Home / Convenience stores / Florida / Pinellas County, FL
How many convenience stores exist per person present — residents plus daytime workers — by ZIP code, compared with the U.S. average. Establishment data: Census ZBP 2023. Population: ACS 2024 5-year. 68 ZIP codes analyzed.
Includes: Convenience stores · Corner stores · Small-format food & fuel marts. Counted together because official statistics use one 445120 code.
Why a range: Census does not publish a ZIP-by-industry count when that ZIP has 0–2 businesses. 46 ZIP cells in this county are withheld, 26 of them with residents. Each could hold 0, 1, or 2 businesses: counting them all as 0 gives the low end, counting them all as 2 gives the high end — the data cannot say where the truth sits. The density range uses only the populated cells, since ZIPs without residents are excluded from per-person rates. How ranges work
138 convenience stores are counted in Pinellas County — 1.2 per 10,000 people present (residents + daytime workers), 129% of the U.S. rate of 0.9.
On the comparable ZIP set (ZIPs with a published count in every year), the total has stayed roughly flat between 2015 and 2023 (102 → 104).
Measured supply density is highest in 33714 (Saint Petersburg), 33706 (Saint Petersburg), 33755 (Clearwater) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 34698 (Dunedin), 33781 (Pinellas Park), 33764 (Clearwater).
Census County Business Patterns (2023) puts the county-wide count at 164 establishments, including locations in ZIP codes that do not map to a ZCTA — so it can run slightly above the sum of the ZIP rows on this page. It is annual, so it lags more than QCEW.
Measured density: Pinellas County sits at 129% of the U.S. rate for convenience stores. 26 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 129% and 177% of the U.S. rate. Why. The highest measured density is in 33714 and 33706; the lowest measured density is in 34698 and 33781.
This is a supply-side read: it counts existing convenience stores and the people present, not foot traffic, income, rent, or whether locals want more. Treat low density as a question to check, not an answer.
Pinellas County had an estimated 965,870 residents on July 1, 2024 — 0.6% more than in 2020 (+6,031). In the latest year it lost 1,431 people: births minus deaths −5,051, international migration +7,642, domestic migration −3,973. Components are published separately and may not sum exactly.
484,609 jobs were located in the county in 2022 (+4.6% since 2019), and 439,665 employed residents lived there (+2.1% since 2019). LODES counts wage and salary jobs covered by unemployment insurance plus federal jobs; it excludes the self-employed and military, and counts jobs rather than unique people. 2019→2022 spans the pandemic, so read it as one change, not a trend.
Population and migration say whether the customer base is growing. They do not say whether people want another business of this type. Definitions and limits → · Full county detail →
ACS 5-year estimates for 2015–2019 and 2020–2024. Only ZIPs with at least 2,000 residents in both windows and a change larger than the survey margin of error (90%) are listed — small ZIPs move a lot on paper without a real signal.
Not shown. Census publishes convenience stores only combined with vending machine operators, so a convenience-only nonemployer count is not published.
Census Nonemployer Statistics 2024 (NAICS 2022). We do not approximate this figure.
Each ZIP area is shaded by its density of this business type per person present (residents + workers) vs. the U.S. average. Click any ZIP to zoom to its boundary, see its numbers, and switch to aerial imagery. Individual mapped shops appear as dots at street level — Show locations zooms you in to them (scroll or double-click to zoom).
Map data © OpenStreetMap contributors, tiles by OpenFreeMap.
White dots are ZIP totals from the official Census count — the bigger the dot, the more convenience stores in that ZIP. They are aggregated, not individual shops. Turquoise dots are individual locations mapped in OpenStreetMap, shown only at street level; they are crowd-sourced and indicative, may be incomplete, and do not equal the official counts. Only the official ZIP totals feed the saturation index.
Number of convenience stores in Pinellas County across 2015–2023, on a consistent set of ZIP codes present in every year — so changes are real, not ZIP-boundary artifacts. 2015/2018 are pre-pandemic baselines; 2020/2021 are COVID-distorted.
These ZIPs have far more workers (or visitors) than residents, so their counts are driven by daytime and visitor demand — not a place to read "shortage" from resident numbers alone.
| ZIP | Neighborhood | Type | Residents | Workers | Convenience stores | Index |
|---|---|---|---|---|---|---|
| 33744 | Bay Pines | Destination | 59 | 4,955 | 0–2 | — |
| 33762 | Clearwater | Employment hub | 5,063 | 16,447 | 0–2 | — |
Scroll the table sideways to see all columns.
Click a column header to sort. Index = convenience stores per 10,000 people present (residents + 0.5×workers) ÷ the U.S. average. 100% = exactly average.
| ZIP | Neighborhood | Residents | Workers | Convenience stores | Per 10k | Index | Status |
|---|---|---|---|---|---|---|---|
| 33714 | Saint Petersburg | 19,227 | 4,441 | 13 | 6.1 | 663% | Very high density |
| 33706 | Saint Petersburg | 15,298 | 8,087 | 10 | 5.2 | 566% | Very high density |
| 33785 | Indian Rocks Beach | 4,907 | 1,950 | 3 | 5.1 | 558% | Very high density |
| 33755 | Clearwater | 27,476 | 5,906 | 12 | 3.9 | 432% | Very high density |
| 33707 | Saint Petersburg | 24,324 | 6,153 | 10 | 3.6 | 399% | Very high density |
| 33765 | Clearwater | 13,808 | 12,996 | 7 | 3.4 | 377% | Very high density |
| 33701 | Saint Petersburg | 18,823 | 25,047 | 10 | 3.2 | 349% | Very high density |
| 33705 | Saint Petersburg | 27,939 | 8,241 | 8 | 2.5 | 273% | Very high density |
| 33710 | Saint Petersburg | 33,796 | 11,482 | 9 | 2.3 | 249% | High density |
| 33771 | Largo | 27,596 | 11,252 | 7 | 2.1 | 231% | High density |
| 33702 | Saint Petersburg | 31,294 | 14,576 | 7 | 1.8 | 199% | High density |
| 33711 | Saint Petersburg | 20,013 | 5,716 | 4 | 1.7 | 191% | High density |
| 33712 | Saint Petersburg | 27,142 | 4,209 | 5 | 1.7 | 187% | High density |
| 33759 | Clearwater | 17,557 | 12,588 | 4 | 1.7 | 184% | High density |
| 33703 | Saint Petersburg | 25,264 | 3,902 | 4 | 1.5 | 161% | High density |
| 33770 | Largo | 25,368 | 7,337 | 4 | 1.4 | 151% | High density |
| 33773 | Largo | 16,741 | 14,561 | 3 | 1.2 | 137% | Above average |
| 33756 | Clearwater | 32,201 | 16,836 | 5 | 1.2 | 135% | Above average |
| 34683 | Palm Harbor | 33,911 | 5,813 | 4 | 1.1 | 119% | Above average |
| 33764 | Clearwater | 27,311 | 14,146 | 3 | 0.9 | 95% | Below average |
| 33781 | Pinellas Park | 28,072 | 12,692 | 3 | 0.9 | 95% | Below average |
| 34698 | Dunedin | 38,171 | 10,283 | 3 | 0.7 | 76% | Below average |
| 33704 | Saint Petersburg | 16,232 | 7,144 | 0–2 | — | — | Not published |
| 33708 | Saint Petersburg | 15,060 | 4,723 | 0–2 | — | — | Not published |
| 33709 | Saint Petersburg | 25,050 | 5,758 | 0–2 | — | — | Not published |
| 33713 | Saint Petersburg | 31,729 | 10,318 | 0–2 | — | — | Not published |
| 33715 | Saint Petersburg | 8,032 | 1,149 | 0–2 | — | — | Not published |
| 33716 | Saint Petersburg | 19,328 | 35,140 | 0–2 | — | — | Not published |
| 33729 | Saint Petersburg | — | 2,214 | 0–2 | — | — | Not published |
| 33731 | Saint Petersburg | — | 82 | 0–2 | — | — | Not published |
| 33732 | Saint Petersburg | — | 505 | 0–2 | — | — | Not published |
| 33733 | Saint Petersburg | — | 2,609 | 0–2 | — | — | Not published |
| 33734 | Saint Petersburg | — | 79 | 0–2 | — | — | Not published |
| 33736 | Saint Petersburg | — | 17 | 0–2 | — | — | Not published |
| 33738 | Saint Petersburg | — | 9 | 0–2 | — | — | Not published |
| 33742 | Saint Petersburg | — | 28 | 0–2 | — | — | Not published |
| 33743 | Saint Petersburg | — | 27 | 0–2 | — | — | Not published |
| 33744 | Bay Pines | 59 | 4,955 | 0–2 | — | — | Not published |
| 33747 | Saint Petersburg | — | 4 | 0–2 | — | — | Not published |
| 33757 | Clearwater | — | 10 | 0–2 | — | — | Not published |
| 33758 | Clearwater | — | 166 | 0–2 | — | — | Not published |
| 33760 | Clearwater | 20,501 | 18,276 | 0–2 | — | — | Not published |
| 33761 | Clearwater | 18,290 | 9,454 | 0–2 | — | — | Not published |
| 33762 | Clearwater | 5,063 | 16,447 | 0–2 | — | — | Not published |
| 33763 | Clearwater | 21,146 | 5,274 | 0–2 | — | — | Not published |
| 33766 | Clearwater | — | 25 | 0–2 | — | — | Not published |
| 33767 | Clearwater Beach | 8,081 | 6,265 | 0–2 | — | — | Not published |
| 33772 | Seminole | 23,733 | 5,684 | 0–2 | — | — | Not published |
| 33774 | Largo | 19,799 | 3,631 | 0–2 | — | — | Not published |
| 33775 | Seminole | — | 79 | 0–2 | — | — | Not published |
| 33776 | Seminole | 12,556 | 1,450 | 0–2 | — | — | Not published |
| 33777 | Seminole | 18,582 | 9,140 | 0–2 | — | — | Not published |
| 33778 | Largo | 16,807 | 2,242 | 0–2 | — | — | Not published |
| 33779 | Largo | — | 16 | 0–2 | — | — | Not published |
| 33780 | Pinellas Park | — | 32 | 0–2 | — | — | Not published |
| 33782 | Pinellas Park | 22,461 | 5,283 | 0–2 | — | — | Not published |
| 33784 | Saint Petersburg | — | 17 | 0–2 | — | — | Not published |
| 33786 | Belleair Beach | 1,582 | 121 | 0–2 | — | — | Not published |
| 34660 | Ozona | — | 46 | 0–2 | — | — | Not published |
| 34677 | Oldsmar | 22,762 | 17,692 | 0–2 | — | — | Not published |
| 34681 | Crystal Beach | 1,284 | 110 | 0–2 | — | — | Not published |
| 34682 | Palm Harbor | — | 48 | 0–2 | — | — | Not published |
| 34684 | Palm Harbor | 27,029 | 9,733 | 0–2 | — | — | Not published |
| 34685 | Palm Harbor | 18,132 | 3,443 | 0–2 | — | — | Not published |
| 34688 | Tarpon Springs | 8,522 | 1,102 | 0–2 | — | — | Not published |
| 34689 | Tarpon Springs | 27,324 | 9,232 | 0–2 | — | — | Not published |
| 34695 | Safety Harbor | 18,098 | 6,070 | 0–2 | — | — | Not published |
| 34697 | Dunedin | — | 60 | 0–2 | — | — | Not published |
Scroll the table sideways to see all columns.
The numbers above tell you how the market looks. To find a specific storefront, these marketplaces list available retail space for lease.
Open vacancy data (actual empty units) is only published by some cities; where it exists we show it directly. Listings above are third-party.
Census withholds some ZIP-by-industry cells to protect small businesses. 0–2 means the cell was not published (Census publishes industry counts only at 3+ per ZIP), so the ZIP is left unranked rather than counted as zero. ~ means the count is estimated from the broader industry group and can be off by a business or two.
When a data vintage changes or a section of this report is revised, we post it to a public update feed. No email address, no account, no tracking — and nothing is sent unless something actually changed.
Establishments. U.S. Census ZIP Code Business Patterns (ZBP 2023), NAICS 445120 — convenience retailers. Counts establishments with paid employees. ZBP is published annually with a ~2-year lag.
Fresh county signal. This industry has no clean QCEW series, so the freshest county signal is Census County Business Patterns 2023: 164 establishments and $13M in annual payroll county-wide. This count includes locations in ZIP codes that do not map to a ZCTA, so it can be slightly higher than the sum of the ZIP rows. CBP is annual and slower than QCEW. It cannot be broken down by ZIP.
Population. U.S. Census American Community Survey 2024 5-year ZCTA population (table B01003). Workers are the ZIP's total employment from the same 2023 ZBP file.
Demand ("people present"). Demand comes from residents and daytime workers: residents + 0.5 × workers. This is the single documented assumption in the model and can be tuned.
Index. index = (ZIP establishments ÷ ZIP demand × 10,000) ÷ the U.S. average for this industry (0.91 per 10,000 present). It is a supply-side density measure; it does not capture income, rent, tourism, or whether locals actually want more.
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.