Google Local Search Glossary

The following is a collection of terms and definitions from a number of Google’s patent filings on Local Search. It’s possible that not everything discussed in these patent applications has been incorporated into Google Local search – but the interesting thing about many of these patent filings is exploring whether or not they may have been.

Search query categorization for business listings search

Local Search Result with Pasta category

Category Classification Component – Finds appropriate categories for searchers’ queries. May use yellow page business listings or a category classification model automatically trained from different possible training data sources.

Category Classification Model – Based on training data from sources such as yellow page listings, categorized business web sites, consumer reports information, restaurant guides, query traffic data, and advertisement traffic data. Uses statistics to associate search queries with relevant business categories.

Directory listings – Business information may be taken from yellow page type directory listings, such as those compiled by various phone companies. These listings include business categories as well as business names associated with each of those categories.

Miscellaneous Pre-Classified Business Data – From sources like consumer reports information, restaurant guides, or web-based directory listings. Web pages about a specific business contain words fitting into specific categories which may be used to modify categories that a business appears within. Example: a business listed on a page with words on it about “Italian Restaurants” may be placed in an “Italian Restaurant” category.

Query traffic data – Searchers’ selections from searches may be used by the classification component to classify businesses when their query terms are ambiguous. Example: someone searches for “films” and they receive business listings from a “theater” category and a “photographic film” category. If they select listings from the “photographic film” category, the classification component may modify the probability that query shows “photographic film” category results.

Advertisement accompanying a boats Miami search

Advertisement traffic data – When a searcher selects a displayed advertisement, that may indicate that the advertisement was relevant to the search query. The search query and the category of the selected advertisement may be considered training data that can be used to modify or initially train the category classification model in a manner similar to the training performed for query traffic data.

Scoring local search results based on location prominence

Location Prominence – A system may identify a first document associated with a geographic location within a geographical area and identify a second document associated with a geographic location outside the geographical area. The system may also assign a first score to the first document based on a first scoring function and assign a second score to the second document based on a second and possibly different scoring function. These scoring functions can be related to distances, and to other scoring factors. Location prominence may refer to a score generated for a document based on one or more factors unrelated to the geographical area with which the document is associated, the searches performed by users, and/or the search queries provided by the users. Location prominence may use factors that are intended to convey the “best” documents for the geographical area rather than documents based solely on their distance from a particular location within the geographical area.

The location prominence score may be based on a set of factors that are unrelated to the geographical area over which the user is searching. This set may include one or more (a combination) of the following factors:

  1. A score associated with an authoritative document;
  2. The total number of documents referring to a business associated with the document;
  3. The highest score of documents referring to the business;
  4. The number of documents with reviews of the business;
  5. The number of information documents that mention the business (such as Dine.com, Citysearch, and Zagat.com); and,
  6. The set of factors may include additional or different factors.

These factors may possibly be combined with distance scores in some instances.

Map segment showing a centerpoint for Washington, DC, near the Whitehouse.

Centerpoint – When scoring local search results, the search engine may identify a location within the geographical area. It may be associated with the location of city hall, downtown, or a geographic center of the area, or based upon some other centerpoint using geographic information. The local search engine identifies all business listings and/or web pages within a predetermined radius of the identified location. The local search engine may then identify those business listings and/or web pages that match the search query. The identified business listings and/or web pages are assigned distance scores according to their distance from the identified location and ranked based on their scores.

Postal Codes – A geographical area might be identified by set of postal codes allocated to the geographical area, determine a postal code associated with a document, determine whether the postal code is included in the set of postal codes associated with the geographical area, score the document based on a first scoring function when the postal code is included in the set of postal codes associated with the geographical area, and score the document based on a second scoring function when the postal code is not included in the set of postal codes allocated to the geographical area.

Latitude and longitude coordinates – A geographic area might be identified by latitude and longitude coordinates associated with the geographical area, determine a latitude and longitude coordinate associated with a document, determine whether the latitude and longitude coordinate is included in the set of latitude and longitude coordinates associated with the geographical area, score the document based on a first scoring function when the latitude and longitude coordinate is included in the set of latitude and longitude coordinates associated with the geographical area, and score the document based on a second scoring function when the latitude and longitude coordinate is not included in the set of latitude and longitude coordinates associated with the geographical area.

Combination Scores – A score may be assigned to a document based on a combination of two or more of a score associated with another document that is identified as authoritative for the document, a total number of documents referring to a business associated with the document, a highest score associated with the documents referring to the business, a total number of documents with reviews of the business, or a number of information documents that mention the business, and using the score to rank the document.

Broad Area – May be identified as being associated with the search query. Is intended to refer to any geographic location that is specified as an incomplete postal address (i.e., less than a full postal address). Any geographic location that is identified by less than a street name and street number can be considered a broad area. A broad area may include a city, a zip code, a street, a city block, a state, a country, a district, a county, a metropolitan area, a large area (e.g., Lake Tahoe area), a combination of areas (e.g., Sunnyvale and Mountain View), etc. When a search query includes information regarding a geographical area, then the broad area may be identified from the search query.

Zcodes – If a search query includes the phrase “Mountain View,” then the broad area may be identified as “Mountain View.” A set of “zcodes” may be identified that correspond to the broad area. These could be postal codes, such as a U.S. Postal Service zip code in the United States, or something similar to a zip code outside the United States. A set of zcodes corresponding to a broad area may include those zip codes that have been allocated to the geographical area associated with the broad area.

Example:

For the Mountain View example above, assume that the set of zcodes includes the zip codes 94039, 94040, 94041, 94042, and 94043. To compress space, the zcode sets may be stored as a series of ranges. In the case of Mountain View, the zcode set may be stored as 94039:5, which corresponds to the zip code range of 94039 to 94043. If a zip code is unallocated to any other broad area, then it may be added to the range of a surrounding or adjacent zcode set. For example, if the zip code 94044 is unallocated, then it may be added to the Mountain View zcode set.

Top and left sides of map, with zoom and direction slider showing.  Boundaries of maps can be latitude and longitude coordinates.

Map Boundaries – The entire visible map area within a map window. If a search query doesn’t include information regarding a geographical area, then the broad area may be identified in another way. If the user is accessing a map, the entire visible map area within the map window may be considered the broad area. So, a search for a business type or category while looking at Google maps might use a broad area associated with the map. As the user zooms in or out on the map, or moves the map left or right, and/or provides an identifier relating to a geographical area of interest, the broad area is within the map window. The latitude and longitude of the map window may define the broad area.

Search Area – Associated with the broad area, a location within the broad area may be determined. This location may be associated with the location of city hall, a downtown area, a geographic center, or some other location within the broad area. A circle with a predetermined radius (e.g., 30 miles, 45 miles, 90 miles, etc.) may effectively be drawn around this location. The area of this circle may constitute the search area.

Relevant Documents – A relevant set of identified documents may be determined based on the search query, possibly based upon whether the documents that contain the term(s) of the search query in their title, content, and/or category string. When the query includes multiple terms, documents that contain the terms as a phrase, include all of the terms, but not necessarily together, contain less than all of the terms, or synonyms of the terms may be included in the relevant set.

Broad Area Relevant Documents – A determination is made for documents in the relevant set as to whether they fall within the broad area. If they do not, then a distance score may be calculated for those documents. The distance score associated with a document may be determined based on the distance the postal address and/or the latitude and longitude coordinate associated with the document is from the location within the broad area (e.g., the location representing the middle of the search area). If the document is within the broad area, then a location prominence score associated with the document may be determined.

Additional Scoring Factors – In addition to the scoring factors above for location prominence, it’s possible for other scoring factors to be used also. Examples:

  • Numeric scores of the reviews (e.g., how many stars or thumbs up/down),
  • Some function (e.g., an average) of all the scores of the reviews,
  • Type of document containing the review (e.g., a restaurant blog, Zagat.com, Citysearch, or Michelin),
  • Types of language used in the reviews (e.g., noisy, friendly,dirty, best),
  • 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,
  • Financial data about the businesses, such as the annual revenue associated with the business and/or how many employees the business has,
  • Number of years the business has been around or how long the business has been in the various listings, and;
  • Others.

Methods and systems for endorsing local search results

Local Search Endorsements – Users associated with each other in a social network can create and share personalized lists of local search results and/or advertisements through their endorsements of local search results and/or ads. Those endorsements can be used to personalize the search engine’s ranking of local search results by letting users re-rank results for the people endorsing them, and for the people who trust those endorsers.

Local Search Endorsement Entries – Entries made in a social network including information associated with an endorsed local article. These can include a particular local search query, one or more article identifiers for local articles and/or ads that the user has endorsed for the local search query, and the kind of endorsement for each of the endorsed local endorsed articles and/or ads.

Methods and systems for improving a search ranking using location awareness

Location Awareness – Uses some combination of location score and topical score to order documents related to a query to improve search rankings for that query. It may also include selecting a set of documents from the group of documents, determining a distance score for each document in the set of documents using a document location associated with the document and the location associated with the query, and ordering the set of documents as a function of both the topical scores of the set of documents and the distance scores of the set of documents.

Location Sensitivity – A location component may analyze the query to determine a keyword, or a query topic. A location sensitivity of the identified topic or query is determined. Some topics are location sensitive, and some aren’t. Different topics, query types, users, geographic locales, etc. may influence a different determination of location sensitivity. The amount or extent to which geographically-based search results are relevant to the topic and a relevant geographic range for the topic may be decided by examining such things as user behavior (e.g., user selection behavior, such as mouseover or click through) of search results presented to the user. Examples of location sensitivity:

Topic: A topic, such as “pizza,” may be strongly associated with local documents or web pages (high location sensitivity), whereas a topic like “travel plans” may be less location sensitive.

Scale of default map on a search for pizza in Newark, Delaware

Scale of default map on Search for pizza in Newark, Delaware.

Scale of default map on a search for travel plans in Newark, Delaware

Scale of default map on search for travel plans in Newark, Delaware

Query Types: Certain query types (e.g., commercial queries) may have different location sensitivity.

User Specific: Some users may specify a more local focus for their desired search results than other users, or may be determined to have a more local focus based, at least in part, on browsing history, search history, or transactional or other kinds of available data.

Location differences: One location, such as Manhattan, N.Y., might be more location sensitive compared to another geographic area, such as Camas County, Idaho (the most sparsely populated county in Idaho).

Specificity of Query: The specificity of a location term provided or inferred (e.g., a location specified by a user or a search query), such as a zip code versus a city versus a street address, may affect location sensitivity, as would information, such as a user specified maximum distance (“I’m willing to travel 30 miles to . . . “).

Example:

When a user types in search queries, such as “infinity auto” and “pizza,” a location component may determine associated topics of “car/automobile” and “restaurant.” The location component may determine the sensitivity of the topics “car/automobile” and “restaurant” to location-based search results. It may determine that users are generally more location sensitive for the topic “pizza” than for the topic “automobiles/cars,” so that users may generally be interested in documents on the topic of “automobiles/cars” that are farther away from their location, whereas users may generally only be interested in documents on the topic of “pizza” that are nearer to their location. Location sensitivity can be determined relatively, or can also be mapped to a distance (e.g., users are generally interested in documents with a distance of up to 50 miles for “automobiles/cars,” but only 5 miles for “pizza”).

Document Identification – The search engine looks for previously indexed relevant documents in a search database in response to a query. This document data can include a universal resource locator (URL) that provides a link to a document, web page, or to a location from which a document or web page can be retrieved or otherwise accessed by the user, data indicating one or more locations with which documents are associated, and data corresponding to the text of the documents.

Topic Score – Various information retrieval and other techniques used by conventional search engines are used to determine the relevance of a document, such as text information, link information and link structure, personalized information, etc. This topical score is generated from various sources and signals other than location information. A topic score is also used to find advertisements relevant to a target document.

Locations of pizza shops around a centerpoint, mostly based upon a distance score.

Distance Score – One or more locations is determined to be associated with each of the identified documents, and a distance score is calculated for each based, at least in part, on the distance between the location(s) associated with the document and the location associated with the search query. This distance could be based upon such things as:

  • straight-line distance
  • Driving Distance
  • Estimated Driving Time

Combined relevance score – The topical scores and distance scores could be merged to yield a combined relevance score for a document. The combined relevance score may result in different ranking orders than if documents were ranked by relevance to a topic or by distance alone. How the patent application describes this:

In one embodiment of the invention, because the combined relevance score C considers both the topical score R and the distance score F of a document, it may be possible that the ordering of documents according to combined relevance scores C yields a different order than if the documents are ordered according to topical scores R or according to distance scores F. For example, consider three documents: document A, document B, and document C. Assume that document A has a topical score R1, a distance score F1, and a corresponding relevance score C1; document B has a topical score R2 (where R2>R1), a distance score F2 (where F2R1), a distance score F3 (where F3

Location extraction

Location Extraction – During a Web search, the search terms may indicate the name of a geographic area, and a local search might be done when that geographic area is unambiguous enough.

Ambiguous Search Query – The names of some geographic areas correspond to common words (e.g., Mobile), and it can be hard to tell if a searcher was referring to a location in their search.

Unambiguous Search Query – A user provided query clearly shows an intent for local search documents. A geographic reference may not be completely unambiguous if it is hard to tell which geographic location was being requested, as may happen in a search which includes a City name, but there may be more than one City with the same name.

Results shown when City name is ambiguous

Unambiguous City – If there are two cities with the same names in different states, this process may decide that the one with the largest population should be labeled as an unambiguous city. (The same may be done with counties.) Alternatively, a look at the searcher’s IP address may inform the search engine of which city was the one used in a query. Sometimes a searcher will be asked to choose which state they meant.

Blacklist – A blacklist may be maintained for unambiguous city names that, when combined with one or more other words, mean something other than their respective cities. For example, assume that the city of Orlando, Florida is an unambiguous city. When Orlando appears in a search query with the word Bloom, however, the user likely desires information associated with the actor “Orlando Bloom” and not information concerning flower shops in the city of Orlando. If the city name together with one or more other search terms of the query appear on the blacklist, then a regular web search may be performed based on the search term(s) of the query.

Authoritative document identification

Authoritative Document – The identification of a document or web page (URL) that is associated with a business at a location. This system determines documents that are associated with a location, identifies a group of signals associated with each of the documents, and determines authoritativeness of the documents for the location based on the signals.

Candidate Documents – Documents associated with a particular location, they may be analyzed to identify snippets of text (where a snippet of text may be defined as a portion of a document or the entire document) that include information associated with the location, such as a full or partial address of the location, a full or partial telephone number associated with the location, and/or a full or partial name of a business associated with the location. Links from these may point to the authoritative document. Other signals may be viewed to determine which candidate document is the authoritative document amongst the group of candidates, such as domain names, business name used in anchor text, etc.

Document segmentation based on visual gaps

Document Segmentation – A document may be segmented based on a visual model of the document. The visual model is determined according to an amount of visual white space or gaps that are in the document. The visual model is used to identify a hierarchical structure of the document, which may then be used to segment the document.

Listings on a Web page, with addresses, and visual gaps between them.

Geographic Signals – Information related to a geographic locations, such as full or partial mailing address or telephone number, or name of a business. A page may be filled with different geographical signals, which are segmented from each other by visual gaps. Example: a web page may include a list of restaurants in a particular neighborhood and a short synopsis or review of each restaurant. Or, a page may be filled with multiple reviews of the same restaurant, and segmentation may be used to separate those.

Indexing documents according to geographical relevance

Indexing by Geographical Relevance – Indexing documents relevant to a geographical area by indexing, for each document, multiple location identifiers that collectively define an aggregate geographic region. When creating the index, the search engine may determine a set of geographical areas surrounding a geographical area relevant to a document and associate references to the set of geographical areas with the document index.

Geographical Regions – With some local search engines, the local geographic region of interest is a region defined by a certain distance or radius from a starting location, such as a certain number of miles from a zip code or street address. Ideally, the local search engine should efficiently locate and return relevant results in the desired geographic region.

Location Identifiers – Documents in a database may each be associated with a geographical region. The region may be specified by a location identifier associated with the document. Location identifiers might be derived from a model of the Earth’s surface using a hierarchical grid, such as the well known Hierarchical Triangular Mesh (HTM) model.

Geographically Relevant Documents – Any document that, in some manner, has been determined to have particular relevance to a geographical location. Business listings, such as yellow page listings, for example, may each be considered to be a geographically relevant document that is relevant to the geographic region defined by the address of the business. Other documents, such as web pages, may also have particular geographical relevance. Example: a business may have a home page, may be the subject of a document that comments on or reviews the business, or may be mentioned by a web page that in some other way relates to the business. The particular geographic location for which a document is associated may be determined from postal address or from other geographic signals.

Aggregate Geographic Region – A local search engine efficiently indexes documents relevant to a geographical area by indexing, for each document, multiple location identifiers that collectively define an aggregate geographic region. When the index is used to respond to individual search queries, the aggregate geographic region may be efficiently searched by merely adding a location identifier to the search query.

Classification of ambiguous geographic references

Ambiguous Geographic References – Partial geographic information is associated with a document, which makes it difficult to classify as belonging to a specific geographical location.

Geo-Relevance Profile – A geographic location may be associated with a string of text in a document by looking at a geo-relevance profile that contains that geographic information. A geo-relevance profile is built by looking at a number of documents relating to a business at a specific location.

Known Geographic Signals – A known geographic signal may include, for example, a complete address that unambiguously specifies a geographic location. The geographic signal can be located by, for example, pattern matching techniques that look for sections of text that are in the general form of an address. For example, location classifier engine 100 may look for zip codes as five digit integers located near a state name or state abbreviation and street names as a series of numerals followed by a string that includes a word such as “street,” “st.,” “drive,” etc. In this manner, Location classifier may locate the known geographic signals as sections of text that unambiguously reference geographic addresses.

Known Geographic Regions – Documents that are determined to be associated with valid geographic signals are assumed to be documents that correspond to a known geographic region.

Training Text for Geographical Location Associations – Text selected as training text associated with a document could be chosen a number of ways. Examples: A fixed window (e.g., a 100 term window) around each geographic signal may be selected as the training text. The whole document may be selected. Or, documents with multiple geographic signals may be segmented based on visual breaks in the document and the training text taken from the segments.

Location Identifier Fields – Collected Information based upon types of geographic signals which are filled with text selected for each geographic signal. An example may be zip codes corresponding to the geographic signals.

Zip Codes – Postal codes, which can be used as a geographic signal. They tend to be particularly useful to use as an identifier for a geographic location because zip codes that are close to one another numerically tend to correspond to locations that are close to one another geographically.

Histograms – A way of mapping the occurence of strings in text selections relative to location identifiers for which the terms or phrases occur. The histogram can also be referred to as the geo-relevance profile of the term/phrase. Example: a histogram is created for the bi-gram “capitol hill.” It might include three dominant peaks, a large peak centered in the vicinity of zip code 20515, which corresponds to the “Capitol Hill” area in Washington, D.C., a relatively small peak centered in the vicinity of zip code 95814, which corresponds to the “Capitol Hill” area in Sacramento, Calif., and a moderate peak centered in the vicinity of zip code 98104, which corresponds to the “Capitol Hill” area in Seattle, Wash. While references to “capitol hill,” may involve other places, the histogram illustrates that overall, “capitol hill” tends to be used when referring to one of these three locations. Washington, D.C., which corresponds to the largest peak, can be interpreted as the most likely geographic region intended by a person using the phrase “capitol hill.”

Two examples of histograms showing the number of occurrences of the phrases 'Capitol Hill' and 'Bay Area' relative to different geographic regions.

Statistically Significant Spikes – When it appears that certain terms or phrases may be relevant to a particular geographic location, based upon their proximity to geographic location information while looking at the training text. If certain phrases tend to be tied to certain locations in a way that appears meaningful based upon number of occurences over data collected from the training text, their appearance could be said to be statistically significant.

Local item extraction

Confidence Scores – When a system identifies a document that includes an address and locates business information, that system may assign a confidence score to the business information, where the confidence score relates to a probability that the business information is associated with the address. The system determines whether to associate the business information with the address based on the assigned confidence score.

Local Item Extraction – When looking at a document, attempting to assign a location and assign confidence scores to that document by looking at the business information on the page, at terms that preceed the address to see if any are a business name, and if there are telephone numbers, whether or not the numbers are associated with that business. Landmarks associated with the business may also be identified and assigned a confidence score.

Business Information – A business name (also referred to as a “title”), a telephone number associated with the address, other information related to a business.

Yellow Pages Data – Information commonly associated with a business that is taken from a telecom directory. Some addresses may not have associated yellow pages data or possibly incorrect yellow pages data. Businesses with associated yellow pages data may be used as part of a training set used to extract location information from pages that don’t have associated yellow pages data. The documents in the training set may be analyzed to collect features regarding how to recognize business information in a document when the document includes an address.

Training Set Features – These could include such things as a distance that a candidate term is from a reference point (e.g., the address in the document), characteristics of the candidate term, boundary information associated with the candidate term, and/or punctuation information associated with the candidate term. The particular features that are useful to determine a title may differ from those features that are useful to determine a telephone number. The features may differ still for determining other types of business information.

Landmarks – Information about the location of a business, such as a postal address. This information is tied to attributes of the landmarks such as business name, telephone number, business hours, or a link to a web site or a map) in a document. In other implementations, the above processing may apply to other landmarks and attributes, such as finding the price (attribute) or a product identification number (attribute) associated with a product (landmark).

Assigning geographic location identifiers to web pages

Geographic Location Identifier – may be a partial or complete postal address, telephone number, area code, etc or any other suitable value associated with a physical geographic position, such as longitude and latitude. The geographic location identifier may be based on links, such as hyperlinks, that connect the nodes in the collection of documents – based upon a relevancy of the web documents to each other.

Geographic Relevancy Criteria – Geographic location identifiers included within web pages may be assigned to other web pages that may or may not contain that information, if certain relevancy criteria is in place. This means that web pages that either do not include geographic descriptive information or include unrefined or incomplete geographic location information could be searched or identified based on an assigned geographic location identifier. Document relevancy may be determined based on several factors, such as relative distance between documents, terminology used, and local or web site determination. Example: a home page for a Web site doesn’t contain any address information, but the site has that information on an “About us” page, a “contact page,” and a “directions” page – if certain critieria as defined in the patent application is met, then the home page is seen by the search engine as being relevant for the address information on those other pages.

Forward or Outbound Link – A link originating from a first page and leading to a second page may be called a forward or outbound link relative to the first page and indicate that the first page is a linking document.

Backlink – A link from a first page to a second page may be characterized as a backlink from the second page to the first page. A link originating from the second page and leading to the first page may be called an inbound link relative to the first page and indicate that the first page is a linked document.

Notes:

I’ve organized the items in the glossary by patent filing because their use may be limited to the context of the document they are found within. There is some cross over on a number of these documents, so that when for instance, the patent application on “Location Prominence” refers to “Authority Documents,” it’s clear that they are referring to the definition in the patent application on “Authoritative Documents.”

More glossary items will be added from Google’s patent documents on digital maps, driving directions, and out of home advertising, and from Google’s Help and FAQs on Local Search. If you have any suggestions or questions, please contact me or comment below.

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68 thoughts on “Google Local Search Glossary”

  1. Bill,
    What a wonderful start you’ve made on this. You really worked hard on this! This will be a document we can all reference repeatedly to explain some of these concepts and I, for one, am truly grateful for your efforts to gather all of this information together in one place for easy reading.
    Miriam

  2. Thank you, Miriam.

    I’m hoping that this glossary can give us a shared vocabulary and make it a little easier to talk about local search. I’ll probably add to this over the next few weeks and months, and simplify some of these entries a little more.

  3. Hi Bill,
    One thing that might make this even more useful would be pictoral representations of some of the features you are describing. In other words, little screen shots of some of the types of information that Local Search can include in listings that are being pulled from other sources. I don’t know if you want to get into doing that, but I know that for me seeing an image really cements what I’m reading about. Just a thought!
    Miriam

  4. Bill, this is really terrific. Can’t believe you’re saying it’s just a “start” — what a great start!

    I like Miriam’s image idea, and if I were to make one other suggestion, it would be this: Whenever there’s legal/patent jargon, rewriting it to be more readable would help. :-) Or, if not rewriting it (because I see the value in using the exact language), maybe add an “In other words” section after any entries that need one. It would help us common folk.

    Great stuff!

  5. Thanks, Matt.

    I’m going to be polishing this up, and simplifying it as much as I can, hopefully without simplifying it too much.

    I have ten images in there now, and I’ll probably add some more to try to show some of the concepts covered in a visual manner.

    Appreciate the comments and suggestions.

  6. Bill-

    Nice Work! I really like the structure as it has the added benefit of acting as a crib sheet for the local patents as well.

    At the next conference you need to give everyone a quiz. :)

    Mike

  7. Fantastic – absolutely fantastic! I just sent out a bid for
    a Local Search Optimization project. This Glossary is going straight into my files. Thanks, Bill! Charles Knight SEO

  8. Wow… very comprehensive, thanks. Let me check if you missed one… mmhh.. probably not :)

  9. Thanks, Charles and Carsten.

    I’m probably going to have a couple more parts to this glossary in the future, so keep an eye out for those. There are a series of patent applications that focus more on the Maps themselves, and another that talk about driving directions and out-doors/display type advertising.

    There are also some older patents and patent applications that I probably need to address to make this more complete. Most of the ones that I’ve written about so far seem to fit together better than those other ones, so I left them out for now.

    The real difficulty with these is trying to understand how knowing about this stuff might be helpful when you try to make your site do well within a local search.

    Here’s a look at that using some of this information:

    Imagine that I have a business that sells cars in Newark, Delaware. Do I focus on the use of the word “automobiles” or “cars” or both in my pages? A local search in the area for “car dealerships” shows a good number located nearby. A local search in the area for “automobiles” doesn’t show all of those dealerships, and covers a much wider area. Which of those two searches do I want to be more likely to be included within? How can I be included in both?

    I think understanding the concept of location sensitivity related to the two different terms makes it easier to understand why they are treated so differently in local search results.

  10. I’ve only skimmed this so far….but my experience is that Bill’s reviews of patents are invaluable.

    Bill did a review in summer 2005 for a G patent that was just issued. It absolutely described changes they made in February of that year that dramatically changed and improved the logic for long tail geo searches in Google at that time.

    It didn’t describe the ranking algo…but did describe how to structure the site to be visable for searches that were local in nature.

    As Bill wrote above, reviewing this writing and others enables someone to get a sense of what is being applied and what isn’t from among the patents.

    As it regards Bills question above–how to get seen or found if you are selling cars/automobiles within Newark Deleware…Knowing all the ins and outs of different applications (ie Google maps and google search) knowing the patent applications–, assessing what is applied and not applied and then working with a large variety of keywords, keyword phrases, various sources, etc. can do a good job.

    But I wouldn’t just stick to G maps applications or G search….I’d work both very vigorously. I’d also work vertical/topical markets and local markets to get web visibility every which way!

    Kudo’s Bill. You are revealing many mysteries within an organized context that most of us wouldn’t know a thing about if not for your efforts.

    Thanks

    Dave

  11. Thank you, Dave.

    But I wouldn’t just stick to G maps applications or G search….I’d work both very vigorously. I’d also work vertical/topical markets and local markets to get web visibility every which way!

    Agree absolutely.

  12. Hi Again, Bill,
    Can I ask you to verify if I am understanding Location Prominence and Location Sensitivity correctly. You mentioned these two items in your comment on my blog, and made me want to be sure I’m really clear on these two as they seem quite important. Please, would you let me know if this is right:

    Location Prominence – Here the word ‘promininence’ appears to be most operative, in that a document could be valued based upon how many links or mentions it is getting on other documents, regardless of the location. Because of this, a person searching for, let’s say, organic farms in the SF Bay Area might be shown a choice of farms. However, Google might rank a farm that is 10 miles away higher than one that is 3 miles away, because that farm is referenced in more exterior documents such as Yelp, Yellow Pages, SF Chronicle, etc. Do I have this right?

    Location Sensitivity – Here, I believe you are saying that Google might determine that a user would be willing to travel farther for certain types of items. So, in the pizza/automobile example you are giving, one might assume that a person would be willing to drive around a bit to find the perfect car, as opposed to wanting to get a pizza as quickly as possible. However, with the car scenario, if Google Local is presenting a variety of results for this, and let’s say all the car dealerships are located between 5-10 miles from the query location, would what is closest then rank higher, or would Google then fall back on location prominence to determine which of the dealerships is better by virtue of exterior references? Am I understanding Location Sensitivity correctly, even in a simplified way?

    I hope it isn’t burdensome to have me repeating back to you what you are explaining here in the glossary. I tend to need to say things back in my own words to ask if I’m properly understanding what is being said to me. I’m so eager to become more educated about this, Bill.
    Thanks!
    Miriam

  13. The questions are appreciated, Miriam.

    If you’re asking, other people probably are too. And in answering you, I may find the right words to simplify the entry in the glossary above, or make it a little more clear.

    You’re close with both.

    Location prominence means that there are non-geographic factors that make one business more “prominent” in an area than others. This may mean that it has better reviews, or more links to its web site, or more mentions on web pages, in directories, and reviews. Where it gets confusing is that location prominence factors may be used for businesses inside of a defined broad area (or the search area within that broad area), and not used for businesses outside of that area. (Geographic-based factors may also be used in combination with the location prominence scores for businesses within the area.)

    For businesses outside of the area, just geographic based scores (such as distance scores) may be used.

    The effect of location sensitivity is to make the broad area used different sizes. Remember that location prominence factors are looked at for businesses within a “broad area.” So location sensitivity determines the range of distance that businesses will be rated either with location prominence factors or just geographic factors.

    The range (or scale on a map) can differ based upon the:

    (1) topic of the query (closer for pizza and further for travel planners),

    (2) the intent (commercial or noncommercial) of searches (closer for shoes and further for tourist attractions),

    (3) personalized user specific factors (someone who travels and searches over a greater distance might be shown maps with bigger broad/search areas),

    (4) location differences so that they would maybe be closer for urban, more spread out for suburban, and then maybe very narrow for rural – with only one result (a search in New York City might show 10 pizza places within two miles of each other. A search in (suburban) Bear, Delaware may show ten pizza places within an eight mile radius. A search for pizza in Camas County, Idaho may just show one pizza place because the others are over 20 miles away, and it’s unlikely that someone will drive that far for a slice.

    (5) How specific the location information is in the query – a full address would be pretty specific and show a small area. A zip code would show a bigger area. A search with just a City and State name might show an even larger area, especially of the City had more than one zip code. A county search might show an even wider area.

    So, location sensitivity = radius of search area based upon who is searching and what they are looking for, and how they ask for it.

    Location prominence = ranking factors unrelated to geography within the search area determined by location sensitivity.

    The two different concepts work together

  14. A-ha!
    That really did help. I think I get it!

    This makes good sense to me:

    “So, location sensitivity = radius of search area based upon who is searching and what
    they are looking for, and how they ask for it.

    Location prominence = ranking factors unrelated to geography within the search area
    determined by location sensitivity.”

    It must be hard to figure out exactly how to word this, Bill, but you have just done a great job with this. For me, the examples of the “so a guy looking for pizza” somehow bring the language onto a platform that suddenly becomes very real and clear because I can envision a person’s activities. That helps me.

    Thank you, so much, for your kindness in explaining this so thoroughly.
    Miriam

  15. Bill: I finally read this in its entirety and reread it quite a bit as reading through patent language and even Bill’s descriptions of patent language isn’t easy for me.

    Incredible job, Bill. It should be must reading.

    It appears that the G Maps algo’s are potentially quite complex with many potential factors impacting if a map even shows, if businesses fall within the maps descriptions and how to rank.

    Reading through this made me painfully aware that I haven’t stayed abreast of this situation.

    What struck me that amongst potential algo impacts were aspects based on G reading and evaluating personal behavior (personalization) advertising on G PPC, and some other criteria to which I hadn’t been paying attention.

    As Bill said in the beginning an important point about all this is to try and ascertain, measure and study how many and which of the various elements Google is actually implementing.

    As mentioned above I’ve seen the G maps ranking algo’s change. I suspect they will continue to change as they refine the many potential impacts and adjust weights for various queries. They certainly have the capacity to continuously evaluate results.

    Bravo, kudus, and thanks, Bill!!!!

    Dave

  16. Thanks, Kimber.

    I’m going to try to find some more time, to try to simplify some of the text in the glossary if I can. Some of it just doesn’t get any simpler though, no matter how hard I try. :(

    Thanks for your comments.

  17. Cheers Bill! This is incredibly helpful! It must be the ultimate collection of local search terms ever!

  18. Thanks. :)

    I’ve been meaning to come back and add a couple of additional terms. I’m sure that there are some things that I’ve missed. And some more pictures.

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  20. Bill, you’ve got to put these extensive posts of yours into a book one day. I mean, there’s just so much here, and the information is quite invaluable. I suggest looking at lulu or one of the self-publishing entities on the web there is out there. A lot of guys are putting this kind of thing into a book, and I’m sure you would get a great response if you did the same thing. Good luck. Excellent post again, I’m going to have to read it again and take notes!

  21. I’ve only skimmed this so far….but my experience is that Bill’s reviews of patents are invaluable.

    Bill did a review in summer 2005 for a G patent that was just issued. It absolutely described changes they made in February of that year that dramatically changed and improved the logic for long tail geo searches in Google at that time.

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  27. More than two years after posting this article remains beneficial for those of us with “brick and mortar” businesses. I’m on the phone with my webmaster now discussing some of the ideas you raised.

    Thanks!

  28. Hi Rey,

    Happy to hear that you found this useful. The local search patents from Google contain a lot of interesting material, and I’ve spent a fair amount of time looking at actual local search results with them in mind. You’re welcome.

  29. Hi Aaron,

    Yes,there are meta tags that are useful for geo-targeting. I’ve looked at a number of them. Unfortunately, none of the major search engines use them except possibly Bing, so their value may be limited.

  30. It might also be worth mentioning that there are meta tags in existence now that can be used for geotagging your site, which may help with local searches, especially if your business is a local brick-and-mortar store.

  31. I agree with an earlier commenter about this information still having current value even after two+ years.

    I was going to hit the back button on my browser because of the age of the article, but then I decided to read just a little bit of it… then a little bit more… then a little bit more, and soon I discovered I’d read the whole thing with great interest.

    I’m helping a friend who owns a brick and mortar business and some of the insights revealed here are going to be very helpful.

    Thanks Bill

  32. Hi Mike,

    Thanks. I do think that a lot of the ideas behind the patent filings I pulled terms from still have a fair amount of value in describing aspects of how Google Maps works. Good luck in your efforts with your friend’s business.

  33. Bill, this is an amazing one stop resource about Google local search! You have explained all terms is such detail and this will definitely be a key resource for any beginner or avid practitioner in helping them find all the valuable information in one place.

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  35. 4 years ago after you wrote this article, up to now its still a great contribution and a guide to us to understand those words and to know more better about Google terms . What a wonderful collection you have! Thanks Mr. Bill

  36. Bill as usual I have stumbled across a fantastic post of yours. I see this is a fairly old article. Has anything changed recently as far as ranking factors go with placement on Google Places? What in your estimation are the 5 top keys to getting the best placement at the moment?

  37. Hi Bill,

    Thanks. Many of the things that help someone rank well in Google Place results are still the same today, such as relevance between the query and the name of the place/categories, distance from a map’s centerpoint, and location prominence. Google came out with a video earlier this year which called those the three most important aspects of ranking for Google Places.

    I’m looking at another patent right now which I think is pretty interesting, and may write a post about it sometime soon. It involves the use of synonyms when it comes to queries and rankings of local search results.

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