How to map competition density and anchor tenants automatically

A competition map answers two questions at once. Density tells you how saturated an area already is with the brands you compete against, which is the first signal of whether a market has room. The anchor and co-tenant view tells you what sits next to a candidate site, because a grocery anchor, a big-box draw, or a complementary brand next door changes how much traffic a location inherits. Researchers studying retail clusters have found that anchor tenants measurably lift foot traffic to nearby non-anchor stores, which is the practical reason co-tenancy shows up in lease terms and site decisions in the first place.

Doing this by hand means pulling addresses, geocoding them, and eyeballing a map. The automated version pulls the same places from a structured dataset and renders the picture in minutes, so the work shifts from data entry to judgment.

Everything downstream depends on the points of interest you start with. A POI dataset is a list of physical places with names, categories, coordinates, and often store-level attributes. Open sources like Overture Maps publish broad place data you can download in bulk and process yourself, which suits teams with the engineering to handle raw geographic files. Foursquare maintains a large independent place catalog with an open core and commercial tiers. SafeGraph is a places data provider focused on store-level POI attributes and geometry, licensed for teams that want accurate footprints rather than just pins. Google Places, accessed through its API, gives you the freshest names and hours but is built for live lookups rather than bulk analysis, and its pricing is usage-based.

The tradeoff across these sources is coverage versus freshness versus effort. Open data is cheap and broad but needs cleanup. Licensed data arrives more structured and attributed. Live APIs are current but meter every call. Many teams combine an open base layer for breadth with a verified brand list for the competitors that actually matter to their decision.

A raw POI file contains every coffee shop, gym, and dry cleaner in a region, and dumping all of it onto a map produces noise. The useful map comes from a deliberate filter. Decide which brands are direct competitors, which are adjacent, and which categories signal demand without competing. A quick-service burger brand cares about other burger and chicken concepts as direct rivals, treats nearby fast-casual as adjacent, and reads grocery and fitness anchors as demand signals. Filtering by brand name catches the named competitors; filtering by category catches the ones you have not listed yet. Keep the two filters separate so you can layer them, because a category sweep often surfaces a competitor your brand list missed.

There are two honest ways to show density, and they answer different questions. Counts within an area tell you how many competitors fall inside a trade area or hex cell, which is concrete and easy to defend. A kernel density surface, the smooth heatmap, shows where points concentrate and fades between them, which reads well but invites two mistakes. The first is treating a hot spot of many small POIs as if it were saturated by strong competitors, when a heatmap weights every dot the same regardless of store size or sales. The second is reading the smoothed color as precise when it is an interpolation. Use the heatmap to find clusters, then confirm with actual counts and, where you have it, the draw of each location. Watch for duplicate records too, since the same store listed twice will inflate any density measure.

A density map across a whole city is interesting, but the decision turns on what surrounds one candidate site. That means drawing a trade area around the site and asking which anchors and co-tenants fall inside it. A mileage radius is the easy default and the wrong one. A circle counts competitors a customer would never reach because a highway, river, or rail line sits between them, and it ignores ones just outside the line that share the same traffic. A drive-time or walk-time area follows the road network, so the anchors and competitors it includes are the ones that genuinely compete for the same trips. Once the trade area is defined, the overlay becomes specific: this site sits inside a center anchored by a grocer, with two direct competitors and one complementary brand within a ten-minute drive, and the next-nearest cluster is twice that far.

A finished-looking map is where most workflows stop, and it is one step short of a decision. Saturation only matters relative to demand, so the competitor count inside a trade area has to be read against the population and spending inside that same area. A dense corner can still be under-served if demand is denser still, and a quiet corner can be a poor bet if there is little demand to win. The other missing piece is your own network. If a strong-looking site mostly pulls trips from a store you already operate two miles away, the map looks good while the move only shifts sales around. Folding density and co-tenancy into a weighted score, alongside demand and cannibalization, is what converts the picture into a ranking you can take to a committee.

Broker map-makers such as CRE Retail Maps and SitesUSA are built for speed and presentation. They carry libraries of verified brand logos and make clean co-tenancy and anchor callouts for a leasing pitch, and they are inexpensive, but they visualize rather than score. General mapping and GIS tools like Maptive, Maptitude, and eSpatial let you upload a competitor list and generate heatmaps and territory overlays affordably, with the catch that you supply the POI data and do the interpretation. Mobility and POI platforms, including Placer, Echo Analytics, and Foursquare, add footfall, dwell, and cross-visitation on top of place data, which is powerful and costs more. None of these is designed to turn density into a defensible site decision, which is the gap the site-selection category fills.

Geod sits at the decision end of this workflow. Its competition layer maps competitor and anchor POIs inside a drive-time or walk-time trade area around a candidate site, surfaces both density and co-tenancy, and feeds them into an explainable score alongside demand and a gravity-model cannibalization check against your own locations. The output is a ranking and an exportable site brief, not a wall map. Geod is the wrong tool when you want a polished logo presentation map for a single lease pitch, where a broker map-maker is cheaper and faster, or when you want to license raw POI data for your own pipeline, where you should go straight to Foursquare, SafeGraph, or Overture. It also does not sell a standalone footfall panel; it consumes and validates against that kind of data rather than producing it.

Any automated competition map is only as good as the places underneath it, so a few checks pay off. Confirm the vintage of the dataset, because a closed store still plotted as open will distort both density and the anchor picture. De-duplicate records and reconcile brand names, since franchise locations and chains often appear under slightly different labels. Watch geocoding precision, as a POI snapped to a street centroid instead of a rooftop can land on the wrong side of a road and the wrong side of a trade area boundary. Keep the source and date attached to the layer, especially if the map is going in front of a committee that will ask where the competitor list came from.