Geo-Grid Rank Tracking: Measure Edmonton Google Maps Rankings

How should I choose a geo-grid territory and point spacing?

Start with the business’s actual service territory and the decision the scan must support. A dense grid around one district answers a different question from a broad Edmonton coverage scan. Store the centre, point coordinates, spacing and exclusions. More points are not automatically better if the added territory has no commercial relevance.

The live DecodeLocal SEO audit describes 90+ fixed points, with roughly 1.8-kilometre spacing in its standard method. A different footprint must be documented rather than quietly substituted. Name the provider, timestamp and observation settings so someone else can understand what the coloured cells represent.

How do I calculate geo-grid coverage with the correct denominator?

For a single query, top-three coverage equals valid locations ranking 1-3 divided by all valid measured locations, multiplied by 100. A failed request is not a rank of 20. A business absent from a successfully measured top-20 result is ‘not found within 20’, not a known position of 21.

For multiple queries, decide whether each query-point observation has equal weight. If weights are used, publish them and retain the unweighted result. Do not call this percentage market share: a grid samples visibility, not every customer, click or purchase. A grid result also is not Search Console’s average position.

What settings must a geo-grid rank tracking report include?

Create a scan contract before collecting data. It identifies the business, exact query, result surface, coordinates, device, language, collection date and result depth. Store the public profile identifier where available rather than matching only a business name, which can be shared by unrelated businesses.

Define whether the target is a Maps result, a local pack within a search page or an organic website position. These are different measurements. Do not combine them into one average rank. Record whether the data source simulates a coordinate, how businesses are matched and what limitations the provider states.

Keep the planned coordinates immutable for a comparison cohort. If the business moves or the commercial territory changes, create a new named cohort and explain the change. Retain the previous definition so an analyst can distinguish new sampling from new visibility.

Should I use a 5×5, 7×7 or larger geo-grid?

Choose the grid to answer the business question, not because one dimension looks impressive in a report. A 5×5 grid contains 25 planned points and a 7×7 contains 49. Those counts say nothing about land coverage until the spacing and boundary are stated. A compact grid near the base and a broad grid across the service territory answer different questions.

For a regular square with n points along a side and spacing s, the distance between the outer point centres is (n − 1) × s. A 5×5 grid with 1.8 km spacing spans 7.2 km between its outer centres on each side. This is a geometric example, not a claim about the dimensions of every archived grid or the area Google considers relevant.

Irregular service territories may need points selected around actual customer areas rather than an automatic square. Document exclusions such as unserved areas. If points are weighted by commercial importance, publish the weights and report the unweighted count too. Never describe sampled-point coverage as a percentage of Edmonton residents reached.

What is the difference between top-three coverage and average rank?

Top-three coverage is the number of included points where the business ranked first, second or third divided by the stated included-point count. In the published Sherwood Park sample, Adster appears in the top three at 21 of 25 measured points: 84%. It appears first at 10 of 25: 40%. These are sampled-location measurements.

The same dataset has 23 points with a local pack. Restricting the denominator to those points gives 21/23, approximately 91.3%, for top-three coverage. Neither calculation should be silently substituted for the other. State whether the denominator represents all measured points or only points with the feature.

An average rank can be misleading when the collection stops at a fixed depth. A business absent from the first twenty results is known to be outside that depth, not known to rank twenty-first. Assigning it 21 produces an invented exact position. Report observed-rank summaries alongside outside-depth counts and the coverage measure instead.

How do I compare two geo-grid scans fairly?

Match the query, business identifier, coordinate list, device, result surface and collection depth. Compare points successfully collected in both periods, then disclose how many planned points were omitted because one period failed. Preserve the full exports, including failures, so the comparison can be reconstructed.

For illustration, a business visible in the top three at 18 of 90 matched points has 20% coverage. A repeat with 27 of the same 90 points has 30% coverage. That is an increase of 10 percentage points and 50% relative to the original coverage. It is not evidence of 50% more enquiries or revenue.

Annotate website changes, profile moves, competitor changes and collection differences. Multiple interventions prevent confident attribution to one change. Use a schedule appropriate to the engagement and the stability of the observations. A repeat scan verifies a new observation; it does not by itself prove why a rank changed.

What should a business do after finding weak geo-grid coverage?

Separate a local weakness around the base from a loss concentrated farther away. Compare service queries rather than assuming the broad category describes every service. Record which competitors appear consistently and whether the business has a relevant service page and accurate profile representation.

A grid identifies where to investigate. It does not prescribe an extra office, fifty citations or a fixed review target. For a location decision, combine the observations with actual demand, operating costs and deliverable territory. For an existing business, begin with a written diagnosis linking each recommendation to the measured query and points.

The completed report includes the scan contract, raw observations, error log, point map, coverage calculations and a next-step register. Every chart should identify its dates and denominator. The business should be able to retain the exports independently of the reporting tool.

How do missing results change a geo-grid rank tracking report?

In the actual Sherwood Park sample below, top-three coverage for Adster is 21 ÷ 25 = 84% across all measured points. First-place coverage is 10 ÷ 25 = 40%. Restricting the denominator to the 23 points with a local pack yields 91.3% and 43.5%. Both denominators answer a definable question, but swapping them between reporting periods would create an apparent improvement without a single rank changing.

Keep three states separate in the export: a successful result where the business is outside the collected depth, a successful result with no local pack, and a failed collection. A failure is unknown, not last place. For comparison, report the planned points, successfully collected points and matched points retained in both periods. Avoid averaging censored ranks by assigning an arbitrary 21 to every ‘not found’ result. Show top-three coverage and the count outside depth instead. A denser grid changes the sampling design; it does not retroactively improve the business's coverage.

What does the measured Sherwood Park geo-grid actually show?

This is the archived August 30, 2026 SEO agency query, collected on desktop at 25 points. It is a measurement-method example, not a current result or a client improvement. The point-level table and source labels preserve the observed states.

What are the next steps for geo-grid rank tracking?

1. 01Save the grid definition and frozen query list before the baseline.
2. 02Log device, language, date, result type and observation depth.
3. 03Separate valid results, not-found results and failed requests.
4. 04Calculate top-three coverage with its numerator and denominator.
5. 05Repeat the same protocol and describe changes in percentage points.

What should the completed work contain?

A reusable grid definition, raw observation table, failed-request log and before/after coverage summary. Keep baseline exports immutable so a later analyst can reproduce the calculation.

Download the measurement worksheet. Record the evidence date, owner, action and verification result before marking a task complete.

How can I apply geo-grid rank tracking to a business decision?

Illustrative: baseline visibility is 18 of 90 valid locations, or 20%. A repeat is 27 of the same 90 locations, or 30%. That is +10 percentage points and +50% relative growth in sampled coverage. It is not evidence that revenue increased by either amount.

What else should I know about geo-grid rank tracking?

Which local SEO guide answers my next question?

• Google Maps visibility diagnosis
• map-pack reach estimator
• Sherwood Park measurement
• verify reported SEO results

What sources support this geo-grid rank tracking guide?

Official guidance informs the checks. DecodeLocal’s dated research supplies measured exhibits; worked scenarios are labelled illustrative. The workflow does not establish a guaranteed ranking or a causal effect from one change.

• Google: how local ranking works
• Google: Search Console performance reports