For those of you that have not read the patent here is a salient summary of the concept from the conclusion of Google’s patent filing on Scoring local search results based on location prominence. Here is a relevant description of Location Prominence from the patent filing:

Further, it has been described that a location prominence score may be generated based on a set of factors that includes one or more of the following factors: a score associated with an authoritative document, the total number of documents referring to a business associated with the document, the highest score of documents referring to the business, the number of documents with reviews of the business, and the number of information documents that mention the business. In another implementation, the set of factors may include additional or different factors.

[0073] For example, one factor may relate to the numeric scores of the reviews (e.g., how many stars or thumbs up/down). Another factor might relate to some function (e.g., an average) of all the scores of the reviews. Yet another factor might relate to the type of document containing the review (e.g., a restaurant blog, Zagat.com, Citysearch, or Michelin). A further factor might relate to the types of language used in the reviews (e.g., noisy, friendly, dirty, best). Another factor might be derived from user logs, such as what businesses users frequently click on to get detailed information and/or for what businesses they obtain driving directions. Yet another factor might relate to financial data about the businesses, such as the annual revenue associated with the business and/or how many employees the business has. Another factor might relate to the number of years the business has been around or how long the business has been in the various listings. Yet other factors will be apparent to  one skilled in the art. 

[0074] It may also be possible to use the factors to train a model using machine learning techniques. The model may be used, for example, to determine the probability that a user might select a particular document in the search results.

This patent filing is from 2004 and to the best of my knowledge has not yet been approved. Bill Slawski has discussed this patent in a fair bit of detail. What follows is a specific look at the ranking factors that Google considered in the patent. Much could have changed in the way that Google currently calculates rank but in this summary you can find many of the attributes that were identified in our research last summer and in David Mihm’s Local Search Ranking survey.

Here is a breakout of the set of factors that are prioritzed in the Patent:

You can see how Google has expanded its reiterative PageRank thinking to include non-website-based factors from across the geographic web. Local Search indices evolved when very few small businesses had websites, making it essential to come up with an alternative to PageRank that could use the information that was available at the time.

Google, however, didn’t limit themselves to these five above factors. The patent assumes that geo information across the web will change over time but not in a lock-step fashion across all industries. There is the implicit assumption that available information about any given business will become richer over time. Low-signal industries might rely on the simpler geographic web information while high-signal industries that are very competitive might move closer to the traditional link-based web ranking model, with important geo enhancements.

There was general agreement in David Mihm’s Local Search Ranking Factors survey as to the need to be located in the geographic area searched and to have appropriate categories related to the search query. Those elements allow the business listing to be considered relevant to a particular local query. It appears that this concept of Location Prominence Score when combined with the relevance factors form the core principle behind Google’s ranking.