How to read a trade area

A trade area map is one of the most shared screenshots in retail real estate, and one of the least read. People notice the blob, say “it’s bigger than I expected,” and move on. The useful information is in the details: which hexes make the cut, how quickly the share falls off with distance, who lives there, and why the shape looks the way it does.

This post covers how MapDove builds a trade area and how to interpret each part of it.

What the map actually shows

For any place, MapDove models where its visitors come from. It runs the category’s Huff model over the city’s hex grid (cells of roughly 0.8 km²). Each hex’s flow to the store is its demand (population + 0.4 × daytime population) multiplied by the probability that its residents choose this store over every competitor and over the outside option. Divide each flow by the total and you get the hex’s share of the store’s modeled visitors.

The hexes are then ranked by share, and MapDove keeps the smallest set that covers 85% of modeled visitors. Every hex carries both its own share and a cumulative share, so you can see whether the store’s top 10 hexes account for 40% of visitors or 70%.

Why 85% and not 100%? The last 15% is a long, thin tail of cells that each contribute a fraction of a percent. Including them makes the trade area look huge and hides its real core. In the demo dataset, all visitor figures are synthetic, modeled from neighborhood-level parameters, so read the trade area as modeled visitor origins and not as a device trace.

Distance quantiles: the shape of decay

The summary reports two distances:

• Median (p50) distance: half of modeled visitors come from closer than this.
• Average distance: the visitor-weighted mean.

The gap between the two is the diagnostic. When average and median are close, the store pulls evenly from a compact area, which is typical for fuel & convenience or quick service. When the average is well above the median, a small group of distant hexes is carrying weight. Usually that means a destination format such as a shopping center, cinema or department store, or a store at the edge of town whose nearest competitors are far away.

Compare distances against the category’s character. Grocery has a 9 km modeled trade radius and moderate distance decay. A grocery store with a 1.5 km median is a neighborhood store. One with a 5 km median is acting as a regional anchor, and you should ask why.

Segment mix: who, not just where

Each hex has a dominant lifestyle segment, and the summary weights those segments by visitor share. MapDove’s segments include Urban Professionals, Young Renters, Suburban Families, Working Families, Golden Years and Value Seekers, among others.

Three ways to use the mix:

1. Check fit. A premium Urban Loom apparel store drawing 45% from Value Seekers has a positioning problem or a location problem.
2. Compare with the catchment. A 3-mile ring shows who lives around the store. The trade area shows who the model expects to visit. When the two differ, competitors are capturing part of the ring.
3. Find lookalikes. A strong store’s segment mix is a profile you can look for elsewhere. Combine it with the segments docs and suitability weights to find similar territory.

The neighborhood breakdown does the same job in plainer language: “31% Downtown, 18% East Austin” is easier to bring to a leasing meeting than a hex map.

What shapes mean

Shape	Typical cause	What to ask
Compact, roughly circular	Steep decay (fuel, coffee, pharmacy), evenly spread competition	Is the store simply serving its neighborhood?
Lopsided or crescent	A strong competitor on one side takes the near hexes	Is the competitor’s share vulnerable or entrenched?
Elongated along a corridor	Demand concentrated along a linear spine	Dubai’s Sheikh Zayed Road, or I-35 north of Austin
Fragmented, several lobes	Polycentric demand, with employment clusters that compete	Typical of Houston’s multiple centers
Very large, diffuse	Destination category or no nearby competition	Is the store a draw, or simply alone?

A lopsided trade area is the most useful shape for planning. It shows exactly where a competitor is winning, and therefore where a second location, or a relocation, would capture demand the current store can’t reach.

Shapes also have a hard edge. A store only competes for hexes within its category’s trade radius (6 km for coffee, 25 km for shopping centers), so a trade area that runs right up to that radius in one direction usually means demand there is underserved.

Common misreadings

• “Bigger is better.” A large trade area often means weak local density, not strong pull. Look at visits per hex alongside area.
• Treating share as penetration. A hex with 6% of the store’s visitors may still send 90% of its own category trips elsewhere. Share is about the store’s visitor base, not about the hex.
• Ignoring daytime population. Downtown hexes with few residents can rank high because of the 0.4 × daytime weight. That’s correct for coffee and lunch formats and misleading for grocery.

Practical takeaways

• Read cumulative share first. How many hexes make up 50% of visitors tells you how concentrated the store is.
• Use the average-to-median distance gap to tell convenience behavior from destination behavior.
• Put the segment mix next to the ring catchment to see what competitors are taking.
• Treat lopsided shapes as leads for infill or relocation analysis.

Explore any store’s trade area in Trade Areas, and see the trade area docs for field definitions.