Robustness & nearby chains

When you click a neighborhood on the map, the detail panel shows two additional signals below the fingerprint visualization: Robustness and Nearby chains. Both help you read neighborhood matches more carefully by revealing what drives or complicates a high score.

Robustness: Is the match weight-dependent?

Robustness tells you whether a neighborhood’s high resemblance rank is stable across different weighting schemes, or whether it hinges on one particular theme.

How it works

The tool re-scores the tract under two conditions:

1. Equal weighting: All 6 feature groups count equally (no tuning via the factor sliders).
2. Omit-one tests: Each of the 6 groups is removed one at a time while the others use default weighting.

Robustness compares these baseline scores against the neighborhood’s current rank (under your chosen weights) and assigns a label:

• Robust (≥ 85th percentile under all conditions): The neighborhood scores high no matter how you weight the groups. It genuinely resembles the brand’s archetype across multiple dimensions.
• Fairly robust (≥ 70th percentile under most conditions, but drops below 70 when one theme is removed): The match holds up across most weightings, but has one theme it depends on. The label tells you which one — for example, “weakest when Density & transportation is dropped (falls to 64th).” If that theme matters to your site selection, this is a good signal; if it does not, look elsewhere.
• Weighting-sensitive (< 70th under all conditions): The neighborhood’s high rank is fragile and leans heavily on a single theme. The label reads: “its high rank leans on [theme] — drop that theme and it falls to [percentile]th.” This is useful context for risk assessment: the match depends on assumptions that may not hold in your analysis.

What it does NOT tell you

Robustness is a measure of internal consistency, not external validity. A tract can be very robust in resemblance to Whole Foods’ archetype but still be a poor site for reasons robustness cannot capture — regulatory environment, real estate availability, competitive saturation outside the 6-km window. Robustness is useful to filter noise from your scoring and to understand what drives each match. Use it alongside the full feature comparison table to decide whether the theme it hinges on matters to you.

Nearby chains: Which tracked brands are close?

Nearby chains shows you competitive density by revealing which of the 11 built-in brands (Trader Joe’s, Whole Foods Market, Sprouts Farmers Market, Chipotle Mexican Grill, Starbucks, Costco Wholesale, Dollar General, Tractor Supply Co., Sweetgreen, H Mart, Cracker Barrel) have a store within 6 kilometers.

How it works

For each tracked brand, the tool finds the nearest store in the OpenStreetMap dataset and calculates the distance to the selected tract’s centroid. Brands within 6 km are listed with their distance rounded to the nearest 0.1 km (or meters if closer than 1 km). If no tracked brands are nearby, the panel reads: “None of the tracked brands have a store within 6 km — a relatively uncontested pocket (among these brands).”

What it does and does not tell you

What it tells you:

• Competitive density among tracked brands: A neighborhood with 4 or 5 of the tracked brands nearby is more saturated than one with none, at least among this sample.
• Whether the brand already competes locally: If you are considering Trader Joe’s and three other tracked chains are within 6 km, the area is already retail-dense and possibly competitive for this type of shopper.

What it does NOT tell you:

• Total competitive threat: The 11 brands in LookAlike are just examples; there may be unlisted competitors (regional chains, independents, mass-market grocers) that are closer or more direct threats.
• Store viability: A sparse competitive zone (no tracked brands nearby) does not mean a store would succeed — it only means those particular brands are not proximate. The area could be rural, low-traffic, or underserved for other reasons.
• Cannibalization risk: Proximity is not the only driver of cannibalization. A Costco 5 km away may compete for a different shopper profile than a Whole Foods. Use compare mode to see which brand more closely matches the neighborhood’s archetype.

Note

The 6 km distance is arbitrary and chosen for tractability. It reflects the approximate distance of a leisurely drive or a difficult public-transit trip in many areas, but your actual catchment may be different. Use nearby chains as one input among several — especially for foot traffic-sensitive retail, walk-shed analysis may be more relevant than 6 km crow-flies distance.

Filtering the ranked list by competition

The nearby-chains readout above describes one selected tract. The same idea is also available as two filters on the whole candidate list, so you can screen for the two most routine triage questions directly:

• At least N km from your own stores. A slider keeps only candidate tracts whose nearest store of the active brand is at least the chosen distance away. Set it above zero to hide neighborhoods so close to an existing location that a new store would cannibalize it. (When no store points are loaded for the brand, the filter keeps the tract rather than guessing.)
• No tracked competitor within 6 km. A toggle keeps only tracts where none of the other tracked brands has a store within 6 km — the “relatively uncontested pocket, among these brands” case. The active brand is not counted as its own competitor.

Each candidate row also carries the distance to the nearest own store and the names of any tracked competitors within 6 km, and both are included as columns in the CSV export, so you can sort or filter further in a spreadsheet.

Proximity counts, not market analysis

These filters count distances between mapped store points. They are not a competition or cannibalization model. “No tracked competitor within 6 km” only means none of the tracked built-in brands is mapped nearby — regional chains, independents, and untracked competitors are invisible to it, and 6 km crow-flies is not a real catchment. Use the filters to narrow a list, then judge competition on the ground.

Using robustness and nearby chains together

A neighborhood that is both robust (independent of weighting) and has no nearby competitors among the tracked brands is a strong triage signal: it resembles the brand across multiple features and sits in a less-contested space. By contrast, a weighting-sensitive match in a densely competitive zone is lower-confidence for site selection purposes, though it might still be worth investigating if the theme it hinges on is central to your strategy.

Remember: both signals are descriptive, not predictive. Resemblance is a necessary condition for site viability, not a sufficient one.