Who Fills the Google Map Pack? Composition Patterns
You search your own city name plus coffee, look at the map pack, and see a mix of names that does not obviously include you or anyone you would call a peer. Is that normal.
Short answer: in the packs Hillcane recorded, chains are a small minority of pack slots, and independent shops from our own audit fill a meaningful share of the top three, but the mix varies enough by place and time that no single number describes what your own pack will show. This is a dated, place specific snapshot, not a national ranking rule.
This post reports composition from 491 pack slots captured across 66 cities on two dates, September 9, 2026 and September 13, 2026. It shows who filled those slots, how the top three broke down, and how to look at your own city's pack in a way that gives you real information instead of a false sense of where you stand.
Method Box
The data behind this post comes from gbp_maps.csv files stacked across six regional research folders, giving 491 rows marked as live and valid. Those rows span 66 distinct cities and 84 distinct search queries, recorded on two dates: 155 rows from September 9, 2026 and 336 rows from September 13, 2026. Pack positions recorded run from one to six.
The overwhelming majority of the queries used were simple city level searches, phrased as coffee shop in the city name, with only a small number of neighborhood level queries included in the set. Each row records whether the listing was a known chain, whether it belonged to a shop in Hillcane's own 201 shop Southeast audit, and its position in the pack. Rating and review count fields exist in this file but are empty on all 491 rows, so this post says nothing about ratings or review counts among pack members.
This is a snapshot of 66 cities on two dates, not a national statistic, and it should be read that way throughout. A different set of cities, or the same cities checked on a different day, could show a different mix, since Google's local results respond to relevance, distance and prominence signals that shift over time and by location, as Google's own guidance on improving local ranking, reviewed for this post on September 21, 2026, describes in general terms.
It is worth naming what this method does well and what it cannot do. It is well suited to answering a composition question, who filled these slots, in what proportion, across a real, dated sample of cities. It is not suited to answering a causal question, why any specific listing held its position, since the underlying file simply does not carry the fields, like rating or review count, that a causal claim would need to lean on.
Finding One: Chains Are a Small Minority of Pack Slots
Across all 491 recorded pack slots, 14 were identified as belonging to a known chain, against 477 that were not. That puts chains at a small fraction of total pack appearances across this snapshot, a useful fact for any owner who assumes national chains have simply taken over the map pack in most places.
This does not mean chains are absent from local search entirely, and it does not mean every city looks the same. It means that, across the specific 66 cities and two dates covered here, chain listings were the exception in a pack slot, not the rule. An owner competing against a specific nearby chain location should still check their own city directly rather than assuming this national pattern applies exactly to their own market.
Finding Two: The Top Three Tells a Fuller Story
Narrowing to just the top three positions, the ones most visible to someone glancing at a map pack, gives 255 slots across the same snapshots. Of those 255 top three slots, 9 were chains, 94 belonged to shops in Hillcane's own 201 shop Southeast audit, and the remaining 152 were independent shops outside that specific audited set.
That means audited independent shops held roughly a third of top three slots in this snapshot, chains held a small share, and the largest single group was independent shops Hillcane has not specifically audited. The map pack in these snapshots is overwhelmingly an independent business space, not a chain dominated one, even though any individual shop's own pack might feel crowded or unfamiliar when they check it themselves.
That largest group, independent shops outside Hillcane's own audited set, deserves a moment of its own. At 152 of 255 top three slots, it is the single biggest category by a wide margin, bigger than chains and audited independents combined. It is a reminder that any owner's actual competition in the pack is overwhelmingly other independent shops, most of which have not been part of any study at all, rather than either a handful of familiar chains or a set of shops Hillcane happens to have already looked at closely.
Looking at it by distinct pack set rather than by slot, 54 of the 84 distinct query and city combinations recorded had at least one audited independent shop somewhere in their top three. That leaves 30 pack sets where no shop from Hillcane's own audit appeared in the top three, which is a real and useful number for understanding how unevenly this composition is distributed across different cities and queries, rather than assuming every city looks like the aggregate average.
Finding Three: The Snapshot Spans Two Dates and Mostly City Level Queries
Of the 491 recorded rows, 155 came from September 9, 2026 and 336 came from September 13, 2026, giving this post two distinct time points rather than one, though the second date supplies the bulk of the data. Composition figures in this post reflect the combined set across both dates, and a reader should treat any single day's snapshot as one moment rather than a fixed state.
The query set used was heavily weighted toward simple city name queries, phrased as coffee shop in the city, with only a small number of neighborhood specific queries included. This matters because a city level query and a neighborhood level query are genuinely different searches, likely to surface a different mix of nearby businesses, and this snapshot's composition figures describe primarily the city level search behavior rather than a full accounting of how neighborhood searches differ.
What This Does Not Prove
This post reports what filled 491 pack slots across 66 cities on two specific dates. It does not identify what causes a listing to appear in a given position, and it makes no claim about rating, review count, or any other factor's influence on placement, since the rating and review fields in the source file are empty across every row. Any post claiming to know why a specific listing ranked where it did, based on this dataset, would be overstating what the data actually contains.
It also does not describe a national pattern. Sixty six cities is a real, dated sample, but it is not exhaustive, and different regions, city sizes, or search conditions could plausibly produce a different mix. Treat these findings as a description of what Hillcane observed in this specific, dated snapshot, not as a rule that applies everywhere.
What to Do With Each Finding
If your own city's pack looks chain heavy to you at a glance, check it directly rather than assuming based on this post's aggregate finding that chains are rare everywhere. This snapshot found chains held a small share of slots overall, but your specific market could be one of the exceptions, and the only way to know is to look at your own search results.
If you do not see your own shop, or any shop like yours, in your city's top three, remember that in this snapshot 30 of 84 distinct pack sets had no audited independent shop in the top three at all. That is a real, common outcome, not a sign that something is uniquely broken about your shop specifically. It is worth knowing you are not alone in that position before assuming the problem is something only your shop faces.
If you want to check your own pack properly, record the same basics this study did: the exact query text you used, your location setting or the city searched, and the date and time of your search. A pack viewed at a different time of day, from a different starting location, or with a slightly different query wording can genuinely show a different mix, so recording those details turns a one time glance into something you can compare against later.
Why the Pack Varies So Much From City to City
The gap between the aggregate finding, roughly a third of top three slots going to audited independents, and the fact that 30 of 84 pack sets had none at all, is worth explaining rather than glossing over. An average built from 84 different city and query combinations can mean two very different things depending on where you look. Some cities in this snapshot may have had two or even all three top spots held by audited independents, pulling the average up, while other cities had none, pulling it back down.
This unevenness is not a flaw in the data. It reflects something real about how local search works: the mix of businesses competing for a given city's coffee shop query depends entirely on who exists in that city, how many independent shops are there, how many chains have a location nearby, and how each of those businesses has built its own online presence over time. A city with three well established independent roasters will naturally look different from a city with one small independent shop competing against two nearby chain locations.
This is exactly why a national or even a six region aggregate, however carefully computed, cannot tell an individual owner what their own market looks like. The aggregate is useful for correcting a broad misconception, that chains dominate the map pack everywhere, but it cannot substitute for checking your own city's actual result.
Reading Your Own Pack Without Fooling Yourself
The most common mistake an owner makes when checking their own pack is running the search once, forming a conclusion, and stopping there. A single search reflects one moment, one location setting, and often one device. Before drawing any conclusion about where you stand, it is worth repeating the same search a few times across different days and, if possible, from a location setting that matches where a real customer would be searching from.
It also helps to separate two different questions that are easy to blur together: whether you appear in the pack at all for a given query, and whether the pack as a whole looks chain heavy or independent heavy. Those are different diagnoses. A pack full of independent shops that simply does not include yours points toward a visibility problem specific to your own listing. A pack dominated by chains, if your city turns out to be one of the less common cases where that happens, points toward a different kind of competitive landscape entirely, and the response to each situation looks different.
Finally, resist reading too much into a single day's position even once you have confirmed you appear in the pack at all. Local results can shift for reasons that have nothing to do with anything you changed, and a shop showing up in position two one day and position four the next is not necessarily evidence of decline. The steadier, more useful signal comes from checking over weeks, not once.
Related Reading
More from Hillcane on this topic, plus the pages on the site that sit next to the work.
Frequently Asked Questions
How many pack slots does this study cover?
The study covers 491 live, valid pack slots across 66 cities and 84 distinct search queries. The rows were recorded on two dates, 155 rows on September 9, 2026 and 336 rows on September 13, 2026, with pack positions running from one to six. The rows were stacked from gbp_maps.csv files across six regional research folders, so the figures describe that dated snapshot rather than a continuously updated dataset.
What share of the map pack do chains hold?
Very little in this snapshot. Of 491 recorded slots across 66 cities, 14 belonged to known chains and 477 did not. In the top three positions, 9 of 255 slots were chains. The snapshots come from September 9, 2026 and September 13, 2026, so chains were the exception rather than the rule on those dates. A shop facing a specific nearby chain location should still check its own city directly.
How often does an independent shop from Hillcane's own audit appear in the top three?
Fairly often, though unevenly. Of 255 top three slots, 94 belonged to shops in Hillcane's own 201 shop Southeast audit, roughly a third. Counted by distinct pack set instead, 54 of 84 city and query combinations had at least one audited independent in the top three, which leaves 30 sets with none. The largest group, 152 slots, went to independent shops outside that audited set.
What if my own shop's pack has no audited independent shop in the top three?
That is a real and fairly common outcome in this dataset. Thirty of the 84 distinct pack sets recorded had no audited independent shop in their top three at all, so this is not a sign of something uniquely wrong with any one shop; it reflects how unevenly composition is distributed across different cities and queries.
Does this study explain why a listing ranks where it does?
No. This post reports composition, meaning who filled which of the 491 slots, and makes no claim about why a listing held its position. Rating and review count fields exist in the gbp_maps.csv source but are empty on every row, so nothing about ratings or reviews can explain placement here. Google's own local ranking guidance, reviewed September 21, 2026, names relevance, distance and prominence only in general terms.
Is this a national statistic about the Google Maps pack?
No. It describes 66 cities and 84 distinct queries on two dates, September 9, 2026 and September 13, 2026, a real but limited sample of 491 pack slots. Local results respond to relevance, distance and prominence signals that shift by place and over time, so a different set of cities, city sizes, or another day could show a different mix. Read it as a dated snapshot, not a national rule.
What kind of queries were used to capture this data?
Mostly simple city level searches, phrased as coffee shop in the city name, with only a small number of neighborhood level queries in the set of 84. That weighting matters, because a neighborhood query is a different search that can surface a different mix of nearby businesses. The composition figures across the 491 slots therefore describe city level behavior on September 9, 2026 and September 13, 2026, not a full city versus neighborhood comparison.
How can I check my own city's map pack properly?
Record the exact query text used, your location setting or the city searched, and the date and time of your search, the same basics this study recorded. A pack can look different at a different time of day or with slightly different wording, so recording these details lets you compare results meaningfully over time rather than relying on a single glance.
Does Google explain how local ranking works?
Only in general terms. Google's guidance on improving local ranking, reviewed for this post on September 21, 2026, names relevance, distance and prominence as factors in local results. It publishes no specific weights or formulas, and this post adds none. The source file here cannot fill that gap either, since its rating and review count fields are empty on all 491 recorded rows, so it supports composition findings, not causal ones.
Should I expect my own city's composition to match these findings exactly?
No. The findings are an aggregate across 84 city and query combinations in 66 cities, and averages hide wide spread: audited independents held 94 of 255 top three slots overall, yet 30 pack sets had none at all. Your city's mix depends on who exists there and how each business built its presence. Check your own pack on several days, recording the exact query, location setting, and date and time of each check.
Work with Hillcane
You search your own city and coffee and the map pack looks unfamiliar. Here is what actually filled 491 pack slots across 66 cities, and how uneven that composition really is.
If you want help reading your own city's map pack accurately, reach Hillcane at hillcane.co/contact or call (256) 384-2449.
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