Local pack results are generated around a specific geographic point, not a city boundary. When someone searches “plumber in Austin,” Google isn’t drawing a polygon around the Austin city limits and filtering businesses inside it. It’s calculating distance from a search point, typically the centroid of the named location or a more specific point implied by the query, and then weighing that distance against relevance and prominence. A business physically located just across a city line, but closer to that calculated point than a business technically inside the city, can outrank the “in-city” business. This is the single most common reason results look geographically “wrong” to someone who assumes city text match is the deciding factor.
Why local pack distance is measured from a point, not a city boundary
Google’s own documentation on local ranking (support.google.com/business/answer/7091) names three factors: relevance, distance, and prominence. Distance is explicitly described as being calculated based on how far each potential result is from the location implied in the search, not from a municipal boundary. City names function as a modifier that helps Google infer a location point and, importantly, also function as a relevance signal in their own right, since a business whose name, categories, description, or on-site content genuinely reference that city can score higher on relevance even without being the closest option.
This is why the interplay between the three factors produces results that look inconsistent if you’re mentally modeling this as a boundary-based filter. A business two miles outside the named city, with strong category alignment, a high review count, and consistent citations, can beat a business a half-mile inside the city that has thin GBP data, no reviews, and a generic category. None of the three factors guarantees a ranking outcome on its own, and Google has not published a weighting formula that would let you predict, for a given query, exactly how much distance is being traded off against relevance or prominence. What’s documented is the existence and general direction of each factor, not a formula you can back-calculate from observed results.
It’s also worth being precise about what “the search point” actually is. For a query with an explicit city or neighborhood name, Google generally uses a point associated with that named area. For queries without an explicit location (“plumber near me,” or just “plumber” with location services on), the point is the user’s actual detected location, which is why implicit-local and explicit geo-modified queries can produce meaningfully different result sets for the same underlying business, even when nothing on the business’s profile has changed.
There’s a further wrinkle that trips up practitioners auditing this manually: the point used for a “city” query is not necessarily the geographic centroid of the city as drawn on a map. Cities have commercial centers, population centers, and geographic centers that can sit in noticeably different places, especially for large or irregularly shaped municipalities, or cities that have annexed large low-density areas over time. A search point Google infers as “Austin” might sit closer to the dense downtown core than to the city’s literal geographic midpoint, which means a business in a denser, more central neighborhood can have a real, structural distance advantage over a business that is technically closer to the city’s geographic boundary but farther from wherever Google’s systems have actually anchored the query. This is invisible to manual review unless you’re actively checking coordinates rather than reasoning from a map of city limits.
This also explains why rank-tracking tools that let you set a specific lat/long grid point for local pack tracking produce more useful, less confusing data than tools that only let you specify “city, state” as a tracking location. If you’re auditing why a business ranks differently across two tools, checking whether they’re anchoring the query to the same coordinate is a legitimate first diagnostic step before assuming an algorithmic explanation.
A worked example of distance beating city-limit intuition
Picture a search for “plumber in Riverdale.” Business A sits 0.4 miles inside the Riverdale city limit but out in a sparsely built residential stretch, has a bare-bones Business Profile with a generic “Plumber” category and 6 reviews. Business B sits 1.7 miles away, technically across the line in the neighboring town, but close to Riverdale’s dense commercial core, has a precise “Emergency Plumbing Service” category, a fully filled-out services list, and 140 reviews averaging 4.7 stars.
Under a city-boundary mental model, Business A should win by default since it’s the only one actually “in” Riverdale. In practice, Google is calculating distance from a search point that likely sits closer to Riverdale’s commercial center than to Business A’s residential location, and Business B’s much stronger relevance and prominence signals can outweigh its larger raw distance from that point. Business B outranking Business A despite being over a mile farther from, and technically outside, the searched city is exactly the outcome the point-and-radius model predicts and the boundary model can’t explain.
Fixing local pack visibility when you’re outside the searched city
Stop treating “is my business located inside the named city” as the operative question. It isn’t a binary gate. Instead:
- Verify your actual pinned location on the Business Profile is accurate. An imprecise pin, even by a few blocks, changes the distance calculation Google uses, and it’s a purely mechanical fix with no ambiguity involved.
- Invest in relevance signals that don’t depend on your address: an accurate primary category, service/product lists that match what people search for, and on-site content that genuinely discusses the area you serve. If your distance disadvantage is structural (you’re simply not centrally located), relevance and prominence are the levers you can actually move.
- Don’t assume a closer competitor with weaker signals will stay ahead forever, and don’t assume you can out-city-match your way past a real distance gap by stuffing city names into your business name or descriptions. That’s a policy violation (keyword-stuffed business names are explicitly prohibited under Google’s guidelines) and doesn’t change the underlying distance calculation anyway.
- If you’re auditing why a competitor outranks you despite being farther from the searched city center, check their review count, category precision, and whether their address is simply closer to the actual query’s implied point than you assumed. City-limit intuition is frequently wrong once you check the real coordinates.
The practical mental model: think in terms of a point and a radius that shifts per query, with relevance and prominence able to pull a farther business ahead of a closer one, rather than a fixed city boundary that businesses are either inside or outside of.
