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Measured per person present — residents plus half of employment — against the U.S. rate for the same industry. Census ZBP 2023, ACS 2024. Only ZIPs with at least 10,000 residents and at least 5 published businesses are ranked; ZIPs that are mainly workplaces are excluded.
What the list actually shows. A single shopping center can hold dozens of apparel stores, and the ZIP around it can have only 10,000–20,000 residents. Divide one by the other and the ZIP looks 10–40 times denser than the U.S. average. That is a real fact about supply, but it describes a destination — an outlet center, a tourist strip, a mall district — not a neighborhood that is short of shops.
This is why the site never turns density into advice. The same arithmetic that ranks 17 apparel clusters at the top would mark a genuinely underserved neighborhood as "low competition", when the reason may be that there is no foot traffic at all.
| # | ZIP | Place | Industry | Businesses | Per 10,000 present | vs. U.S. |
|---|---|---|---|---|---|---|
| 1 | 70116 | New Orleans Orleans Parish, LA |
Bars | 52 | 41.1 | 4071% |
| 2 | 02093 | Wrentham Norfolk County, MA |
Clothing stores | 39 | 25.8 | 3739% |
| 3 | 34222 | Ellenton Manatee County, FL |
Clothing stores | 25 | 15.0 | 2174% |
| 4 | 79835 | Canutillo El Paso County, TX |
Clothing stores | 17 | 13.5 | 1958% |
| 5 | 55301 | Albertville Wright County, MN |
Clothing stores | 18 | 11.6 | 1685% |
| 6 | 92173 | San Ysidro San Diego County, CA |
Clothing stores | 35 | 11.1 | 1613% |
| 7 | 06413 | Clinton Lower Connecticut River Valley Planning Region, CT |
Clothing stores | 15 | 9.9 | 1429% |
| 8 | 33559 | Lutz Hillsborough, FL |
Clothing stores | 21 | 9.5 | 1377% |
| 9 | 43074 | Sunbury Delaware County, OH |
Clothing stores | 18 | 9.3 | 1352% |
| 10 | 98271 | Marysville Snohomish County, WA |
Clothing stores | 34 | 9.1 | 1320% |
| 11 | 11901 | Riverhead Suffolk County, NY |
Clothing stores | 36 | 8.9 | 1288% |
| 12 | 11755 | Lake Grove Suffolk County, NY |
Clothing stores | 13 | 8.8 | 1278% |
| 13 | 33928 | Estero Lee County, FL |
Clothing stores | 33 | 8.6 | 1250% |
| 14 | 01940 | Lynnfield Essex County, MA |
Clothing stores | 14 | 8.6 | 1241% |
| 15 | 17545 | Manheim Lancaster County, PA |
Car washes | 17 | 6.1 | 1239% |
| 16 | 68028 | Gretna Sarpy County, NE |
Clothing stores | 17 | 8.4 | 1213% |
| 17 | 79414 | Lubbock Lubbock County, TX |
Clothing stores | 17 | 8.2 | 1180% |
| 18 | 93203 | Arvin Kern County, CA |
Clothing stores | 18 | 8.0 | 1155% |
| 19 | 60415 | Chicago Ridge Cook County, IL |
Clothing stores | 13 | 8.0 | 1151% |
| 20 | 03031 | Amherst Hillsborough, NH |
Pet care | 10 | 7.1 | 1129% |
Scroll the table sideways to see all columns.
“vs. U.S.” compares the ZIP's density with the national rate for the same industry: 100% is exactly average, 1,000% is ten times the national rate. Counts are published values only; withheld cells (0–2) are not ranked.
Same filters. This is the more useful view: it shows where each category is unusually clustered without letting one industry dominate the list.
| Industry | ZIP | Place | Businesses | Per 10,000 present | vs. U.S. |
|---|---|---|---|---|---|
| Bars | 70116 | New Orleans, LA | 52 | 41.1 | 4071% |
| Clothing stores | 02093 | Wrentham, MA | 39 | 25.8 | 3739% |
| Car washes | 17545 | Manheim, PA | 17 | 6.1 | 1239% |
| Pet care | 03031 | Amherst, NH | 10 | 7.1 | 1129% |
| Convenience stores | 31206 | Macon, GA | 29 | 10.0 | 1093% |
| Auto repair | 91411 | Van Nuys, CA | 26 | 9.3 | 1074% |
| Dentists | 92604 | Irvine, CA | 96 | 31.8 | 949% |
| Nail salons | 94123 | San Francisco, CA | 23 | 7.9 | 933% |
| Gyms | 53575 | Oregon, WI | 19 | 9.4 | 920% |
| Day care | 53216 | Milwaukee, WI | 61 | 18.6 | 918% |
| Grocery stores | 11237 | Brooklyn, NY | 69 | 13.1 | 842% |
| Beauty salons | 95030 | Los Gatos, CA | 27 | 16.6 | 797% |
| Pharmacies | 11366 | Fresh Meadows, NY | 12 | 8.0 | 773% |
| Restaurants | 70116 | New Orleans, LA | 59 | 46.6 | 729% |
| Coffee shops | 94704 | Berkeley, CA | 39 | 13.2 | 628% |
| Fast food | 08736 | Manasquan, NJ | 50 | 30.3 | 454% |
Scroll the table sideways to see all columns.
Density is supply, not demand. A high figure says many businesses of that type share the same resident and worker base. It does not say they are profitable, nor that a new one would fail.
Why clothing leads. Family clothing stores (NAICS 448140) are the most clustered category in the data: a mall can host dozens under one ZIP while the surrounding population stays modest. Coffee shops, restaurants, and salons are spread closer to where people live, so their peak densities are lower.
Why workplaces are excluded. A ZIP with 50,000 jobs and 2,000 residents would otherwise top every list. Those ZIPs are reported separately on each county page as employment hubs and destinations.
What is not counted. Businesses without paid employees are excluded; they are published separately by Census NES and shown beside employer counts on the county pages. Withheld ZIP cells (0–2 businesses) are marked and never ranked.
Sources: U.S. Census ZIP Code Business Patterns 2023, American Community Survey 2024 5-year (table B01003), Census CBP 2023 for the national employment denominator. Methodology →
Enter a ZIP code to see all 16 industries in one place, or browse the national counts.
BizGapMap — free, no-login small-business saturation reports for major U.S. counties. Built entirely from public U.S. Census, BLS, and IRS data (ZBP 2023, ACS 2024 5-year, PEP 2024, LODES, QCEW, IRS SOI). No forecasts, no revenue guesses — withheld cells are marked, and the few derived counts are labelled as estimates.