Without an explicit location term in the query, Google infers a geographic reference point from device or IP-based location, and where available, past location history and account settings, rather than calculating a literal geometric midpoint between nearby population centers. When a user genuinely sits between two major cities, the result set may blend results from both or lean toward whichever market Google’s system has higher confidence is the actual relevant one, based on the strength and precision of the available location signals, not a fixed formula that mathematically averages coordinates between the two cities.
Why this is a confidence problem, not a geometry problem
It’s tempting to imagine Google literally computing the midpoint coordinate between two cities and searching outward from there, but that’s not how the underlying signal works. Google’s location inference for a query without explicit location text is built from the actual location signals available for that specific search: device GPS if granted, IP-based approximate location, and account-level signals like Location History if the user has that enabled and Google has access to it. These signals have varying precision, a precise GPS fix is a very different input than a coarse IP-based geolocation that might place someone in a metro area but not a specific neighborhood, and Google’s system works with whatever precision it actually has for that search, not a theoretical exact position.
For a user genuinely between two cities, this means the outcome depends heavily on which signals are available and how precise they are, not on the user’s literal geographic midpoint. A user with a precise GPS location that happens to sit closer to one city will likely see results leaning toward that city, following the standard distance-based local ranking logic Google’s Business Profile documentation describes (distance from the search’s location reference point is one of the three named local ranking factors, alongside relevance and prominence). A user whose only available signal is a coarse IP-based location covering a wider area might see a more blended result set, or a set that leans toward whichever city Google’s system treats as the more likely default for that coarse signal, which is not something Google has published an exact methodology for.
It’s important to be honest about what isn’t public here: Google has not disclosed the precise weighting logic between multiple ambiguous or lower-confidence location signals, nor a specific tie-breaking rule for exactly-between scenarios. Google’s Business Profile Help documentation on how local results work describes the general inputs (location signals, distance, relevance, prominence) without publishing the exact resolution algorithm for genuinely ambiguous cases. Any claim to know the specific formula should be treated as speculative rather than confirmed.
Why this matters more for some business types than others
The practical stakes of this ambiguity aren’t uniform across business types. A business offering a genuinely local, in-person service, a specific restaurant, a specific retail storefront, is directly affected by which city’s results the searcher’s ambiguous location resolves toward, since the business can only serve people within a practical travel distance and being surfaced in the “wrong” city’s result set for a given searcher provides little practical value even if it happens. A business offering a service-area model, a contractor or service provider genuinely covering both nearby cities, has a different and arguably more favorable relationship to this ambiguity, since being surfaced for searchers resolving toward either city can produce a genuinely relevant result either way, provided the business has accurately configured its service area in its Business Profile to reflect that it does cover both markets.
This distinction argues for different practical responses depending on business type. A single-location storefront genuinely near the boundary between two markets has limited ability to influence which city a given ambiguous search resolves toward, since that resolution depends on the searcher’s own device signals, not anything the business controls; the more productive use of effort is ensuring the business’s own listed location and category data are as precise as possible so that whichever result set a given search resolves into, the business is correctly and competitively represented within it. A multi-market service-area business, by contrast, benefits directly from explicitly configuring its service area to include both cities, since this removes ambiguity about whether the business should be considered relevant to searches resolving toward either market.
Practical implication: don’t assume a single fixed market determines visibility for edge-location users
Recognize that visibility in the “between two cities” zone is inherently less predictable and more signal-dependent than visibility within a clearly-defined single market. A business located in or targeting this kind of geographically ambiguous zone should expect more variability in which searches surface it, since the underlying location signal Google is working from varies in precision and source from one searcher to the next.
Ensure Business Profile location data (address, service area if applicable) is as precise as possible. Since the business’s own location is a fixed, confirmed part of the equation even when the searcher’s implicit location is ambiguous, precise and accurate business location data gives Google’s distance calculation the best available fixed point to work from, regardless of how uncertain the searcher’s side of the equation is.
Don’t rely on assumptions about which of the two nearby cities “counts” as the default market. Since Google hasn’t disclosed a tie-breaking rule, a business shouldn’t build strategy around an assumption that one city is systematically favored; testing actual search visibility from multiple realistic searcher locations within the ambiguous zone gives a more grounded picture than theorizing about Google’s internal logic.
Consider that explicit-location queries bypass this ambiguity entirely. For a business genuinely serving both nearby markets, content and metadata that naturally supports being found via explicit-location queries (a user searching “[service] near [specific city]”) sidesteps the inference-based ambiguity of no-location queries altogether, since an explicit location term gives Google an unambiguous signal to work with regardless of the searcher’s actual device location.
The honest answer is that Google resolves this through a confidence-weighted blend of whatever location signals are actually available for that search, not a disclosed geometric or algorithmic tie-breaking formula between two named cities.
