Privacy | Top NewsWe collect the publicly broadcast MAC addresses of Wi-Fi access points. If a user has enabled wireless tethering on a mobile device, that device becomes a Wi-Fi access point, so the MAC address of such an access point may also be included in the database. Wi-Fi access points that move frequently are not useful for our location database, and we take various steps to try to discard them.
About The Author: Matt McGee is Search Engine Land's Executive News Editor, responsible for overseeing our daily news coverage. His news career includes time spent in TV, radio, and print journalism. His web career continues to include a small number of SEO and social media consulting clients, as well as regular speaking engagements at marketing events around the U.S. He blogs at Small Business Search Marketing and can be found on Twitter at @MattMcGee and/or on Google Plus. See more articles by Matt McGee
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Chris Silver Smith, July 19th, 2011 at 5:37 pm ET: I think the data must be slanted by Chitika’s relatively limited set of sites to derive data from. I don’t believe these findings can be representative. In smaller towns, there are fewer providers, so there’s actually less reason to perform local searches to find more service providers. I think there is actually less online local searching going on in small towns, which is partly why print directories are projecting those markets to remain stronger areas for print yellow pages products to remain viable. Small, rural towns in the South also have slower average internet access speeds, IMHO, which also discourages online searching.
Hmm… Where are they getting this from?
The possible flaws in this logic. If they are considering searches with local keywords then I could see this conclusion. Most large cities have better developed local search and businesses involved in SEO. Therefore while in Phoenix one could simply search for “pest control” and get the results that they want without having to refine it to “Phoenix pest control”. If they miss the fact that both searches have local intent then the survey will be flawed. I believe many people and local qualifiers to search after being disappointed with the first search. This also could be included out of habit for smaller towns simply because of past experience.
Do their numbers add up with what Fredrick Vallaeys reported? – http://twitter.com/#!/Thos003/status/63419377034334208
Could this be based solely on desktop searches? Are more city dwellers carrying smart phones?
It’s unfortunate that the Chitika article didn’t cover how they defined “local” search query. Considering that Google is aware of the (approximate) location of the searcher (based on his/her IP) searching for “car service” should also be considered local, just like “car service in Boston.”
Matt McGee, July 19th, 2011 at 6:00 pm ET: Gabor – did you read the complete article above? I list 15 example queries that were counted as local and it includes several phrases like the one you’ve mentioned here.
Chitika also did a study that showed that most local searches are performed on mobile devices –> http://insights.chitika.com/2010/mobile-users-45-more-interested-in-local-than-non-mobile/. I would like to see what percentage of those people in the smaller towns listed have smartphones vs. the larger cities listed.
…sorry… pest control guy typo…
“I believe many people add local qualifiers to a search after being disappointed with the first search.”
BTW… if they count both “pest control” and then the follow up search “pest control Chattanooga” as 2 separate searches then their numbers would also reflect a higher searches for those areas where local search is not as developed.
Hi everyone, Gabe from Chitika here.
The data we reported was in the form of ratios, so, many of you are correct, that there are fewer people actually “searching local” in the more rural areas compared to cities.
The purpose of this study is not to find out where the largest raw number of people are searching local, rather, it is to find out where the greatest percentage of traffic is composed of local queries. The keywords in our algorithm are not location specific in any way.
Although we give preference to queries with location in them, our algorithm primarily consists of local categories which you might find in a phone-book.
If you have any other questions, check out our website insights.chitika.com or shoot us a message on Facebook.
Thanks! – Gabe
Thanks Matt and Chitika for the thought provoking post.
Right off the bat, only 1 of the cities mentioned is small and none are rural (rural was not mentioned in the article but it mentioned in comments). Several are Southern. I think the explanation is partly related to the very real-world movement afoot to buy local. Lexington, Greenville, Knoxville, and Chattanooga fit the bill particularly well – relatively forward thinking municipalities with a well-off population committed to improving the local economy. Add to that a dash of Southern-ness and there you have it. More local search. And that is cool.
To be fair it is also easier to isolate IP addresses and serve the right local results in towns of this size than say, L.A. In a larger market knowing your IP address doesn’t tell you enough about whether to serve a local result in the way that it does in Greenville, SC. I see more errors served – failures to deliver good local results – in these confusing larger markets. Examples of confusing IP addresses that may or may not give you the right take on local: West Hills and L.A.; Chula Vista and San Diego; Evanston and Chicago; and Marietta and Atlanta. I’m sure there are lots of other examples of areas where IP address can fool the search engine in either direction, either into showing an unnecessarily limited local result or into not showing one at all. In these markets IP filtering is only part of the answer (the other parts are user intent and behavioral patterns). I think getting local search right is more difficult in the larger cities and that may be resulting in the appearance of a smaller percentage of local search.
The outlier in this group is Pensacola FL (56k population, beach town, military town). The others are all progressive mid sized cities 300k + in population.
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