Geogrid

Google's local search results are geographically variable — sometimes dramatically so. A pizza shop in lower Manhattan might rank #1 in the Map Pack when someone searches "pizza" from the street corner outside the restaurant, but rank #6 when someone searches the same word from three blocks away. The geographic position of the searcher is itself a ranking signal. Proximity to the business matters; proximity to competitors matters; the density of the local market matters.

Single-IP rank trackers — the kind that have been the standard tool in SEO since the late 2000s — completely miss this dimension. They sample from one IP, report one number, and call it the "rank." That rank is true at that single point. It says almost nothing about what's happening across the rest of the area where the business actually competes for customers.

Geogrids solve this by sampling from a grid of points. The result isn't a single number; it's a heatmap. You can see where you dominate, where you slip, and where you're invisible.

A geogrid scan takes three inputs: a center coordinate (usually the business's location), a keyword to scan for, and a grid size (typically 5×5, 7×7, or 9×9). The scanner generates a grid of geographic points around the center, then runs the same Google search from each point and records the rank of the target business.

For a 5×5 scan, that's 25 individual Google searches, each from a different geographic position. The output is a matrix of ranks — one number per cell — that gets rendered as a colored heatmap (greens for high rank, yellows for middling, reds for low or absent).

Modern geogrid endpoints handle the geographic targeting through Google's UULE parameter, which lets a request specify the location it should be evaluated from. This is what makes the results actually different per cell — without UULE targeting, all 25 searches would return the same result.

Different grid sizes serve different purposes:

• 3×3 (9 points) — spot check or sanity check. Useful for the free tool above, or for diagnosing a specific neighborhood. Not detailed enough for monitoring.
• 5×5 (25 points) — single-business standard. Captures enough resolution for dense urban markets. Fastest to scan; good for daily monitoring at low credit cost.
• 7×7 (49 points) — the most common default for agencies. Best balance of resolution and cost. Use this for weekly monitoring across most local markets.
• 9×9 (81 points) — suburban or service-area business mapping. Higher resolution catches edge cases that smaller grids miss. Use when the business serves a larger geographic territory or when precision matters for sales conversations.

Different ranking patterns mean different things. Once you start looking at geogrids regularly, you learn to read them:

• Strong at center, weak at edges — the most common healthy pattern. You rank #1-3 close to your location and drop off as the scanner moves toward competitor territory.
• Dead zone in one direction — you rank well in three quadrants but lose one. Usually means a dominant competitor controls that area.
• Uniformly weak across the grid — you barely show up anywhere. Foundation issue: profile not optimized, categories wrong, citations inconsistent.
• Patchy / competitive split — you and competitors trade cells unpredictably. Means the market is highly competitive and proximity is the dominant signal.
• Strong at edges, weak at center — rare. Usually a sign of indexing problems with the business's profile near the registered address.

The shift to agent-driven local SEO has made geogrid data more useful, not less. A human looking at a heatmap can see patterns. An agent looking at a JSON matrix can also identify patterns — and can do it across all 12 client locations every week, flag the ones with deteriorating AGR, draft an investigation prompt for the worst-performing cells, and chain into citation audits or competitor analysis automatically.

Most operators running multi-location local SEO now have a cron-scheduled agent that runs geogrid scans across all client locations, computes AGR/ATRP/SoLV deltas vs. previous scans, and only surfaces locations where something meaningfully changed. The agent reads the grid; the human reads the agent's summary.