A recovered binary exposing part of Geostore, the system Google uses to represent geographic objects, sheds new light on how Google Maps actually works. The recovered material covers 72 Geostore ranking signals, 793 data source providers, 446 local search intent types, 50,998 Mapcore styles, 12,936 label styles, and 10,936 searchable Geostore declarations. The ranking signals get attention, but the architecture around them tells the more important story about how Google understands places.
What Geostore actually is
Google represents geographic objects internally as Features. A Feature can be a business, a building, a road, a city, a station, an area, a transit element, or a 3D object. For an establishment, the object can contain identity, geometry, source information, websites, business-chain relationships, Knowledge Graph references, concepts, and ranking information.
The familiar Maps listing is assembled later. What a business owner edits in Google Business Profile is not necessarily what Google maintains internally as the entity. Google builds a canonical representation of the place that can incorporate data from multiple sources, survive changes in geometry, and connect to other Google identifiers, including the Knowledge Graph machine ID (MID).
For local SEO, the entity is the more useful unit to consider. The listing is the interface. The entity sits underneath it.
How 793 source providers shape a single listing
One of the most revealing parts of Geostore is its provenance system. A business does not simply have one source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else entirely. The corpus exposes 793 source providers, along with mechanisms for provenance, priority, trust, and conflation.
Conflation is the process used when several sources describe the same object and disagree. Geostore contains generic mechanisms that can pick one value, merge several values, or combine them. It also models trust levels ranging from blocked or untrusted sources to trusted and super-trusted ones.
This gives a different interpretation to a common local SEO problem. Changing a field in Google Business Profile does not guarantee that Google’s canonical representation immediately becomes that new value. The edit becomes another piece of evidence entering a system that may already have competing evidence. For businesses struggling with persistent incorrect attributes, duplicate information, or changes that repeatedly revert, this architecture helps explain why the problem can be harder than editing a listing.
How the archive pairs with Google’s 2024 documentation
The binary itself gives structures, field numbers, and complete enumerations. Google’s March 2024 documentation often gives prose explaining what those structures mean. The recovered archive contains 10,936 Geostore declarations that can be searched by message name, package, field type, documentation text, tag number, status, and other properties.
If a signal exists, the archive lets you find its declaration. If a field existed in the 2024 documentation, you can read the associated description. If a field has been stripped from the newer client scope, its protobuf tag still leaves a numbered hole. The 2024 leak gave many descriptions of Google’s systems. The newer binary gives much more of their actual vocabulary. Together, they provide a more useful picture than either source does on its own.
What Oyster Rank’s 72 signals actually measure
Geostore has its own ranking system. Internally, it is called Oyster Rank. A complete visible enumeration of 72 signals was recovered. The list includes Google reviews, web query volume, listing impressions, listing opens, direction requests, website clicks, chain membership, Wikipedia signals, popularity, prominence, landmark information, and road usage.
Out of 72 values, 25 are explicitly marked deprecated. The important limitation is that the signal names were recovered, not their current weights. The schema shows a pipeline in which raw observations are extracted, normalized, and mixed into the Feature’s rank, but the coefficients that would tell how much each signal contributes are outside the recovered scope. SIGNAL_GOOGLE_REVIEWS proves that reviews belong to the Oyster Rank vocabulary. It does not prove that reviews currently carry a particular weight in a Maps search.
Why 72 signals are not the Maps algorithm
Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still has to go through additional systems. Maps must understand what the person means, identify a geographic context, generate candidates, evaluate semantic relevance, and serve a final result set. A simplified pipeline looks like: Geostore entity, query understanding, semantic matching, candidate generation, geography and quality, reranking, results.
There are additional complications. A separate scorer runs entirely offline on the device. It has eight signals across 13 tiers and is distinct from both Oyster Rank and server-side Places ranking. There is no single Maps ranking formula. Different scoring and retrieval systems operate at different stages. Turning the 72 Oyster Rank signals into a checklist of 72 Google Maps ranking factors would miss most of the architecture.
Why local search does not use a fixed radius
A common local SEO model imagines Google looking within a predefined radius around the user and ranking the businesses found inside it. Measurements show something more dynamic. Using the same origin in Paris, the geographic footprint changed considerably depending on the query. A dense query such as “pharmacie” produced a far smaller search area than a brand query such as “Carrefour.” The environment matters too. The same pharmacy query expanded dramatically when run in a sparsely populated rural area.
When geographic weighting was removed from the same engine across 5,083 calls and 86,584 results, the median distance moved from 6.87 km with geography to more than 4,000 km without it. The non-geographic order remained extremely stable. Geography is doing more than reordering the same list of candidates by distance. It changes what the retrieval system considers in the first place. Distance is still fundamental in local SEO, but “I am closer, I should rank higher” is an incomplete model.
How Maps and the web connect through entities
Geostore Features can connect to the Knowledge Graph through a MID. On the web index side, documents can also carry MIDs. Google has a layer called webref that associates documents with entities and stores information including topicality, confidence, geographic metadata, and document-level scores. The relationship also works at the document-ranking level. Recovered structures describe a relative ranking signal between different documents for the same entity, along with properties such as whether a page is an author page, publisher page, or reference page.
This creates a different way to think about a store locator or location page. Its role may extend beyond ranking for queries such as “shoe shop Paris.” The document can become evidence about the underlying entity. The SEO objective is then partly to make it easy for Google to establish which entity the document describes, how much of the document is actually about that entity, how confident that association should be, and whether the document is a useful reference for it.
How Google understands concepts, not just categories
The semantic layer goes considerably beyond the primary category visible on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and other concepts. A simple “ramen” query followed through several parts of the system shows the search results did not all belong to one category. Google connected the query with ramen restaurants, Japanese restaurants, Asian restaurants, and other related concepts.
Inside listings, the semantic representation goes deeper. Review topics, menu dishes, and other attributes can be represented as entities rather than plain strings. Semantic understanding becomes much more important when the interface starts answering complex questions.
What lives on the phone itself
Not everything is calculated on Google’s servers. On-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles were found. One object, ChainAffinity, suggests the system can model affinity toward a recurring retail chain. The broader architecture is clear. The phone itself participates in building geographic context. Personalization in Maps can combine server-side knowledge of the world with a local model of the user’s own geography.
Why ranking still does not guarantee map visibility
Search results are only one of the outputs of Maps. The visual map has another problem to solve. Thousands of potentially relevant entities cannot all receive labels simultaneously. That job belongs partly to Mapcore, which holds 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions. A business can be eligible or highly ranked and still fail to appear as a visible name on the map.
Search ranking and map visibility are separate optimization problems. This distinction becomes especially important when people measure Maps visibility using screenshots or map grids. The visual surface includes a rendering decision after retrieval and ranking have already happened.
Why Gemini sits on top of this stack
Google is rapidly expanding Ask Maps and other AI-powered experiences, but much of the infrastructure required to answer complex questions was already present. The system already has canonical place entities, semantic concepts and attributes, reviews and extracted topics, Knowledge Graph relationships, web evidence, geographic retrieval, behavioral signals, personal geographic context, listing composition, and ranking systems. Gemini adds a conversational interface over these layers. That changes what a local query can be. “Best ramen near me” is relatively easy. “Where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service?” requires a different kind of place representation, and Maps has been building many of those ingredients.
FAQ
What is Geostore?
Geostore is the system Google uses internally to represent geographic objects. It stores businesses, buildings, roads, cities, and other features as canonical entities that can connect to the Knowledge Graph and serve as the foundation for Maps listings.
What is Oyster Rank?
Oyster Rank is the ranking system inside Geostore. A recovered binary exposes 72 signals in Oyster Rank, including reviews, web query volume, listing impressions, direction requests, and popularity, but it does not reveal their current weights.
Does Google Maps use a fixed search radius?
No. Measurements show the geographic footprint changes with the query and the surrounding environment. Removing geographic weighting from the retrieval engine pushed median distances from 6.87 km to more than 4,000 km, indicating geography changes which candidates are considered rather than only reordering them by distance.
Related coverage
This article summarizes reporting from searchengineland.com.
