Heatmap

A heatmap assigns a color to each cell in the geogrid matrix based on the rank of the target business at that geographic point. The standard color scheme is:

• Dark green — rank 1–3 (strong position in the Local Pack)
• Light green — rank 4–6 (visible but below the top 3)
• Yellow — rank 7–15 (present in local search but not prominent)
• Orange — rank 16–30 (weak presence; below fold)
• Red — no rank (not appearing in local results at all)

This color scheme mirrors heat intensity: the hotter the color (green = cool, red = hot), the better the rank. At a glance, you can spot where you dominate (the green zones), where you're losing ground (yellow/orange), and where you're entirely absent (red).

Experienced operators learn to diagnose business and market conditions by reading heatmap shapes:

• Bull's-eye (strong center, weak edges) — your business dominates its immediate area but loses rank as distance increases. This is the healthy pattern for a single location.
• Holes or dead zones — one or more cells showing red or orange while surrounding cells are green. Often indicates a competitor with strong local dominance in that area, or a citation/review problem at that specific address.
• Uniform weakness — the entire grid is yellow or red. Foundation issue: profile optimization, category mismatch, inconsistent NAP, or insufficient citations.
• Competitive scatter — alternating colors with no clear pattern. Sign of a tight market where multiple competitors are equally matched and proximity is the primary tiebreaker.
• Asymmetry — one quadrant performs well while others don't. Suggests a competitor or market cluster in the weaker areas.

Heatmap usefulness depends on grid density. A 3×3 heatmap (9 cells) gives a quick sanity check but misses neighborhood-level variation. A 5×5 (25 cells) shows enough resolution for most urban markets and reveals local competition patterns. A 7×7 (49 cells) is the common practice for weekly agency monitoring. A 9×9 (81 cells) catches edge cases in suburban or service-area territories.

Larger grids take longer to scan and cost more credits, but they also reveal finer-grained rank variation. The choice depends on how much geographic precision your business or client needs. A single-location pizza shop rarely needs 9×9; a multi-service contractor covering three counties usually does.

In automated local SEO, heatmaps become machine-readable data structures — JSON matrices of rank values — rather than visual images. An AI agent can ingest a heatmap, detect dead zones automatically, compute rank-average metrics like AGR (Average Geographic Rank), flag cells that deteriorated week-over-week, and feed those insights into investigation chains (competitor analysis, citation audits, review triggers).

Most agents don't display the heatmap as a visual; they parse it structurally and respond in natural language: 'Your heatmap shows strength at center but a dead zone to the northeast — likely competitor territory.' This automated reading of heatmap patterns at scale is a core part of modern multi-location local SEO.