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Historical Geogrid Data: How to Read and Compare Scans

Historical Geogrid Data: How to Read and Compare Scans

Hand-drawn neighborhood grid and scan points

Historical geogrid data is a series of Google Maps ranking scans for the same business, keyword and grid, taken over weeks or months and stored so you can compare them. One scan tells you where a business ranks today. A history tells you whether that picture is improving, slipping or just noisy, and in which neighborhoods. To compare geogrid scans fairly, you have to keep the scan settings identical and judge the change against real business outcomes, not against how green the map looks.

This guide covers what the history shows, how to set scans up so they can be compared, how to read the changes, and where the data misleads.

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TL;DR

  • Historical geogrid data is only comparable when grid size, spacing, center point, keyword, device and language stay the same between scans.
  • If you change any of those settings, start a new baseline and keep the old series separate.
  • Read the history at point level and by neighborhood, not only as an average rank.
  • A greener map is a visibility signal, not proof of results. Check it against calls, direction requests and booked jobs.
  • Scan on a fixed schedule so gaps in the data do not hide trends.

Table of Contents

What historical geogrid data actually contains

A geogrid scan places a grid of points over a service area, say 5×5 or 7×7. At each point it runs the same Google Maps search and records where the business appears. Each scan is a snapshot: one keyword, one grid, one moment.

Stack those snapshots and you get a history with three layers:

  1. Point-level ranks. The position of the business at each coordinate, scan after scan.
  2. Coverage metrics. The share of points where the business shows in the top three, the top ten, or not at all.
  3. Competitor context. Who holds the top spots at points you lost or have never won.

The point-level layer is the most useful and the one most often ignored. An average rank can stay flat while the business gains ground to the north and loses it to the south. Only the point-level history shows that.

Why do ranks differ from block to block at all? Google says local results rest primarily on relevance, distance and prominence, and distance is measured from the searcher’s location. A grid makes that distance effect visible. A business is usually strongest around its own address and weaker as the searcher moves away, and competitors closer to those outer points get an advantage.

A 2026 study of six UK businesses by a local SEO company illustrates the pattern. In its latest scans, 83.9% of center pins ranked in the top three, against 12.0% at 0 to 1 km and 4.1% at 2 to 3 km. That is a small sample, so treat it as an illustration of the drop-off rather than a benchmark for your clients. Your own history will show your own curve, which is far more useful.

Top-three rankings decline with distance from center

Set up scans so they can be compared

The most common mistake with historical data is comparing scans that were never comparable. A map that looks worse in March than in February may simply have been run on a different grid.

Guidance from several local SEO publishers lands on the same rule. Comparison requires identical control variables: grid size, point spacing, center coordinates, keywords and device or language settings for the whole engagement. Another 2026 guide adds result surface and the vendor’s rank rule to the list, and says a change in any of them creates a different measurement.

Use this as a pre-flight checklist before the first scan:

Setting Why it must stay fixed What to record
Grid size (5×5, 7×7, 9×9) More points change coverage percentages Rows and columns
Point spacing or radius Wider grids reach weaker areas Distance between points
Center coordinates Moving the center shifts every point Latitude and longitude
Keyword Different phrases rank differently Exact wording
Device and language Mobile and desktop results can differ Both settings
Business tracked Duplicate or renamed profiles break the series Profile name and place ID

Write the settings down in the client file. A year from now, you or a colleague will need them.

What to do when you must change the grid

Sometimes the grid has to change: the client opens a second service area, or the first radius was too tight. The honest answer is to start over. One guide puts it plainly: moving a grid from 5×5 to 7×7 makes historical comparisons invalid unless you begin a fresh baseline. Keep the earlier series for what it measured, label the new one with its start date, and do not splice the two into one trend line.

If you need continuity, there is one workaround: compare only the exact coordinates the two grids share, for the same keyword. That is a narrower claim, so label it as such in the report.

Schedule matters too

Scan on a regular rhythm: weekly for active campaigns, monthly for maintenance clients. Irregular scanning leaves gaps that hide when a change started. Guides on grid tracking make the same point: inconsistent monitoring frequency obscures trends and makes month-to-month comparison unreliable. Pick a day of the week and a time window and hold to them.

How to compare two geogrid scans, step by step

Once the settings match, compare scans in a fixed order. It keeps you from telling a story the data does not support.

  1. Confirm the settings. Check the scan records for grid, spacing, center, keyword and device. If anything differs, stop and treat the two as separate baselines.
  2. Compare coverage first. Look at the share of points in the top three and top ten. This is the headline number for a client.
  3. Move to point level. Find the points that changed by several positions, up or down, and mark them on the map.
  4. Group the changes by neighborhood. A cluster of green points to the east means something. A single point that flipped usually means little.
  5. Check who moved. At the points you gained or lost, see which competitors held the top spots before and after.
  6. Look at the surrounding events. Note anything that happened between scans: new reviews, profile edits, new service pages, a competitor opening nearby.

Step 3 matters most. Single points can swing from one scan to the next because search results shift for reasons unrelated to your work. Look for patterns that repeat over several scans before you report them as movement.

With several months of scans, a few useful patterns appear.

Expansion or contraction of your strong zone. The area where the business ranks in the top three grows outward from the address, or shrinks back. This is the clearest progress signal for most local SEO work.

Neighborhood gaps. Some areas stay red scan after scan, even though they sit inside the service area. These are the market gaps. They may reflect a strong competitor, a lack of local relevance, or simple distance. Each calls for a different fix.

Seasonal swings. Service businesses see demand and competitor activity change through the year. A history lets you tell a seasonal dip from a real loss, because you can compare this January to last January on an identical grid.

Effect of specific changes. If you rewrote the primary category or added service pages in a given week, the scans before and after give you a before-and-after picture for that action. Treat it as evidence, not as proof, for reasons covered next.

Keyword divergence. The same grid can look healthy for one keyword and weak for another. Keeping a separate series per keyword shows which services the business owns and which it does not.

If you want a deeper look at tracking mechanics, our guide on Google Maps rank tracking with historical data covers how ranking trends are stored over time, and our overview of how to track Google Maps rankings covers the basics.

What the data cannot tell you

Geogrid history is a diagnostic. It is not a verdict.

A ranking difference is an observation, not proof of cause. The same guidance that stresses identical settings also says to review point-level patterns and actual inquiry changes alongside grid data. A separate guide agrees that repeated scans can reveal broad movement across geographic clusters, but a greener map does not by itself prove a business outcome. It suggests comparing results with calls, direction requests, forms, visits or booked jobs where you have them.

Three practical limits follow from that.

  • Reliability varies with search conditions. A scan simulates a searcher at a coordinate. Real searchers differ by device, history and exact location, so a single scan is an estimate. That is why consistent settings and repeated scans matter more than any one result.
  • Correlation is not attribution. If rankings rose in the same month as a new ad campaign, a seasonal peak and a review push, the grid cannot say which did the work.
  • More green is not always more revenue. Winning points in an area with few customers is worth less than holding one in a dense, high-value neighborhood.

The fix is to pair the grid with outcome data. Track calls, direction requests and form fills from the Google Business Profile and your own analytics, then line them up against the scan dates. When you want to test whether ranking movement tracks with other marketing activity, a reference on localized advertising and measurable outcomes can help you think through what to measure alongside rankings. Treat each grid change as a hypothesis to test, then test it.

Turning history into client reports

Agencies get the most from historical data when it becomes a simple story the client can follow.

A reliable report structure:

  1. Baseline. The first scan, with the settings stated.
  2. Latest scan. Same settings, same keyword, side by side with the baseline.
  3. Coverage change. Top-three share and top-ten share, then and now.
  4. Where it moved. Two or three neighborhoods that improved, one that did not.
  5. What we did. The specific work done between scans.
  6. What happened to leads. Calls, directions and forms across the same period.
  7. Next test. One change to make before the next scan.

Keep the language plain. A business owner does not need to know about point spacing. They need to see that the area around their second location went from red to green, and that calls from that area went up too.

Our pages on the geo grid SEO tool approach and on measuring the impact of local SEO work go further on presenting results.

Perspective: why we built history to be portable

We see the same failure again and again. An agency tracks a client for six months, the client asks for a second location or a wider service area, and the tool’s pricing or structure punishes the change. The agency then trims the grid or drops keywords to save credits, and the history quietly stops being comparable.

That is a pricing problem that becomes a data problem. At Maprank, every plan includes unlimited businesses and white label reporting, and you pay by scan credits, so adding a client does not change the economics of keeping a clean baseline. Grid size and spacing are yours to set, which also means they are yours to keep fixed. We also do not ask for Google account connections, so a client’s history never depends on access that can be revoked.

Our view is that geogrid history should be treated as a way to ask better questions. A cluster of red points tells you where to look, not what to do. Use it to form a hypothesis, make one change, rescan on the same settings, and check the result against the leads the client actually received.

— Local

Keep your history with Maprank

If you run local SEO for several clients, the cheapest setup is the one that lets you hold settings steady without counting locations. Maprank plans are priced by scan credits, seats and grid size, and every plan includes unlimited businesses and white label reports on your own domain.

  • Solo: $19 per month or $228 per year
  • Agency: $39 per month or $468 per year
  • Pro: $79 per month or $948 per year
  • Scale: $149 per month or $1,788 per year

Maprank

See the current plans on the Maprank pricing page, or try a Google Maps rank checker to see where a business stands before you set its baseline.

A practical routine for the first 90 days

If you are starting a history from zero, this routine gives you a defensible record.

Week 1: baseline. Choose the grid, spacing, center and two to five keywords that map to the client’s revenue. Run the first scan. Save the settings.

Weeks 2 to 4: hold steady. Scan weekly on the same settings while you make your first changes. Log each change with its date.

Month 2: first review. Compare coverage to baseline. Check calls and direction requests for the same period. Flag any neighborhoods that moved by a wide margin.

Month 3: decide. Keep the grid if it covers the service area well. If it does not, open a new baseline on a new grid and keep the first series archived, labeled with its start and end dates.

Then repeat. After a year, the history will show seasonality, the effect of your larger projects and the places where the market is still open. For help picking a tracker with these capabilities, our comparison of the best Google Maps rank trackers lays out the criteria, and our guide to a local SEO grid tracker shows how to map a whole service area.

FAQ

How long should I keep historical geogrid data?

Keep it for the length of the client relationship, and longer if storage allows. A full year gives you a year-over-year comparison that separates seasonal changes from real ones. If you open a new baseline after changing the grid, archive the old series rather than deleting it.

Can I compare scans taken on different grid sizes?

Not as a continuous trend. Guidance from local SEO publishers is to start a new baseline when the grid size, spacing or center changes. You can compare only the exact coordinates the two grids share, for the same keyword, and you should say so in the report.

How often should I run scans?

Weekly works for active campaigns, and monthly is enough for maintenance. What matters most is a steady rhythm, because irregular scans leave gaps that hide when a change began. Choose a schedule that fits your credit budget and keep to it.

Does a greener map mean the business is getting more customers?

Not necessarily. More green points mean better visibility for that keyword and grid, but visibility does not guarantee calls or jobs. Compare scan changes with calls, direction requests, form submissions and booked work before you claim a result.

Why do two scans of the same grid sometimes differ?

A scan estimates what a searcher at a given coordinate sees, and results can shift between runs. Single points can swing for reasons unrelated to your work. Look for changes that repeat across several scans and cluster in a neighborhood before treating them as real movement.

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