Home / Pharmacies & drug stores / Pennsylvania / Allegheny County, PA
How many pharmacies 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. 114 ZIP codes analyzed.
Includes: Pharmacies · Drug stores · Community & compounding pharmacies. Counted together because official statistics use one 446110 code.
Why a range: Census does not publish a ZIP-by-industry count when that ZIP has 0–2 businesses. 86 ZIP cells in this county are withheld, 70 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
126 pharmacies are counted in Allegheny County — 0.8 per 10,000 people present (residents + daytime workers), 79% of the U.S. rate of 1.0.
On the comparable ZIP set (ZIPs with a published count in every year), the total has fallen 8% between 2015 and 2023 (124 → 114). That set is smaller than today's county total. 2020–2021 were pandemic-distorted, and Census rounds counts, so small moves are within noise.
Measured supply density is highest in 15234 (Pittsburgh), 15139 (Oakmont), 15132 (Mckeesport) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 15108 (Coraopolis), 15227 (Pittsburgh), 15217 (Pittsburgh).
Census County Business Patterns (2023) puts the county-wide count at 182 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: Allegheny County sits at 79% of the U.S. rate for pharmacies. 70 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 79% and 166% of the U.S. rate. Why. The highest measured density is in 15234 and 15139; the lowest measured density is in 15108 and 15227.
This is a supply-side read: it counts existing pharmacies 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.
Allegheny County had an estimated 1,231,814 residents on July 1, 2024 — 1.4% fewer than in 2020 (−17,857). In the latest year it gained 1,676 people: births minus deaths −1,693, international migration +8,548, domestic migration −5,192. Components are published separately and may not sum exactly.
712,047 jobs were located in the county in 2022 (−2.4% since 2019), and 582,425 employed residents lived there (−3.9% 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.
Census Nonemployer Statistics counts businesses with no paid employees — a separate universe from the employer establishments counted above; a business is in one or the other, never both. They are not storefronts by definition: many are home-based, mobile, or part-time.
We show them beside the density index, not inside it. In categories such as nail and beauty salons, child care, and pet care, one-person businesses can outnumber employer establishments, so the employer-only count understates how many businesses exist. That is not evidence of higher demand.
Receipts are gross revenue reported for these businesses — not profit, and not the size of the local market. Census publishes this only at county level, so it is not shown on the ZIP map. See methodology.
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 pharmacies 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 pharmacies in Allegheny 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 | Pharmacies | Index |
|---|---|---|---|---|---|---|
| 15222 | Pittsburgh | Employment hub | 5,725 | 54,863 | 7 | 205% |
| 15213 | Pittsburgh | Employment hub | 30,466 | 61,917 | 5 | 79% |
| 15056 | Leetsdale | Employment hub | 1,041 | 2,242 | 0–2 | — |
| 15086 | Warrendale | Destination | 788 | 10,407 | 0–2 | — |
| 15123 | West Mifflin | Destination | 2 | 72 | 0–2 | — |
| 15219 | Pittsburgh | Employment hub | 11,376 | 35,729 | 0–2 | — |
| 15225 | Pittsburgh | Employment hub | 1,085 | 2,887 | 0–2 | — |
| 15233 | Pittsburgh | Employment hub | 2,596 | 6,678 | 0–2 | — |
| 15275 | Pittsburgh | Destination | 86 | 14,032 | 0–2 | — |
Scroll the table sideways to see all columns.
Click a column header to sort. Index = pharmacies per 10,000 people present (residents + 0.5×workers) ÷ the U.S. average. 100% = exactly average.
| ZIP | Neighborhood | Residents | Workers | Pharmacies | Per 10k | Index | Status |
|---|---|---|---|---|---|---|---|
| 15234 | Pittsburgh | 14,014 | 3,532 | 7 | 4.4 | 430% | Very high density |
| 15139 | Oakmont | 6,702 | 3,252 | 3 | 3.6 | 349% | Very high density |
| 15132 | Mckeesport | 18,898 | 5,663 | 5 | 2.3 | 223% | High density |
| 15222 | Pittsburgh | 5,725 | 54,863 | 7 | 2.1 | 205% | High density |
| 15025 | Clairton | 16,939 | 4,349 | 4 | 2.1 | 203% | High density |
| 15215 | Pittsburgh | 11,907 | 5,415 | 3 | 2.1 | 199% | High density |
| 15224 | Pittsburgh | 10,665 | 9,923 | 3 | 1.9 | 186% | High density |
| 15221 | Pittsburgh | 29,374 | 7,950 | 6 | 1.8 | 174% | High density |
| 15235 | Pittsburgh | 33,951 | 9,999 | 7 | 1.8 | 174% | High density |
| 15106 | Carnegie | 19,244 | 7,724 | 4 | 1.7 | 168% | High density |
| 15201 | Pittsburgh | 13,333 | 9,052 | 3 | 1.7 | 163% | High density |
| 15205 | Pittsburgh | 24,060 | 25,757 | 6 | 1.6 | 157% | High density |
| 15146 | Monroeville | 28,332 | 21,714 | 6 | 1.5 | 148% | Above average |
| 15237 | Pittsburgh | 43,588 | 24,830 | 8 | 1.4 | 138% | Above average |
| 15206 | Pittsburgh | 29,056 | 13,514 | 5 | 1.4 | 135% | Above average |
| 15236 | Pittsburgh | 31,313 | 14,397 | 5 | 1.3 | 126% | Above average |
| 15216 | Pittsburgh | 21,471 | 3,797 | 3 | 1.3 | 124% | Above average |
| 15238 | Pittsburgh | 12,868 | 22,426 | 3 | 1.2 | 121% | Above average |
| 15090 | Wexford | 25,763 | 13,495 | 4 | 1.2 | 119% | Above average |
| 15102 | Bethel Park | 30,344 | 8,287 | 4 | 1.2 | 112% | Above average |
| 15210 | Pittsburgh | 25,227 | 1,776 | 3 | 1.1 | 111% | Above average |
| 15136 | Mc Kees Rocks | 22,623 | 7,187 | 3 | 1.1 | 111% | Above average |
| 15212 | Pittsburgh | 27,652 | 33,203 | 5 | 1.1 | 109% | Above average |
| 15101 | Allison Park | 26,036 | 5,012 | 3 | 1.1 | 102% | Above average |
| 15217 | Pittsburgh | 25,795 | 6,310 | 3 | 1.0 | 100% | Above average |
| 15227 | Pittsburgh | 28,460 | 4,199 | 3 | 1.0 | 95% | Below average |
| 15213 | Pittsburgh | 30,466 | 61,917 | 5 | 0.8 | 79% | Below average |
| 15108 | Coraopolis | 42,799 | 44,648 | 5 | 0.8 | 74% | Below average |
| 15006 | Bairdford | 180 | 54 | 0–2 | — | — | Not published |
| 15007 | Bakerstown | 256 | 314 | 0–2 | — | — | Not published |
| 15014 | Brackenridge | 3,095 | 1,239 | 0–2 | — | — | Not published |
| 15015 | Bradfordwoods | 1,291 | 64 | 0–2 | — | — | Not published |
| 15017 | Bridgeville | 17,782 | 12,008 | 0–2 | — | — | Not published |
| 15018 | Buena Vista | 712 | 99 | 0–2 | — | — | Not published |
| 15020 | Bunola | 481 | 81 | 0–2 | — | — | Not published |
| 15024 | Cheswick | 9,325 | 3,635 | 0–2 | — | — | Not published |
| 15030 | Creighton | 1,018 | 491 | 0–2 | — | — | Not published |
| 15031 | Cuddy | 382 | 403 | 0–2 | — | — | Not published |
| 15032 | Curtisville | — | 9 | 0–2 | — | — | Not published |
| 15034 | Dravosburg | 1,643 | 455 | 0–2 | — | — | Not published |
| 15035 | East Mc Keesport | 2,002 | 248 | 0–2 | — | — | Not published |
| 15037 | Elizabeth | 10,417 | 1,813 | 0–2 | — | — | Not published |
| 15044 | Gibsonia | 27,370 | 7,035 | 0–2 | — | — | Not published |
| 15045 | Glassport | 4,368 | 615 | 0–2 | — | — | Not published |
| 15046 | Crescent | 2,141 | 508 | 0–2 | — | — | Not published |
| 15047 | Greenock | 203 | 17 | 0–2 | — | — | Not published |
| 15049 | Harwick | 974 | 18 | 0–2 | — | — | Not published |
| 15051 | Indianola | 329 | 387 | 0–2 | — | — | Not published |
| 15056 | Leetsdale | 1,041 | 2,242 | 0–2 | — | — | Not published |
| 15064 | Morgan | 360 | 183 | 0–2 | — | — | Not published |
| 15065 | Natrona Heights | 10,894 | 4,050 | 0–2 | — | — | Not published |
| 15071 | Oakdale | 12,663 | 3,697 | 0–2 | — | — | Not published |
| 15076 | Russellton | 1,010 | 208 | 0–2 | — | — | Not published |
| 15082 | Sturgeon | 275 | 18 | 0–2 | — | — | Not published |
| 15084 | Tarentum | 9,743 | 3,839 | 0–2 | — | — | Not published |
| 15086 | Warrendale | 788 | 10,407 | 0–2 | — | — | Not published |
| 15088 | West Elizabeth | 344 | 253 | 0–2 | — | — | Not published |
| 15091 | Wildwood | — | 210 | 0–2 | — | — | Not published |
| 15096 | Warrendale | — | 799 | 0–2 | — | — | Not published |
| 15104 | Braddock | 8,034 | 1,427 | 0–2 | — | — | Not published |
| 15110 | Duquesne | 5,149 | 1,129 | 0–2 | — | — | Not published |
| 15112 | East Pittsburgh | 3,655 | 856 | 0–2 | — | — | Not published |
| 15116 | Glenshaw | 14,461 | 2,584 | 0–2 | — | — | Not published |
| 15120 | Homestead | 18,029 | 7,125 | 0–2 | — | — | Not published |
| 15122 | West Mifflin | 18,816 | 11,920 | 0–2 | — | — | Not published |
| 15123 | West Mifflin | 2 | 72 | 0–2 | — | — | Not published |
| 15126 | Imperial | 7,276 | 3,386 | 0–2 | — | — | Not published |
| 15127 | Ingomar | — | 121 | 0–2 | — | — | Not published |
| 15129 | South Park | 10,789 | 696 | 0–2 | — | — | Not published |
| 15131 | Mckeesport | 8,209 | 1,556 | 0–2 | — | — | Not published |
| 15133 | Mckeesport | 6,112 | 222 | 0–2 | — | — | Not published |
| 15134 | Mckeesport | — | 65 | 0–2 | — | — | Not published |
| 15135 | Mckeesport | 4,859 | 505 | 0–2 | — | — | Not published |
| 15137 | North Versailles | 10,043 | 2,886 | 0–2 | — | — | Not published |
| 15140 | Pitcairn | 3,032 | 395 | 0–2 | — | — | Not published |
| 15142 | Presto | 1,513 | 269 | 0–2 | — | — | Not published |
| 15143 | Sewickley | 21,851 | 9,684 | 0–2 | — | — | Not published |
| 15144 | Springdale | 3,771 | 998 | 0–2 | — | — | Not published |
| 15145 | Turtle Creek | 6,695 | 1,843 | 0–2 | — | — | Not published |
| 15147 | Verona | 15,798 | 3,529 | 0–2 | — | — | Not published |
| 15148 | Wilmerding | 2,351 | 891 | 0–2 | — | — | Not published |
| 15202 | Pittsburgh | 19,564 | 3,606 | 0–2 | — | — | Not published |
| 15203 | Pittsburgh | 10,370 | 13,271 | 0–2 | — | — | Not published |
| 15204 | Pittsburgh | 8,467 | 868 | 0–2 | — | — | Not published |
| 15207 | Pittsburgh | 10,961 | 1,567 | 0–2 | — | — | Not published |
| 15208 | Pittsburgh | 10,094 | 2,159 | 0–2 | — | — | Not published |
| 15209 | Pittsburgh | 12,328 | 1,896 | 0–2 | — | — | Not published |
| 15211 | Pittsburgh | 10,728 | 1,208 | 0–2 | — | — | Not published |
| 15214 | Pittsburgh | 14,048 | 1,077 | 0–2 | — | — | Not published |
| 15218 | Pittsburgh | 13,379 | 3,354 | 0–2 | — | — | Not published |
| 15219 | Pittsburgh | 11,376 | 35,729 | 0–2 | — | — | Not published |
| 15220 | Pittsburgh | 17,644 | 17,527 | 0–2 | — | — | Not published |
| 15223 | Pittsburgh | 6,255 | 2,511 | 0–2 | — | — | Not published |
| 15225 | Pittsburgh | 1,085 | 2,887 | 0–2 | — | — | Not published |
| 15226 | Pittsburgh | 13,999 | 2,282 | 0–2 | — | — | Not published |
| 15228 | Pittsburgh | 18,236 | 3,247 | 0–2 | — | — | Not published |
| 15229 | Pittsburgh | 15,188 | 1,991 | 0–2 | — | — | Not published |
| 15230 | Pittsburgh | — | 644 | 0–2 | — | — | Not published |
| 15231 | Pittsburgh | — | 3,200 | 0–2 | — | — | Not published |
| 15232 | Pittsburgh | 11,349 | 9,173 | 0–2 | — | — | Not published |
| 15233 | Pittsburgh | 2,596 | 6,678 | 0–2 | — | — | Not published |
| 15239 | Pittsburgh | 21,410 | 4,456 | 0–2 | — | — | Not published |
| 15240 | Pittsburgh | — | 3,149 | 0–2 | — | — | Not published |
| 15241 | Pittsburgh | 22,294 | 8,502 | 0–2 | — | — | Not published |
| 15243 | Pittsburgh | 13,456 | 4,145 | 0–2 | — | — | Not published |
| 15250 | Pittsburgh | — | 39 | 0–2 | — | — | Not published |
| 15258 | Pittsburgh | — | 5,143 | 0–2 | — | — | Not published |
| 15259 | Pittsburgh | — | 18 | 0–2 | — | — | Not published |
| 15260 | Pittsburgh | 0 | 758 | 0–2 | — | — | Not published |
| 15261 | Pittsburgh | — | 261 | 0–2 | — | — | Not published |
| 15262 | Pittsburgh | — | 2,010 | 0–2 | — | — | Not published |
| 15272 | Pittsburgh | — | 2,574 | 0–2 | — | — | Not published |
| 15275 | Pittsburgh | 86 | 14,032 | 0–2 | — | — | Not published |
| 15276 | Pittsburgh | — | 3,445 | 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 446110 — pharmacies & drug stores. 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: 182 establishments and $153M 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 (1.03 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.