Understanding how Google Maps ranking signals work gives local businesses a far clearer picture of how to earn visibility, because the listing people see is the surface of a much larger place-data system. A reverse-engineering analysis recovered a non-public scope of Geostore, the system Google uses to represent geographic entities, and cross-referenced it with Maps protocols, network traffic, the web index, mobile services, style tables, on-device components, and Google’s 2024 documentation leak. The recovered material includes 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 signals will draw attention, but the architecture around them explains more about how Google understands places and where local search is heading as Maps becomes a conversational product.
What is the difference between a listing and an entity?
A useful mental model starts with Geostore. Google represents geographic objects internally as Features. A Feature can be a business, building, road, city, station, area, 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 work with. The listing is the interface, and the entity sits underneath it.
How does Google combine data from 793 providers?
One of the most revealing parts of Geostore is its provenance system. A business does not have a single source. Its name might come from one provider, its phone number from another, its category from another, and its geometry from somewhere else. 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 up to trusted and super-trusted ones.
This reframes a common local SEO issue: 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 hold competing evidence. For businesses dealing with persistent incorrect attributes, duplicate information, or changes that keep reverting, this architecture helps explain why the fix can be harder than editing a listing.
What are the 72 Oyster Rank signals?
Geostore has its own ranking system, internally called Oyster Rank. The analysis recovered a complete visible enumeration of 72 signals, including:
- Google reviews
- Web query volume
- Listing impressions
- Listing opens
- Direction requests
- Website clicks
- Chain membership
- Wikipedia signals
- Popularity
- Prominence
- Landmark information
- Road usage
Out of 72 values, 25 are explicitly marked deprecated. The important limitation is that the analysis recovered the signal names, 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 show how much each signal contributes fall outside the recovered scope. The presence of a reviews signal proves reviews belong to the Oyster Rank vocabulary. It does not prove reviews currently carry a particular weight in a Maps search.
Are the 72 signals the full Google Maps algorithm?
No, and this is the key clarification. Oyster Rank appears to characterize the importance of the entity inside Geostore. A user query still passes 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 more like: Geostore entity, then query understanding, then semantic matching, then candidate generation, then geography and quality, then reranking, then results.
The analysis also found a separate scorer running entirely offline on the device, with eight signals across 13 tiers, 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, so treating the 72 Oyster Rank signals as a checklist of 72 Maps ranking factors would miss most of the architecture.
Does local search use a fixed radius?
Direct testing of the geographic layer shows something more dynamic than a predefined radius. Using the same origin, 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. Google appears to adapt the candidate space to both the query and what exists around the user.
Removing geographic weighting from the same engine, across 5,083 calls and 86,584 results, moved the median distance from 6.87 km with geography to more than 4,000 km without it. The non-geographic order stayed extremely stable, which suggests geography does more than reorder the same candidates by distance. It changes what the retrieval system considers in the first place. Distance remains fundamental, but "I am closer, so I should rank higher" is an incomplete model.
How are Maps and the web connected?
Geostore Features can connect to the Knowledge Graph through a MID, and documents in the web index 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 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 changes how to think about a store locator or location page. Its role can extend beyond ranking for a query like "shoe shop." The document can become evidence about the underlying entity. The 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. Web SEO and local SEO are much less separate inside Google’s infrastructure than their interfaces suggest.
How does Google understand concepts instead of categories?
The semantic layer goes well beyond the primary category on a listing. Google uses GConcepts, a shared conceptual vocabulary that can describe businesses, dishes, attributes, cuisines, service modes, and more. Following a simple "ramen" query through the system, the 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 representation goes deeper. Review topics, menu dishes, and other attributes can be stored as entities rather than plain strings. For an AI system this is valuable. Instead of rereading thousands of reviews each time someone asks about wait times or good ramen, Google can work from structured themes, entities, and precomputed signals already attached to the place. Semantic understanding becomes more important as the interface starts answering complex questions.
What geographic intelligence lives on the phone?
Not everything is calculated on Google’s servers. The analysis found on-device structures associated with visits, place candidates, frequent places, trips, home and work, mobility patterns, and user location profiles. One object, ChainAffinity, suggests the system can model affinity toward a recurring retail chain, alongside the separate offline scorer. The evidence level varies between components: some structures are explicitly named in the recovered schema, while parts of the persona layer can only be reconstructed from compiled structures. The broader point is clear. The phone participates in building geographic context, so personalization can combine server-side knowledge of the world with a local model of the user’s own geography.
Why can a ranked business still miss the map?
Search results are only one output of Maps. The visual map has to solve another problem, because thousands of relevant entities cannot all receive labels at once. That job belongs partly to Mapcore. The analysis recovered 50,998 Mapcore styles and 12,936 label styles. Label visibility can change with zoom and other rendering conditions, so 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, which matters when people measure Maps visibility using screenshots or map grids, since the visual surface reflects a rendering decision made after retrieval and ranking.
How does Gemini fit on top?
Google is expanding Ask Maps and other AI-powered experiences, and much of the infrastructure needed to answer complex questions was already present. The system already holds 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, which changes what a local query can be. "Best ramen near me" is relatively easy. A request such as "where can six people eat near my hotel tonight, with one vegetarian, little waiting time, and good recent feedback about service" requires a richer place representation. Google needs to know what a restaurant is, what it serves, when it is open, what people say about it, where it sits relative to the user’s route, and whether the evidence is reliable enough to recommend it. Maps has been assembling those ingredients for years.
FAQ
How many ranking signals does Google Maps use in Oyster Rank?
The recovered enumeration lists 72 Oyster Rank signals, of which 25 are explicitly marked deprecated. The names were recovered, but their current weights were not, so the list shows the vocabulary rather than how much each signal counts in a live search.
Why do edits in Google Business Profile sometimes revert?
Google combines data from 793 source providers and uses conflation to resolve conflicts, with trust levels ranging from blocked or untrusted to super-trusted. An edit becomes one more piece of evidence entering a system that may already hold competing evidence, so the canonical entity does not always update to match the edit.
Does Google Maps rank businesses within a fixed distance radius?
No. Testing showed the search footprint changes with the query and the surroundings. A dense query produced a smaller area than a brand query, and a query expanded in a rural area. Removing geographic weighting moved the median distance from 6.87 km to more than 4,000 km across 5,083 calls and 86,584 results, showing geography changes which candidates are considered, not just their order.
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This article summarizes reporting from searchengineland.com. See our editorial disclaimer for how our articles are produced.
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