The 21 features & 6 themes

LookAlike scores neighborhoods using 21 observable features grouped into 6 thematic clusters. These are the same features used to build each brand’s archetype and to rank resemblance across the continental US.

Each feature is pulled from open US Census data (ACS 2023 5-year estimates) and Overture Maps Places. They are organized into groups so that correlated features—like income, per-capita income, home value, and rent, which all correlate above r = 0.7—count as one theme rather than four independent pieces of evidence. This prevents any single correlated cluster from dominating the similarity distance.

The 21 scored features

Feature	Unit	Group	Meaning
Median household income	$	Income, wealth & housing cost	Typical household earnings in the neighborhood.
Per-capita income	$	Income, wealth & housing cost	Average income per person.
Median home value	$	Income, wealth & housing cost	Typical owner-occupied home value.
Median gross rent	$/mo	Income, wealth & housing cost	Typical monthly rent including utilities.
Poverty rate	%	Income, wealth & housing cost	Share of people below the federal poverty line.
Bachelor’s degree or higher	%	Education	Share of adults 25+ with at least a 4-year degree.
Population density	/km²	Density & transportation	People per square kilometer of land.
Households without a vehicle	%	Density & transportation	Proxy for walkable, transit-served areas.
Walk / bike / transit commute	%	Density & transportation	Share commuting without a private car.
Renter-occupied housing	%	Density & transportation	Share of homes that are rented, not owned.
Median age	yrs	Age & household type	Half the residents are older, half younger.
Average household size	people	Age & household type	Average number of people per household.
Households with children	%	Age & household type	Share of households with someone under 18.
White (non-Hispanic)	%	Race & ethnicity	Share of residents who are white, non-Hispanic.
Hispanic / Latino	%	Race & ethnicity	Share of residents of Hispanic or Latino origin.
Asian (non-Hispanic)	%	Race & ethnicity	Share of residents who are Asian, non-Hispanic.
Places of interest	/km²	Retail & dining context	All Overture points of interest per km²—commercial intensity and walkability.
Grocery stores	/km²	Retail & dining context	Grocery stores and supermarkets per km²—grocer competition and co-location.
Restaurants	/km²	Retail & dining context	Restaurants and fast-food per km².
Cafés & coffee	/km²	Retail & dining context	Coffee shops and cafés per km².
Shopping & apparel	/km²	Retail & dining context	Clothing, department, and shopping-center POIs per km².

Note

These 21 features are the complete set used by the similarity model. They are chosen to be interpretable, fully documented, and traceable to their data source. See data sources for provenance.

The 3 context fields

Three additional fields appear in neighborhood detail panels and reports but are not scored—they are shown for reference only:

Field	Unit	Source	Meaning
Total population	people	US Census ACS 2023	Total residents in the tract.
Black (non-Hispanic)	%	US Census ACS 2023	Share of residents who are Black, non-Hispanic.
Unemployment rate	%	US Census ACS 2023	Share of the labor force that is unemployed.

How features are scaled and weighted

Before a feature enters the similarity distance, all features are:

1. Clipped to the national 1st–99th percentile to prevent a handful of extreme tracts (top-coded incomes, ultra-dense urban cores) from distorting the national scale.
2. Standardized as deviations from the brand’s archetype, using each feature’s own spread so the distance accounts for how tightly the brand clusters on that feature.
3. Given a per-feature variance floor (λ = 0.5): sigma_eff = sqrt(sigma_brand² + (λ · nat_std)²). The floor raises the effective spread of a feature the brand is artificially tight on, so that feature cannot dominate the score; it never reduces the spread of a feature the brand is already wider on than the nation. See how it works for the full explanation.
4. Averaged within each theme, then averaged across all six themes, so each thematic group counts once regardless of how many features it contains.

This is resemblance, not prediction

These features measure observable neighborhood similarity only. They do not predict whether a store will succeed, what revenue it will generate, or whether a location is a good business opportunity. High resemblance to a brand’s archetype means a neighborhood looks like where that brand already operates—nothing more. See what it is for the full honesty statement.

Feature groups explained

Income, wealth & housing cost (5 features) Captures the affluence level and housing cost burden of the neighborhood. These five features are all strongly correlated (r > 0.7) and are grouped to prevent the affluence cluster from dominating the score.

Education (1 feature) Bachelor’s degree or higher is the only education feature; it distinguishes knowledge-economy, professional, and affluent-suburban neighborhoods from others.

Density & transportation (4 features) Distinguishes urban, walkable, transit-served neighborhoods from car-dependent suburban and rural areas. Population density, vehicle ownership, commute mode, and tenure (renter vs. owner) together describe place type and car dependency.

Age & household type (3 features) Captures whether a neighborhood skews older or younger, and whether it has families or is dominated by individuals and couples. These distinguish family-oriented suburbs from young-professional urban neighborhoods and retiree communities.

Race & ethnicity (3 features) Documents neighborhood racial and ethnic composition. These three (White non-Hispanic, Hispanic/Latino, Asian non-Hispanic) represent the largest demographic shares; Black non-Hispanic is tracked as context only.

Retail & dining context (5 features) Commercial density and competitive retail environment, sourced from Overture Maps Places (June 2026 release). “Places of interest” is a catch-all commercial density; the other four measure specific retail clusters that may attract similar customer types or indicate co-location opportunities.

Tip

You can adjust how much weight each theme has in the similarity score using the factor weight sliders in the app. This lets you explore scenarios (e.g., “What if I only cared about affluence?” vs. “What about urbanism?”). See tuning the factors for details.

Data quality notes

• ACS data (demographics): Carries a multi-year lag (2023 estimates are current as of mid-2024) and sampling error, which is worst in small, sparse tracts.
• Overture Maps (retail context): Coverage is comprehensive for large metropolitan areas but thinner in rural regions; some categories may lag reality.
• Store locations (from OpenStreetMap): Good coverage for large national chains, less complete for regional or franchised brands.

See data sources for full lineage and download endpoints.