1
Who can you reach?
The trade area defines who could realistically visit a site. We build trade areas from actual travel time, not arbitrary radii.
Methodology
How LocationOS answers the three questions—and why you can defend it in committee.
We document everything: data sources, aggregation logic, scoring weights, snapshot dates. No black boxes. When the CFO asks “where did this number come from?”—you have the answer.
Every analysis answers three questions
2
How much demand exists?
Within that trade area: how many people, how much spending power, what's the density? Demographics aggregated to the catchment.
3
How crowded is it?
How many competitors are already serving that trade area? Is there room for another, or is it saturated?
For networks, we add a fourth: What's the impact on your existing stores? Trade area overlap, estimated demand transfer, net new opportunity.
Trade areas: how we define “who can reach”
Travel-time isochrones, not radii
A 10-minute drive isn't a circle. It's shaped by roads, intersections, traffic patterns. We generate real isochrones using road network routing.
Time-of-day aware
Rush hour traffic changes the shape. A site's 10-minute catchment at 8am is different from 2pm or 7pm. We let you specify the time window that matters for your concept.
Multiple rings
Standard output: 5, 10, and 15-minute drive times. Need different thresholds? Configurable by scenario.
Stored and versioned
Every trade area gets a deterministic ID. Reference it later, compare across analyses, audit months after the decision.
What we use: Mapbox Isochrone API with traffic-aware routing.
Demographics: how we measure demand
Source: U.S. Census Bureau
American Community Survey (ACS) 5-year estimates. The standard for population, income, age, household composition, and housing data.
Aggregated to your trade area
Raw Census data is at the block group or tract level. We aggregate to your specific catchment using area-weighted interpolation on an H3 hexagonal grid.
What's included
- Total population
- Total households
- Population density (per sq mi)
- Median household income
- Average household income
- Median age
- Age distribution brackets
- Household size
- Owner vs. renter
- Educational attainment
- Commute patterns
Vintage documented
Every brief shows the ACS vintage (e.g., “ACS 2019-2023”). You know exactly how fresh the data is.
Why we use Census: It's authoritative, consistent, and defensible. Private data vendors model on top of it—we start with the source.
Competition: how we measure saturation
Source: Foursquare Places
13M+ U.S. points of interest with category taxonomy, verified locations, and monthly updates.
Filtered to relevant categories
You're not competing with every business. We filter to your competitive set—QSR, fast casual, coffee, fitness, whatever matches your concept.
Counted and mapped
- Total competitors in trade area
- Competitors per square mile (density)
- Nearest competitors by distance
- List with names and addresses
Supplemented with license data
Where available, we layer in state license records (liquor, food service) for verified operating status. A closed restaurant still in POI data won't inflate your competition count.
Bring your own competitors
In Strategize plans, upload your competitive set. Your intel, your definitions—supplementing or replacing our defaults.
Scoring: how we combine it into a recommendation
Weighted linear model
The score is a weighted sum of components. No neural networks, no hidden layers—arithmetic you can verify.
Default components
- Reach: Population within trade area
- Demand: Income index relative to baseline
- Competition: Inverse of competitor density (more = lower score)
- Accessibility: Trade area size / coverage
Default weights
Reach: 30% | Demand: 30% | Competition: 25% | Accessibility: 15%
Fully transparent
Site scores 74 = Reach (28) + Demand (31) + Competition (-12) + Accessibility (27)
Every component visible. Disagree with the weights? In Strategize, you set your own.
Why linear? Explainability. A linear model can be written on a whiteboard. It survives CFO scrutiny. Complex models score better on benchmarks but die in committee when no one can explain the output.
Cannibalization: how we measure network impact
Trade area overlap
For each existing store, we calculate what percentage of the new site's trade area overlaps with the existing store's trade area.
Overlap visualization
Map showing intersection zones. “34% of Site A's 10-minute catchment overlaps with Store #47's 10-minute catchment.”
Demand transfer estimate
Based on overlap percentage and relative accessibility, we estimate how much of the new site's demand would transfer from existing stores vs. represent net new customers.
Net new calculation
Gross demand − estimated transfer = net new demand.
Site A gross demand: 45,000 population. Estimated transfer from Store #47: 8,100 (18%). Net new: 36,900 (82%).
Conservative assumptions
We default to higher transfer estimates (pessimistic). Better to reject a borderline site than open and cannibalize.
Strategize plan feature
Data freshness: how we keep it auditable
Snapshot timestamps
Every analysis records the data vintage: ACS year, POI update date, isochrone generation time.
Deterministic IDs
Trade areas, snapshots, and analyses get stable identifiers. Reference them in reports, compare across time.
Reproducibility
Run the same inputs six months later—get the same outputs (unless underlying data updated). No stochastic variation.
Methodology in every export
PDF briefs include a methodology section: data sources, aggregation method, scoring weights, snapshot dates.
Why this matters: Deals close months after analysis. Boards review decisions years later. The brief needs to stand on its own.
Current status
Live for all workspaces
- Travel-time trade areas (Mapbox routing)
- Demographics (ACS 5-year estimates)
- Competition (Foursquare POI)
- Explainable scoring with component breakdown
- PDF brief export with methodology
Live for Strategize plans
- Portfolio upload (your locations)
- Custom scoring weights
- Cannibalization analysis
- Batch candidate evaluation
In development
- Nationwide license record coverage (currently partial)
- Traffic count integration
- Foot traffic / mobility data partnerships
- Enhanced daytime population estimates
We'll update this page as capabilities ship.
Need more detail?
We maintain a technical methodology document with data lineage, validation procedures, and calculation specifics.