Google Maps Ranking Factors 2026: What Actually Matters (S2, RSVM, Entity Trust)

The 2026 Proximity Equation

The ranking formula combines S2 Occupancy (about 35%), RSVM (about 25%), Entity Trust (about 25%), Distance Decay (about 3%), and other signals (about 12%).

These are reverse-engineered estimates based on methodology and patent analysis rather than official Google data.

• ▸S2 Occupancy: ~35%
• ▸RSVM (Rank Embed Spatial Vector Matching): ~25%
• ▸Entity Trust: ~25%
• ▸Distance Decay: ~3%
• ▸Other signals: ~12%

Factor 1: S2 Occupancy (~35%)

S2 cells form Google's hierarchical geographic grid, operating at Levels 10-14 for local SEO and representing areas of roughly 0.3 to 5 square miles each.

When customers run location-based queries, Google identifies the relevant S2 cells within a 1-2 mile radius and scores businesses using the four-factor proximity equation. To measure your standing, run a GeoGrid coverage scan to see which S2 cells rank in the top 3 versus which are absent.

• ▸Lever: neighborhood spoke pages with 80% unique content each, in a hub-and-spoke architecture
• ▸Lever: correct primary category assignment
• ▸Measurement: GeoGrid coverage scan to visualize ranked vs. absent cells

Factor 2: RSVM, Rank Embed Spatial Vector Matching (~25%)

RSVM matches your on-page content to the geographic elements of specific S2 cells through landmarks, roads, zip codes, and microclimate information.

Beware the information gain penalty: copy-pasting across twenty neighborhood pages gets flagged as low-information-gain content per US Patent 11,366,956.

• ▸Landmark match: reference real parks, schools, and buildings
• ▸Zip code match: include natural zip code references
• ▸Microclimate content: area-specific environmental factors
• ▸Local cost data: permit costs and typical pricing
• ▸Real photos with GPS EXIF: location-specific imagery

Factor 3: Entity Trust Compression (~25%)

Entity Trust is the aggregate signal that determines whether Google recommends your business, and it can expand your ranking radius from 2 miles to 20 miles.

When signals are coherent across every layer, the effect becomes multiplicative rather than additive, lifting ranking performance significantly.

• ▸Google Business Profile verification
• ▸Citations across authority directories (Clutch, BBB)
• ▸Knowledge Graph recognition
• ▸AI Overview presence
• ▸Review velocity of 2-4 reviews weekly
• ▸Review Q&A coverage with responses

Factor 4 and 5: Distance Decay (~3%) and Other Signals (~12%)

Distance functions as a tiebreaker when competitor signals roughly match, not as the primary ranking driver. Strong neighborhood spoke pages across a service area let you rank from 20 miles away when local competitors lack a developed signal stack. The financial reality: opening a second location costs $50,000-$200,000 for about 3% signal weight, while spoke pages provide 35% weight at a fraction of the cost.

Other signals (about 12%) are behavioral and engagement signals: NavBoost navigation data, click-through rate from Map Pack results, dwell time engagement, and freshness from recent activity.

Your 0-100 AI Trust Score and What To Do

The GeoGrid scan compresses the factors into five scored components: Grid Coverage (30%), Radius Reach (20%), Competitive Position (20%), Entity Consistency (15%), and Reviews & Social Proof (15%). Scores fall into three bands: 0-40 Invisible (weak signals, competitors capture leads), 41-70 Partial (dominance near the storefront, fading at the edges), and 71-100 Dominant (a full, coherent factor stack).

In practice, follow this priority sequence: fix the primary category (10 minutes), run a GeoGrid scan for a baseline, deploy 10-20 neighborhood spoke pages with 80% unique content, start a review velocity program (2-4 weekly), ensure NAP consistency across directories, optimize service pages, and ignore distance decay unless it's operationally necessary.

• ▸0-40 Invisible: weak signals, competitors capture the leads
• ▸41-70 Partial: dominance near the storefront, fades at the edges
• ▸71-100 Dominant: full, coherent factor stack