A ranking study conducted from highway locations between Dallas and Fort Worth found that local pack results shifted from Dallas-centric to Fort Worth-centric within a transition zone spanning approximately 3 miles, with the midpoint showing mixed results from both cities. This finding reveals that Google does not simply assign users to the nearest city center. It calculates a dynamic centroid of relevance that weighs the user’s exact GPS position, the geographic distribution of relevant businesses, and the population-weighted centers of nearby cities. Understanding this centroid determination mechanism explains why businesses in suburban and inter-city locations experience volatile local pack rankings.
How Google Constructs the Proximity Anchor When User Location Is Geographically Ambiguous
For implicit-local queries (searches without an explicit location like “plumber near me” or simply “plumber”), Google uses the searcher’s device-reported location as the initial proximity anchor. When this location falls clearly within a recognized city, the proximity calculation is straightforward: distance from the device to each candidate business.
When the device location falls between recognized geographic entities, such as in a suburban buffer zone between two cities, on a highway between metro areas, or in an unincorporated area adjacent to multiple municipalities, Google must resolve the geographic ambiguity. The system evaluates the relative pull of each nearby geographic entity based on several factors.
Population density around the user’s location influences which city’s businesses receive priority. Areas with higher population density generate stronger geographic entity signals, and Google’s system gravitates toward the population center closest to the user. In the Dallas-Fort Worth corridor, the transition zone occurs roughly where the population distribution shifts from primarily Dallas-oriented residential areas to primarily Fort Worth-oriented ones.
Business density for the query category creates a secondary pull. If a user searches for “Vietnamese restaurant” from a location between two cities, the city with more Vietnamese restaurants exerts a stronger relevance pull because Google has more candidates to evaluate from that city. This category-specific density effect means that the centroid can shift for different query types from the exact same physical location.
Administrative boundary assignments in Google’s internal geographic model provide a baseline city association for each location. Google maintains a comprehensive geographic entity model that assigns every point on the map to one or more geographic entities. This model may differ from official municipal boundaries, postal code assignments, or colloquial city associations that residents use. A user who considers themselves a Dallas resident may fall within Google’s geographic assignment for a different entity.
The practical result is that the centroid of relevance for any implicit-local query is not a fixed geographic point. It is a dynamic calculation that varies by query type, searcher position, and the distribution of relevant businesses in the area.
The Role of Administrative and Postal Boundaries in Resolving Geographic Ambiguity
When a user’s location falls in an area with overlapping geographic identities, Google relies on its internal geographic entity model rather than official municipal boundaries. This model incorporates multiple boundary systems but does not exclusively follow any of them.
Postal code boundaries frequently cross city limits. A user with a Dallas zip code may live in an unincorporated area that Google’s model assigns to a suburb or census-designated place. When this user searches for local services, Google’s centroid calculation uses its internal geographic assignment rather than the postal code city, potentially returning results the user does not expect.
Census-designated places (CDPs) and unincorporated areas present particular challenges. These locations often lack a strong geographic identity in Google’s model, causing the centroid calculation to default to the nearest significant city. A user in an unincorporated area between two cities may see results from the larger city regardless of which city is physically closer, because the larger city has a stronger geographic entity signal in Google’s database.
Google’s geographic model updates periodically as new data from census records, commercial databases, and user behavior patterns inform the boundary definitions. Businesses in areas where Google’s geographic assignment differs from colloquial or postal boundaries may experience shifts in local pack visibility when these model updates occur, even without any changes to their own optimization or competitive landscape.
The mismatch between user perception and Google’s geographic assignment is a common source of confusion. A business owner who believes they serve “Austin” may find that Google associates their address with a suburb, census-designated place, or unincorporated area, resulting in reduced visibility for “Austin” queries despite geographic proximity to the city center.
Why Category-Specific Business Density Shifts the Centroid for Different Query Types
The centroid of relevance can differ for different queries from the same location because category-specific business density influences which geographic center Google treats as the primary relevance anchor.
Search Engine Land’s analysis of proximity bias in local search confirms that Google applies different proximity radii depending on search intent. A search for “coffee” produces results from a tight neighborhood-level radius because Google recognizes hyperlocal intent. A search for “sports stadium” expands the radius to cover a much wider area because the category density is low and the intent implies willingness to travel.
This category-density interaction creates a shift in the effective centroid for users in inter-city or suburban locations. A user between two cities searching for “dentist” may see results centered on City A because City A has a higher concentration of dental practices within the user’s proximity zone. The same user searching for “Porsche dealer” may see results centered on City B because City B’s auto dealer corridor is closer than City A’s. The user has not moved, but the centroid has shifted because the relevant business distribution is different.
For businesses in low-density categories (specialty medical, luxury auto, niche professional services), this effect is pronounced. Google may pull the centroid toward a city 15 miles away that has a concentration of relevant businesses rather than anchoring to the nearest small city that has none. This creates the anomaly where a suburban business’s local pack competitors are not the businesses geographically closest to it but rather businesses near the categorical density center.
Understanding this category-density shift is essential for competitive analysis. The businesses competing for local pack positions in a given category are not necessarily the geographically nearest competitors but the competitors near the category-specific centroid that Google has calculated for each search location.
The Practical Impact on Businesses Located in Suburban and Inter-City Zones
Businesses in suburban areas, exurbs, and inter-city corridors experience the centroid effect most acutely through ranking volatility. Small changes in where nearby residents search from, combined with the dynamic centroid calculation, produce day-to-day ranking fluctuations that fixed-location businesses near city centers do not experience.
The volatility pattern is predictable. Businesses on the city side of a suburban area (closer to the metro core) show more stable rankings because the centroid calculation consistently assigns searches in their vicinity to the metro city. Businesses on the outer edge of a suburban area show higher volatility because searches in their vicinity may be assigned to different geographic entities depending on the exact search position and query type.
Geogrid rank tracking makes this pattern visible. A business in a suburban location might show consistent top-3 rankings across grid points to the south and east (toward the metro core) and inconsistent or absent rankings across grid points to the north and west (toward the suburban fringe or an adjacent city). This asymmetric visibility pattern directly reflects the centroid assignment boundary.
The volatility is not a sign of poor optimization. It is a structural characteristic of the business’s geographic position. Practitioners must set client expectations accordingly: a suburban business between two cities will never achieve the ranking stability of a business in a city center, regardless of optimization effort.
GBP, Content, and Citation Signals That Reinforce Target City Geographic Association
Businesses in inter-city locations can strengthen their association with a target geographic center through multi-signal geographic reinforcement. These signals cannot override proximity calculations but can influence which geographic entity Google associates the business with in ambiguous cases.
GBP service area declarations that include the target city signal geographic relevance to that city. For storefront businesses, the address determines the primary geographic association, but service area declarations can reinforce the target city connection for businesses on the boundary.
Website content that naturally references the target city in service-area context strengthens geographic relevance. This does not mean stuffing the city name into every paragraph. It means including genuine references to serving the target city: response times to that area, projects completed there, familiarity with local conditions, and knowledge of city-specific regulations.
Citations in target-city directories associate the business with that geographic entity across the citation network. Chamber of commerce membership, local business association listings, and city-specific industry directories all contribute geographic association signals.
Local Link Building and GBP Activity for Shifting Boundary-Zone Centroid Assignment
Local links from target-city organizations provide the strongest geographic association signal from external sources. A link from the Dallas Chamber of Commerce associates the business with Dallas in Google’s entity model, potentially shifting the centroid assignment in the business’s favor for searches near the geographic boundary.
GBP posts referencing target-city activity maintain an ongoing geographic relevance signal. Posts about completed projects in the target city, community involvement events in that area, and service announcements for target-city customers generate fresh geographic association signals within the GBP system.
The combined effect of these signals is incremental, not transformative. A business 8 miles from the city center cannot create signals strong enough to compete with a business 1 mile from the center. However, in the ambiguous zone where the centroid assignment could go either way, consistent geographic signals can tip the balance in the business’s favor, expanding the geographic area from which the business captures local pack visibility by one to three miles.
Does using a VPN or searching from an incognito browser change the centroid calculation Google applies?
Incognito browsing removes personalization signals but does not change the location-based centroid calculation. Google still uses the device’s IP address or GPS coordinates for proximity anchoring. A VPN shifts the apparent location to the VPN server’s IP address, which changes the centroid entirely because Google calculates proximity from the perceived location. This is why VPN-based rank checking produces unreliable results; the centroid is calculated from the VPN exit point, not the user’s actual location.
Can a business in a suburban location between two cities optimize for both city centroids simultaneously?
Optimizing for both cities is possible but requires divided resources and typically produces moderate results in both rather than strong results in one. Each city requires its own set of supporting signals: city-specific citations, reviews mentioning that city, local landing page content, and links from city-associated organizations. The GBP listing can declare both cities as service areas, but the proximity anchor remains fixed at the business address. Businesses in the exact midpoint benefit most from dual targeting; those significantly closer to one city should prioritize that city.
How does Google’s centroid calculation differ for voice search queries compared to typed mobile queries?
Voice search queries triggered through a mobile device use the same GPS-based location signal as typed queries, so the centroid calculation is identical. The difference lies in query structure: voice queries are more likely to be implicit-local (“find a dentist”) without explicit city names, which means the centroid calculation activates more frequently. Voice queries through smart home devices use the device’s registered address rather than GPS, creating a fixed proximity anchor that does not shift with user movement.
Sources
- The Proximity Paradox: Beating Local SEO’s Distance Bias – Search Engine Land
- How Proximity Affects Rankings in Local Search Results in 2025 – JurisDigital
- Proximity Is Power: How to Choose a Business Location That Ranks on Google Maps – Trevor Tynes
- Whitespark Local Search Ranking Factors Survey
- Overcoming Google’s Local Search Bias – Energy Circle
