Key Takeaways
- Buffer, drive-time, and mobility methods each answer a different question. Most analysts combine at least two before committing to a location.
- Drive-time isochrones follow real roads and barriers, which is why they generally outperform simple radius buffers.
- Primary trade areas (a 5 to 10 minute drive or walk) generate roughly 50 to 80 percent of a location's revenue, so that inner ring deserves the most scrutiny.
- U.S. retailers closed about 8,270 stores in 2025 against 5,270 openings, a net loss of roughly 3,000 locations, making disciplined site selection a bigger edge than it used to be.
- Rich POI metadata (category tags, open/close dates, brand affiliation) turns catchment analysis from a one-time snapshot into an ongoing monitoring tool.
Picking a location without solid data is one of the most expensive gambles a retail or real estate team can make. This article breaks down exactly how to run catchment area analysis using point of interest (POI) data, including the three core methods analysts rely on: buffer zones, drive-time isochrones, and mobility-based catchments.
By the end, you’ll have a repeatable process for evaluating any candidate site, not just a single example to admire from a distance.
Why Catchment Area Analysis Matters More Than Ever
Retail real estate has gotten less forgiving. Coresight Research’s year-end tracker counted 8,270 U.S. store closures in 2025 against 5,270 openings, a net loss of around 3,000 locations. That’s actually better than Coresight’s original projection of 15,000 closures for the year, but it still means more retail square footage closed than opened for the fourth year running. For market planning teams, the goal of catchment area analysis is straightforward: confirm a new location can hit or exceed store-level EBITDA targets before signing a lease, not after.
At the same time, the market for the tools that support smarter site decisions keeps expanding. The location intelligence market is valued at roughly $27 to $29 billion in 2026 and is projected to grow at a compound annual rate of 11 to 16 percent through the mid-2030s, according to multiple industry research firms. Retailers, real estate investors, and franchise groups are pouring more budget into location data because a wrong site decision is expensive to unwind, a lease is typically a 5 to 10 year commitment, buildout costs run into six or seven figures, and closing an underperforming location often means eating the remaining lease term.
Catchment area analysis (sometimes called trade area analysis) is how market planning teams de-risk that decision before signing anything. Done well, it answers questions like:
- How much of the target category is already represented in the area, and where are the gaps?
- What’s the realistic day-one market share for a new location?
- Is demand in the trade area durable or is it propped up by one or two anchor tenants?
- How many direct competitors are already operating in the same radius or drive-time zone?
- How accessible is the site by foot, car, or public transit, including parking?
The rest of this guide walks through exactly how to answer those questions using POI data.
How to Run Catchment Area Analysis: 3 Methods, Step by Step
There’s no single “correct” way to draw a catchment area. The right method depends on the format of the business (a coffee shop and a warehouse club have very different realistic trade areas), the data you have access to, and how precise you need the answer to be. Below are the three methods worth knowing, in order of increasing sophistication.
Method 1: Buffer (radius) analysis
A buffer trade area draws a fixed-distance ring, say, 1 mile or 2 kilometers, around a location. It’s the fastest method to stand up and a reasonable starting point for an initial screen.
- Pull your existing store locations (or candidate sites) into a mapping tool.
- Pull competitor and complementary business locations for the same area using a POI dataset such as SafeGraph Places.
- Draw a buffer radius around each location, adjusting the distance based on category (a convenience store buffer looks different from a furniture store buffer).
- Overlay the two layers to see where your trade area overlaps with competitors, and check for cannibalization against your own nearby locations.
The tradeoff: a buffer treats every direction as equally accessible, which rarely reflects reality. A highway, a river, or a gated neighborhood can make a location that’s “close” on a map genuinely hard to reach.
Method 2: Drive-time and walk-time analysis
This method replaces the simple radius with an isochrone, a shape that reflects actual travel time along the real road or path network.
- Start with the same store and competitor location files used in Method 1.
- Load the data into a GIS or BI tool that supports isochrone generation.
- Set your time thresholds based on format. A coffee shop might use 3, 5, and 10-minute walk or drive times; a warehouse club or IKEA-style destination might use 10, 30, and 60-minute drive times, since customers are willing to travel farther for a planned, larger purchase.
- Generate the isochrones and layers in competitor POIs to identify coverage gaps, overlaps, and cannibalization risk.
Because drive-time zones follow actual roads, speed limits, and barriers, they’re a meaningfully more accurate picture of who can realistically reach a site. This is also where the “5 to 10 minute drive typically drives 50 to 80 percent of revenue” benchmark from the Key Takeaways becomes actionable: it tells you exactly which isochrone ring deserves the most scrutiny.
Method 3: Mobility-based catchment analysis
This is the most advanced method, and it defines the catchment based on how people actually move rather than an assumed radius or travel time.
- Start with your store and competitor location files.
- Layer in aggregated mobility data showing visits tied to origin census block groups (CBGs), along with what other brands those same visitors frequent.
- Join the mobility data to your location file to see real visitation patterns rather than theoretical accessibility.
- Enrich further with demographic data (age, income, household composition) to understand who is actually showing up, not just who theoretically could.
Mobility-based catchments are the closest thing to ground truth, because they’re built from observed behavior instead of geographic assumption. They’re also the best method for uncovering surprises, a location might have a small buffer or drive-time footprint on paper but pull disproportionately strong actual visitation because of a nearby transit hub or complementary anchor.
Most experienced market planning teams don’t pick just one of these. A common workflow is to use buffer analysis for a fast first-pass screen across many candidate sites, drive-time analysis to shortlist the top candidates, and mobility data to validate the final one or two choices before signing a lease.
Applying the Methods: An Austin Retail Example
To see this in practice, consider a retailer evaluating two fast-growing Austin neighborhoods for a new location: South Congress, just south of downtown, and Domain, in the city’s northern suburbs.
Plotting SafeGraph Places data into a mapping tool like CARTO produces a visual comparison of the two trade areas, including buffer zones and walk-time distance between POIs. In this example, Domain shows a noticeably higher density of clothing and shoe retailers, while South Congress skews restaurant-heavy. That single observation already changes the calculus: a new restaurant might do better in Domain, where it can capture overflow from a constant stream of shoppers and faces less direct restaurant competition; a new retail boutique might do better in South Congress, surrounded by eateries that extend dwell time and encourage browsing.
But category-level density only tells part of the story. The next layer is sub-category detail: even if an area is saturated with “restaurants,” it might have zero fast-casual Mediterranean concepts, for instance. That gap is where the opportunity actually lives. This is where hovering over individual POIs in the dataset earns its keep, useful attributes to check for each location include:
- POI type and sub-category (full-service restaurant, limited-service restaurant, clothing store, shoe store)
- Brand and store name
- Street address and geolocation
- Opened-on date, which helps gauge how recently an area has grown
- Count of similar POI types within a defined radius, such as 0.5 miles
Now contrast that with a different format: a quick-service restaurant chain comparing two highway-adjacent sites using 5 and 10-minute drive-time isochrones instead of a walking buffer. Because QSR customers are largely drive-up traffic, the buffer method from the Austin walkability example would be the wrong tool here; drive-time isochrones that account for the actual highway exit and traffic pattern are the right one. This is exactly why method selection in Section 2 matters as much as the data itself: the same dataset produces a different, more useful answer depending on which method matches the business format.
Choosing the Right Method for Your Business
A quick way to decide which method (or combination) fits your situation:
- Screening 20+ candidate sites quickly?
Start with buffer analysis to eliminate obvious non-starters. - Comparing a shortlist of 2 to 5 finalists?
Move to drive-time or walk-time isochrones for a realistic accessibility picture. - Making a final go/no-go call on a lease?
Layer in mobility data to validate that real visitation patterns match your assumptions before you sign. - Evaluating a destination format (warehouse clubs, big-box furniture, large-format grocery)?
Weight drive-time more heavily, since customers already expect to travel farther. - Evaluating a convenience or impulse format (coffee, quick-service, convenience stores)?
Weight walk-time and immediate-radius competitor density more heavily.
Enhance Retail Site Selection with SafeGraph Places Data
All three methods above depend on the quality of the underlying POI data. SafeGraph Places currently covers more than 80 million verified places, 15,000+ brands, and 900+ categories globally, with monthly updates and documented sourcing methodology, so opened and closed dates, category tags, and brand affiliations reflect what’s actually on the ground rather than a stale snapshot.
This isn’t just theoretical. Avison Young, a data-driven commercial real estate firm, uses SafeGraph data to give clients ground-level insight into commercial real estate site selection, helping answer the exact questions this guide covers: accessibility, nearby amenities, and how well a location is positioned to reach its target customer base.
If you want to go deeper on any of the three methods above, our guide to calculating catchment areas and trade area analysis overview cover additional technical detail. For teams ready to test this against their own candidate sites, SafeGraph Places is available with flexible pricing based on the rows, columns, and delivery frequency your analysis needs.
Closing Thoughts
Catchment area analysis isn’t a one-time report you run before signing a lease and file away. Retail dynamics shift, competitors open and close, and consumer travel patterns change with new roads, transit lines, and neighborhoods. The teams that treat catchment analysis as an ongoing practice, re-running buffer, drive-time, and mobility checks as an area evolves, are the ones who catch problems (or opportunities) before their competitors do. The method matters, but so does the discipline of revisiting it.
FAQ’s
1. What is catchment area analysis?
Catchment area analysis is the process of evaluating the geographic area from which a business draws the majority of its customers, using it to assess demand, competition, and accessibility before opening or closing a location.
2. What's the difference between buffer, drive-time, and mobility-based catchment areas?
A buffer draws a fixed-distance radius around a location and is fastest to build but ignores real-world barriers. Drive-time (or walk-time) analysis uses isochrones that follow actual roads and travel conditions, making it more accurate for accessibility. Mobility-based analysis uses observed visitation data tied to origin locations, making it the most realistic but also the most data-intensive of the three.
3. Which catchment area method should I use first?
Start with buffer analysis when screening a large list of candidate sites, since it’s the fastest to run. Move to drive-time analysis once you’ve narrowed the list, and use mobility data to validate your top one or two finalists before making a final decision.
4. How does POI data support catchment area analysis?
POI data identifies the businesses, categories, and spatial relationships within a trade area, showing competitor density, category gaps, and how an area has grown or changed over time based on open and close dates.
5. How much of a location's revenue typically comes from its immediate trade area?
Industry benchmarks put primary trade areas, generally a 5 to 10 minute walk or drive, at roughly 50 to 80 percent of a location’s revenue in most retail categories, which is why that inner ring deserves the closest scrutiny.
6. Do I need mobility data, or is drive-time analysis enough?
It depends on the stakes of the decision. Drive-time analysis is usually sufficient for an initial site screen. For a final lease decision, especially on a high-cost or long-term commitment, mobility data adds a layer of validation that theoretical accessibility alone can’t provide.
7. What POI attributes matter most when comparing trade areas?
Category and sub-category tags, brand affiliation, opened and closed dates, geolocation, and the count of similar POI types within a set radius are the attributes most useful for spotting gaps and overlaps between candidate sites.
