Back to map Methodology

Methodology

This page explains what the map is measuring and where the inputs came from. The project compares McDonald’s and Burger King as a nearest-location problem using a scrape-derived store dataset, public geographic boundaries, and Census population data. It is meant as a simple comparison tool, not an official brand report and not a travel-time model.

Land share
McDonald’s 79.35% Burger King 20.65%
Population weighted
McDonald’s 73.21% Burger King 26.79%
Store count used
McDonald’s 13,737 Burger King 6,577

Overview

The map has two local views. Voronoi cells show store-level nearest-territory polygons derived from the final store set. Census rasterized shows a Census-derived local layer built from block groups assigned to the nearest chain and then rasterized for stable display in the browser.

Those local layers are separate from the zoomed-out state view. The state view is a higher-level summary, while the local layers show the finer territorial patterns.

Store dataset

The map uses a combined store file with 20,314 rows total: 13,737 McDonald’s and 6,577 Burger King locations. That store layer is scrape-derived, so it should be treated as an approximation for comparison rather than an audited census of every store.

Official reported counts for context

McDonald’s reported 13,706 U.S. restaurants at year-end 2025. This map uses 13,737 McDonald’s points, which is 31 above that figure, or about 0.23% high. Burger King’s April 2, 2026 U.S. hiring release described the brand as operating “nearly 6,500” U.S. restaurants. This map uses 6,577 Burger King points, which is slightly above that rough public U.S. figure. RBI’s 2025 annual report gives an exact Burger King count of 7,025 restaurants in the U.S. and Canada, so the Burger King total in this map should be read as a U.S.-focused approximation rather than an official audited U.S. count.

Land share

Land share is computed in the equal-area U.S. projection EPSG:2163 using a 5 km land grid. Each land-cell center is assigned to the nearest chain with a combined haversine BallTree. The final percentages are summed over U.S. land only, not water.

  • Projection: EPSG:2163
  • Distance rule: nearest chain by haversine distance
  • Resolution used for the headline result: 5 km cells
  • Headline result: 79.35% McDonald’s vs 20.65% Burger King
  • Base land geometry: Natural Earth land and Natural Earth states/provinces

Population weighted

Population share uses ACS 2023 5-year total population matched to TIGER 2023 block-group internal points. Each populated block group is assigned to the nearest chain with the same haversine/BallTree nearest-neighbor logic, and the population totals are summed by winner.

  • Surface: ACS 2023 5-year block groups + TIGER 2023 internal points
  • Scope: 50 states plus DC
  • Populated block groups used: 237,662
  • Total population represented: 332,308,259
  • Headline result: 73.21% McDonald’s vs 26.79% Burger King
  • Official data references: ACS 2023 5-year data and TIGER/Line shapefiles 2023

Local layers

Voronoi cells

Store-level polygons clipped to U.S. land. Each store is assigned nearby Census population, then shaded by the density of population assigned to that store.

Census rasterized

Block groups labeled by nearest chain, then rasterized into map tiles so the browser shows a stable local layer instead of trying to draw every block group as live vector geometry.

Caveats

  • This is a nearest-location analysis, not a drive-time or travel-time analysis.
  • The population model assigns each block group’s population to its internal point; it does not model where people are distributed inside the block group.
  • The Census local mode is display-rasterized for performance and stability, so it is based on block groups but is not raw vector block geometry in the browser.
  • The store set is scrape-derived. It is useful for this comparison, but it is not an official brand reporting feed.

Sources