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Trust & CompanyCoast Pneumatics USA Monthly Store Catchment & ZIP Proximity Dataset
A monthly catchment file: every origin store ZIP mapped to every surrounding ZIP with the distance between them in miles. Three columns, one row per store-to-ZIP pair, ready to join straight into a trade-area model.
This is not a product or pricing dataset. It is the geography layer underneath one — the thing you need before you can answer which stores serve which customers, and which ZIPs no store reaches.
The file already exists, because this pipeline runs every month whether you buy it or not. Most vendors start collecting after you order — which is why they quote a lead time. Here the most recent file is in your account within minutes of payment, with an API key issued at the same time.
What you get, in plain terms
Four things. This is a small, precise file — it does one job and joins to everything else you already have.
Store-to-ZIP distance, precomputed
In the supplied sample one origin store mapped to 50 surrounding ZIPs, from 1.61 to 27.87 miles. No geocoding, no distance API calls, no rate limits.
Joins to anything keyed on ZIP
Three columns and nothing to parse. Attach it to your customer file, your sales file or a demographic layer and the catchment falls out.
Built for trade-area work
Ranking ZIPs by distance from each store is the first step in catchment sizing, territory design and cannibalisation analysis — this file is that step, done.
However you want it
Direct download, REST API, Amazon S3, Google Cloud, Snowflake or SFTP. The API key comes with the dataset.
Fields included in this dataset
All 3 columns, exactly as they appear in the file — taken straight from the sample, not from a brochure. The free sample ships with a data dictionary giving an example value for each one.
Sample rows from the real file
Real rows from the sample file, not an illustration. The free sample is 50 store-to-ZIP pairs with all 3 columns.
Coverage
Refreshed monthly. The supplied sample covers one origin store and the 50 ZIPs around it.
| Metric | In the supplied sample | What it tells you |
|---|---|---|
| Origin stores | 1 | One store's full catchment, in depth |
| Surrounding ZIPs | 50 | Every ZIP within reach of that store |
| Closest ZIP | 1.61 miles | The store's own ZIP |
| Median distance | 20.16 miles | Typical reach for this location |
| Furthest ZIP | 27.87 miles | The outer edge of the catchment as defined |
| Units | Miles | United States geography — see the FAQ |
One store is what the sample shows, not what the feed is limited to. Send your store list and we will build the full matrix — store count multiplied by ZIP radius is what drives the size and the price.
Historical data
Catchments change when stores open and close rather than daily, which is why this is a monthly file. We hold prior months from February 2025 onwards if you need to see how a trade area changed after a new opening.
Ask about historical dataNeed more data points?
We can extend this dataset beyond the standard 3 columns — drive time as well as distance is the most requested addition, along with population and household counts per ZIP, and a nearest-store assignment column.
Request custom fields“We had been calling a distance API in a loop and hitting rate limits every month end. This is the same matrix as a file, and it takes a join instead of a job.”
What people use this dataset for
Site selection teams
Measure how far each store reaches before committing to a new location.
Territory and sales planning
Assign ZIPs to the nearest store without running your own distance calculations.
Catchment analysts
Build trade areas on precomputed distances rather than postcode centroids.
Data teams
Use it as a clean geography layer to join against sales, customer or demographic data.
About store catchment and ZIP proximity data
Catchment analysis starts with one question: how far is each customer ZIP from each store? Answering it yourself means geocoding, a distance API and a loop — which is slow, rate-limited and expensive to rerun. This file is that answer, precomputed and refreshed monthly.
Why a distance matrix is worth buying rather than building
In the supplied sample a single origin store maps to 50 surrounding ZIPs ranging from 1.61 to 27.87 miles. Across a real store estate that is tens of thousands of pairs, recalculated every time a store opens or closes. The calculation is not hard; doing it reliably every month, at scale, without hitting an API quota, is.
What this file is not
It carries no products, no prices and no availability. It is a geography layer. If you need retail pricing or product data to sit on top of it, we run those separately and they join on ZIP or on store identifier.
A note on geography and units
Distances are in miles and the identifiers are United States ZIP codes — the supplied sample is built around a Missouri store. If you need the equivalent for Indian pin-codes in kilometres, that is a different build and we can scope it; tell us the market and we will confirm what exists before you buy anything.
Is collecting this data legal?
Collecting publicly visible product and price information is generally lawful in most jurisdictions. Actowiz collects only public pages, respects robots.txt and platform terms, holds no personal data, and aligns with GDPR and CCPA. We are ISO 9001 and ISO 27001 certified, and this dataset carries documented provenance so your legal team can review the source before you buy.
Frequently asked questions
Your Data Needs
-
Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment. -
Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises. -
US-Based SupportOffices in New York & California. Aligned with your timezone. -
ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.