Why a 5x5 Local Search Grid Scans 91 Points, Not 25
If you ran a scan in EmbedMyReviews and noticed the point count is higher than you expected, you are not looking at a bug. The Local Search Grid uses a hexagonal ring layout, not a literal square grid. A "5x5" setting means five rings around the centre point, which works out to 91 search locations in total.
This guide explains how the point count is calculated, why hexagonal rings are used instead of a 5-by-5 square, what each scan actually costs through DataForSEO, and how to size a scan correctly for the area you want to cover.
For the bigger picture on what a Local Search Grid is and how agencies use it, start with the main Local Search Grid guide. For the in-product setup walkthrough see the docs version of this guide.
The short answer
The number you pick in the Grid Size dropdown is the number of rings around the centre pin. Each ring contains ceil(6 × r) points, where r is the ring index. Add the centre pin and you get the total.
For a "5x5" scan the rings work out as: 1 centre point, plus 6 + 12 + 18 + 24 + 30 around the outside, totalling 91 GPS points.
The naming convention follows other geo-grid tools so the UI feels familiar, but the actual layout underneath is denser and more accurate than a square grid of the same name. You see the same coverage that competitors only offer at their largest grid sizes, at every grid size we ship.
Formula
total_points = 1 + Σ ceil(6 × r) for r in 1..gridSize
Where gridSize is the value you select (3, 5, 7, 9, 11, 13, or 15).
How the rings build up
Every grid starts with one pin at the business location. The platform then places concentric rings around it. The first ring has 6 points evenly spaced, the second ring has 12, the third has 18, and so on. Each new ring adds 6 more points than the one before it.
| Grid size | Centre | Ring breakdown | Total pins |
|---|---|---|---|
| 3x3 | 1 | 6 + 12 + 18 | 37 |
| 5x5 | 1 | 6 + 12 + 18 + 24 + 30 | 91 |
| 7x7 | 1 | 6 + 12 + 18 + 24 + 30 + 36 + 42 | 169 |
| 9x9 | 1 | 6 + 12 + ... + 48 + 54 | 271 |
| 11x11 | 1 | 6 + 12 + ... + 60 + 66 | 397 |
| 13x13 | 1 | 6 + 12 + ... + 72 + 78 | 547 |
| 15x15 | 1 | 6 + 12 + ... + 84 + 90 | 721 |
The 5x5 row is highlighted because it is the most common scan size and the one most often questioned.
If you want to verify the math for any row, multiply each ring index by 6, round up (ceiling), and add them together with the centre pin. For 7 rings that is 1 + 6 + 12 + 18 + 24 + 30 + 36 + 42, which gives 169.
Why hexagonal rings instead of a square grid
A square 5-by-5 grid puts 25 points on a flat plane with even spacing in two directions. It looks tidy on paper, but it is a poor match for how Google Maps results actually change as a searcher moves around. A circle of customers around a business does not radiate out in neat rows. It radiates out in every direction.
Hexagonal rings give you roughly equidistant coverage in all directions from the centre. Every pin in the same ring sits the same distance from the business, so when you look at the report you see how rankings degrade by distance rather than by which corner of a rectangle you happen to be in.
The density also matches how local pack results behave in practice. Rankings can shift over a few city blocks. With 91 points across a 5-mile radius, you catch those transitions instead of stepping over them.
Square grid (other tools)
- Even spacing on a rectangle
- Corners sit further from centre than edges
- 25 points for a "5x5"
- Misses transitions between rows
Hexagonal rings (EMR)
- Even spacing on circles
- All pins in a ring sit at the same radius
- 91 points for a "5x5"
- Captures ranking shifts across the radius
What the distance setting actually controls
The distance value you enter (labelled "Point Spacing" in the scan form) sets the radius of the outermost ring. Inner rings are spaced proportionally between the centre and that edge. The math is simple: ring spacing = distance ÷ grid size.
For a 5-mile distance on a 5x5 grid that gives you a 1 mile gap between rings. Ring 1 sits at 1 mile, ring 2 at 2 miles, all the way out to ring 5 at the full 5 miles.
The point count stays the same regardless of how you set the distance. A 5x5 scan with a half-mile radius and a 5x5 scan with a 10-mile radius both run 91 search locations. The radius only changes how spread out those points are, not how many there are.
Quick reference
Grid size controls density and area coverage. Distance controls how far out the outer ring sits. Keyword count multiplies the total API calls per scan. None of the three affect the other two.
What this means for DataForSEO cost
EMR uses the BYOK model for the Local Search Grid. You connect your own DataForSEO account, and each pin in a scan becomes one Google Maps SERP task at DataForSEO's published rates. We add zero markup.
The formula for billable API tasks per scan is:
Per-scan API task count
api_tasks = total_pins × keyword_count
A 5x5 scan with 3 keywords runs 91 × 3 = 273 SERP tasks.
Pricing changes from time to time, and DataForSEO offers different queue tiers (standard, priority, live). For the current per-task price check DataForSEO directly, then multiply by the task count above to project the cost of a scan. The numbers will still come in well below per-scan credit pricing on dedicated geo-grid tools, but you should run them against today's published rates rather than rely on a hard-coded figure.
Tip for cost planning
The biggest cost lever is grid size, not radius. Going from 5x5 to 7x7 increases your pin count from 91 to 169, an 86 percent jump. Going from 5x5 to 3x3 drops it to 37, a 59 percent saving. Pick the smallest grid that still covers the service area you care about.
How to pick the right grid size
More points is not always better. A denser grid costs more, takes longer to complete, and produces a busier report. The right answer depends on the size of the area your client actually serves.
Small footprint, single neighbourhood
Use this for tight service areas: a single coffee shop, a small dental practice, or any business whose customers all come from inside a 1 to 2 mile radius. Cheapest scan and quickest to run.
Most common, full city or suburb
The default for most local businesses. Covers a 3 to 5 mile service area with enough density to catch ranking shifts between neighbourhoods. The right starting point if you are unsure.
Wider metro or service area business
For plumbers, electricians, HVAC, and other service area businesses covering an entire city or working across multiple suburbs. Enough density to surface where a SAB drops off the map.
Large metro or one-off audit reports
Reserve these for big upfront audits or for businesses with genuinely large coverage areas (regional firms, multi-city franchises). Higher API cost, so most agencies use 5x5 or 7x7 for monthly tracking and reserve the larger grids for sales presentations.
Related guides
Other pages that pair well with this one when you are planning Local Search Grid scans or selling them as part of a client deliverable.
Local Search Grid for Agencies
The full guide. What a geo-grid is, why agencies use it, BYOK economics, and what to look for in a tool.
Local Search Grid Feature
Product-level breakdown of how the Local Search Grid works inside EmbedMyReviews.
Sales Intelligence
Pair grid scans with AI audit reports to build proposals that show both reputation and ranking gaps.
Why Per-Location Pricing Kills Margins
The economics that make BYOK and flat-rate the only viable model for agencies running many scans.
Common questions
Plan your next scan with confidence
Unlimited Local Search Grid scans, hexagonal coverage at every grid size, BYOK DataForSEO pricing, and a full white-label reputation management platform for $99 a month flat.