Category Archives: Local SEO and Maps

How Google Finds ‘Known For’ Terms for Entities

Google finds terms and phrases to associate with entities that can be considered terms of interest for businesses, locations, and other entities. These terms can influence what shows up in search results and in knowledge panels for those entities. Consider it part of a growing knowledge base of concepts, entities, attributes for entities, and keywords that shape the new Google after Hummingbird. Semantics play a role as things that specific entities are known for are identified.

The Red Truck Bakery in Warrenton, Virginia

For example, the Warrenton, Virginia, Red Truck Bakery (local to me) is known for:

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Driving Directions vs. Reviews as Ranking Signals for Google Maps

In my college days, I cooked at some local restaurants (free meals made it an attractive option for a starving college student). One of the restaurants was in the center of town, at one end of Main Street, and it was a popular place for local residents who returned over and over. It had a great reputation, and word-of-mouth propelled advertising for the place. Another dining venue I worked at was outside of the center of town, nearby an interstate highway. It didn’t have a great reputation, and very few regular customers, except for people who would stop during mealtime from the busy interstate. The “food” sign from the highway attracted most of the traffic to its dining room.

Funny thing is that most of the regulars that frequented the first restaurant rarely had to look up its location, because it was so well known. Most of the people who visited the second restaurant had never been there before, and relied upon the highway sign. There’s another restaurant in that location now, and I have no doubt that many people find it via maps or navigation on their mobile devices or in-car navigation. I mention this because I have some issues with a recently granted Google patent.

A screenshot from the patent that shows the different parts of a ranking system for local search that includes directions and reviews.

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How Google May Diversify Search Results by Merging Local and Web Search Results

Google has come under fire the last year or so from critics who claim that the search engine has been providing too many pages from some of the same domains in search results. It appears that this has had them looking at ways that they could provide more diversity within those results. A patent granted to Google earlier this year describes one approach that could have an impact on both local search rankings and Web rankings for authority pages for business entities.

The impact of this approach would be that when these authority pages ranked highly in both Web results and local search results, Google might merge listings for the two, so that the Web search result no longer appears within search results for a specific query and the local search result is potentially boosted higher in results as well.

In the past, I’ve written about How Google Universal Search and Blended Results May Work, describing how Google might decide when and where to include multiple listings within Web search results from different vertical search types, such as local results, images, news articles, videos, and others. Each of these different types of results might be ranked based upon their relevance to a query, and might be included within results based upon how meaningful those results might be to the query and the intent of a searcher.

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Is the Future of Mapping Social?

My neighbor has run over my last two phonebooks, and rendered them virtually unusable. We share the same driveway, and it appears that running over phonebooks, and then backing up to make sure they are really dead has officially become a custom in Virginia, or at least in my neighborhood. It’s OK though, since I can’t remember the last time I’ve used a phone book. I may have a couple of times earlier this century, but I’m not sure. I definitely haven’t used one in in the past couple of years (my neighbor keeps killing them).

On the Fourth of July, Apple published a patent application that describes Routes based on User Ratings and Real Time Accident Reporting. Both Apple and Google have been using GPS information to monitor and report upon gridlock and traffic speed estimates, but imagine both providing a richer and fuller social experience involving the world around them. Imagine being able to choose different routes and see social annotations added to different options on those routes. Here’s a screenshot from Apple’s patent filing:

A screenshot from the Apple patent application showing a number of alternative routes, including choices such as Scenic Route, Light Traffic, No Construction, and Smooth Roads.

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Google’s Query-Based Circles Patent

Are Google’s query-based social circles the answer to Facebook’s Graph Search?

Not too long ago, Facebook launched its Graph Search, which enables people to search for things like “My Friends who live in San Francisco,” and My Friends who like Surfing,’ and “Places my Friends like.”

Imagine if Google Plus allowed you to perform searches such as, “People who take the same bus as me into the city,” or “People who like to eat at the Red Truck Bakery,” or “People attending the Dave Matthews Band Concert next Friday,” and creates in response a social network circle that other people might be invited to join, even temporarily, or who could join anonymously. Or Google Plus may dynamically create such a query-based social circle which it may recommend that you share through as you create a post about a music festival you’re going to, or a meal you’re reviewing from a local hotel.

A screenshot from the patent filing showing social circles for Google Plus, including query based circles for a a music festivals and a grand hotel.

The image above from the patent filing shows a query-based circle for a “Music Festival” and a query-based circle for a “Grand Hotel,” as well as a button to only display query-based circles in the interface.

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Google Granted Patent on Google Maps in Web Search Results

Google’s patents have provided a great number of hints over the past 10 years about local search and how Google treats businesses and landmarks in Maps and Web results and elsewhere. I’ve been fortunate enough to have uncovered some of these patents and written about many of the algorithms and approaches that Google has used, including concepts like location prominence, location sensitivity, Maps in Universal Search, Google’s Crowdsensus Algorithm, and more.

A view across Baltimore's Inner Harbor

I am going to be the keynote speaker at Local U Advanced, Baltimore, starting Friday night, March 8 at 7:00 pm through Saturday at 5:00 pm on March 9 (There’s an early bird discount of $100 if you sign up before Feb. 8th). This Local University presentation will be taking place in Hunt Valley, MD. There’s an amazing group of speakers lined up for the event, covering local, mobile, and social aspects of local search.

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Building Google’s Knowledge Base and Identifying Locations in Web Pages

When we talk about indexing and crawling content on the Web, it’s usually within the context of pages being ranked on the basis of a number of signals found on Web pages that might be ranked in response to queries. Google has told us that the future of search involves Knowledge Bases, and the indexing of Things, Not Strings. Gianluca Fiorelli explored Google’s ideas of Search in the Knowledge Graph Era earlier this week.

A few years back, I wrote some posts about some Google Patents that explored how Google might be extracting and visualizing facts, and using Data Janitors to process that information and clean it up and sort it. Google was granted another patent this week that’s very much related, looking at how Google might understand locations for places collected from Web pages. One of the inventors, Andrew Hogue, gave this Google Tech Talk presentation last year:

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Patent on (Intentional) Errors in Google Maps?

Imagine the Earth broken down into a series of cells, and each of those cells broken down into a series of even smaller cells, and then into smaller cells again, and so on, in a spatial index. Each of the levels become increasingly narrow, and increasingly more precise areas or zoom levels of the surface of the Earth.

As these cells decrease in size, they increase in numbers, which has the impact of increasing the zoom level and the accuracy of areas represented in such an index. Might work good in a place like China, where latitude and longitude are banned for export as munitions. Such a set of cells might be part of a geospatial analyzing module that links specific businesses and points of interest (parks, public regions, landmarks, etc.) to specific places on this model or index of the earth. That might be one index of the businesses and one index for the points of interest, or a combined database that includes both.

Sometimes that index might include a business and a landmark within the same cell. While that could be correct in some instances, such as a shop appearing within the Empire State Building, Often its an error, and sometimes even an intentional error. People will sometimes enter incorrect information into a geographic system like this to try to gain some kind of advantage.

If people search for something like a motel “near” a particular park for instance, the motel that appears to be next to, or even within the boundaries of that part might seem to have something of an an advantage in terms of distance from that part when it comes to ranking the motel. And, sometimes Google doesn’t seem to do the best job in the world at putting businesses in the right locations at Google Maps.

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