Chipotle vs. CAVA: where the trade areas actually overlap

Chipotle operates 3,929 U.S. stores reaching 270M Americans (80.5%) at a 30-min drive. Trade-area overlap with CAVA (472 stores): 55.0%. All numbers below pulled from the live platform API at 2026-05-14 17:05:35 UTC.

Narrative Geo platform view
Narrative Geo platform view (illustrative). All numbers in this post sourced from the platform's live API at 2026-05-14 17:05:35 UTC.

Chipotle operates 3,929 U.S. stores. At a 30-minute drive radius the network reaches 270M Americans — 80.5% of the U.S. population. Average household income of the reached population: $97,342. Median age: 38.3. Working-age population reached: 166M (61.6% of total reach).

3,929
U.S. Stores
270M
Pop. Reached, 30-min
80.5%
National Coverage
$97.3K
Avg HH Income Reached

Demographic mix of reached population

Age cohort breakdown of the 270M-person trade area at 30-min drive. Numbers are absolute population counts from /api/trade-area.

Trade-area overlap with CAVA

Chipotle: 3,929 U.S. stores reaching 270M Americans. CAVA: 472 U.S. stores reaching 151M. Pairwise overlap computed at 30-min drive isochrone, H3 resolution 7.

55.0%
Overlap (Union %)
149M
Both Chains Reach
120M
Chipotle-only
1.8M
CAVA-only

Union of reachable population: 272M (81.1% of U.S.).
Both-chains reach: 149M — 55.0% of union.
Chipotle-only reach: 120M — population within 30 min of Chipotle but NOT CAVA.
CAVA-only reach: 1.8M — population within 30 min of CAVA but NOT Chipotle.

Quick read

Higher overlap percentage = the two networks compete more directly for the same trade-area population. 55.0% overlap means roughly 55.0% of the jointly-reachable U.S. population can shop either chain within 30 min, which sets the upper bound on customer-share competition between the two.

Methodology

Drive-time matrix. 1.083B routed pairs (origin H3 cell × destination H3 cell × 5/10/15/30/60-min drive). Built on OpenStreetMap U.S. extract via OSRM contraction-hierarchy.

Demographics. U.S. Census ACS 2023 5-year, block-group level, projected onto 12.86M H3 res-8 cells via overlap-weighted area apportionment.

Overlap. overlap_pct = both_pop / union_pop. Computed cell-by-cell with set operations; no smoothing.

What this post does NOT include. Named-MSA whitespace candidates (the platform's whitespace endpoint returns H3 cells with lat/lng, not MSA names). Store-to-store cannibalization inside a single chain (the platform only computes overlap between two distinct chains, not between sibling stores of one brand). Forward-looking unit-growth projections (these come from management guidance, not the platform).

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