Global Population by ZIP code
Get global demographics data at administrative and ZIP code level


Population trends
55 years of historical and future population data at 5-year intervals.
Global coverage
Extensive country coverage, including hard-to-source geographies like China, Japan, Brazil, and Russia.
Accurate ZIP code mapping
Population data is available for ZIP codes and regions, and standardized across geographies.
Our Population Data Samples
| iso | zip code | pop1975 | pop2025 | pop2030 | square_km |
|---|---|---|---|---|---|
| US | 10001 | 20022 | 23542 | 24227 | 1.627949 |
| US | 10002 | 72117 | 84905 | 87546 | 2.129734 |
| US | 10003 | 49926 | 58273 | 60294 | 1.475585 |
| US | 10004 | 3038 | 3555 | 3744 | 1.111534 |
| US | 10005 | 6277 | 7244 | 7459 | 0.160261 |
| US | 10006 | 2338 | 2914 | 2927 | 0.250328 |
| US | 10007 | 6369 | 7433 | 7647 | 0.414131 |
| US | 10009 | 53489 | 62878 | 64796 | 1.597301 |
| US | 10010 | 26503 | 31058 | 32074 | 0.955077 |
| US | 10011 | 44834 | 52722 | 54376 | 1.670544 |
| US | 10012 | 21336 | 25096 | 25858 | 0.838118 |
| US | 10013 | 23764 | 27800 | 28672 | 1.411006 |
| US | 10014 | 27986 | 33036 | 34049 | 1.254836 |
| US | 10016 | 48872 | 57263 | 59442 | 1.418998 |
| US | 10017 | 12851 | 15182 | 15619 | 0.775065 |
| US | 10018 | 4929 | 5918 | 5953 | 0.888766 |
| US | 10019 | 35417 | 41706 | 43062 | 1.697855 |
| US | 10020 | 57 | 71 | 81 | 0.060382 |
| US | 10021 | 38116 | 44796 | 46234 | 0.923488 |
| US | 10022 | 28967 | 34014 | 35045 | 1.140709 |
| US | 10023 | 56022 | 65723 | 67577 | 2.605523 |
| US | 10024 | 53131 | 62383 | 64565 | 2.697514 |
| US | 10025 | 83909 | 98742 | 101632 | 2.302129 |
| US | 10026 | 30553 | 35819 | 37082 | 0.992729 |
| US | 10027 | 51848 | 60853 | 63043 | 2.441945 |
| US | 10028 | 39231 | 46064 | 47671 | 0.819919 |
| US | 10029 | 68273 | 80124 | 82897 | 3.036337 |
| US | 10030 | 24113 | 28184 | 29062 | 0.71811 |
| US | 10031 | 50144 | 58796 | 60670 | 1.733068 |
| US | 10032 | 51784 | 60757 | 62820 | 1.742147 |
| US | 10033 | 46415 | 54473 | 56078 | 1.5448 |
| US | 10034 | 34583 | 40731 | 42114 | 2.659349 |
| US | 10035 | 29220 | 34460 | 35311 | 3.63805 |
| US | 10036 | 22003 | 25781 | 26711 | 1.158341 |
| US | 10037 | 15032 | 17632 | 18235 | 0.602984 |
| US | 10038 | 16992 | 19906 | 20406 | 0.732491 |
| US | 10039 | 20831 | 24588 | 25302 | 0.774862 |
| US | 10040 | 37014 | 43635 | 45038 | 1.468524 |
| US | 10041 | 0 | 1 | 1 | 0.011221 |
| US | 10043 | 181 | 215 | 220 | 0.00536 |
| US | 10044 | 9628 | 11294 | 11717 | 0.595877 |
| US | 10045 | 134 | 142 | 156 | 0.003071 |
| US | 10055 | 2 | 3 | 3 | 0.003453 |
| US | 10060 | 1 | 1 | 0 | 0.000324 |
| US | 10065 | 28159 | 33164 | 34183 | 1.08543 |
| US | 10069 | 3301 | 3880 | 4020 | 0.106198 |
| US | 10075 | 24304 | 28529 | 29534 | 0.489567 |
| US | 10080 | 130 | 396 | 393 | 0.009949 |
| US | 10081 | 220 | 239 | 257 | 0.011246 |
| US | 10090 | 7 | 5 | 7 | 0.005125 |
| ISO | Country | Language | Pop | Level | Type | Name | Region1 | Region2 | Region3 | ISO2 | gpc_id |
|---|---|---|---|---|---|---|---|---|---|---|---|
| NZ | New Zealand | EN | 5291198 | 0 | Country | New Zealand | 20150764 | ||||
| NZ | New Zealand | EN | 572978 | 1 | Regions | Greater Wellington | Greater Wellington | NZ-WGN | 20150834 | ||
| NZ | New Zealand | EN | 111791 | 2 | City Councils | Lower Hutt | Greater Wellington | Lower Hutt | NZ-WGN | 20150836 | |
| NZ | New Zealand | EN | 64836 | 2 | City Councils | Porirua | Greater Wellington | Porirua | NZ-WGN | 20150839 | |
| NZ | New Zealand | EN | 49015 | 2 | City Councils | Upper Hutt | Greater Wellington | Upper Hutt | NZ-WGN | 20150841 | |
| NZ | New Zealand | EN | 59161 | 2 | District councils | Kapiti Coast | Greater Wellington | Kapiti Coast | NZ-WGN | 20150837 | |
| RO | România | RO | 19421307 | 0 | Country | România | 20162574 | ||||
| RO | România | RO | 2340334 | 1 | Regiuni | București - Ilfov | București - Ilfov | 20162575 | |||
| RO | România | RO | 1881190 | 2 | National capital | București | București - Ilfov | București | RO-B | 20162576 | |
| RO | România | RO | 228653 | 3 | Sectoarele municipiului | București - Sectorul 1 | București - Ilfov | București | București - Sectorul 1 | RO-B | 20162577 |
| RO | România | RO | 349283 | 3 | Sectoarele municipiului | București - Sectorul 2 | București - Ilfov | București | București - Sectorul 2 | RO-B | 20162578 |
| TR | Türkiye | TR | 86495428 | 0 | Country | Türkiye | 20178761 | ||||
| TR | Türkiye | TR | 14507346 | 1 | Coğrafi bölgeleri | İç Anadolu | İç Anadolu | 20180346 | |||
| TR | Türkiye | TR | 6351239 | 2 | Büyükşehir Belediyeler | Ankara | İç Anadolu | Ankara | TR-06 | 20180377 | |
| TR | Türkiye | TR | 531703 | 3 | Büyükşehir İlçe Belediyeler | Altındağ | İç Anadolu | Ankara | Altındağ | TR-06 | 20180380 |
| TR | Türkiye | TR | 784115 | 3 | Büyükşehir İlçe Belediyeler | Çankaya | İç Anadolu | Ankara | Çankaya | TR-06 | 20180400 |
| TR | Türkiye | TR | 958632 | 3 | Büyükşehir İlçe Belediyeler | Mamak | İç Anadolu | Ankara | Mamak | TR-06 | 20180462 |
| TR | Türkiye | TR | 466637 | 3 | Büyükşehir İlçe Belediyeler | Yenimahalle | İç Anadolu | Ankara | Yenimahalle | TR-06 | 20180491 |
| TZ | Tanzania | EN | 71370093 | 0 | Country | Tanzania | 20182778 | ||||
| TZ | Tanzania | EN | 3029790 | 1 | Mkoa | Dodoma | Dodoma | TZ-03 | 20182779 | ||
| TZ | Tanzania | EN | 582032 | 2 | Wilayas | Dodoma TC | Dodoma | Dodoma TC | TZ-03 | 20182896 | |
| UA | Ukraine | EN | 38744087 | 0 | Country | Ukraine | 20186415 | ||||
| UA | Ukraine | EN | 2589241 | 1 | Special municipalities | Kyiv | Kyiv | UA-30 | 20187109 | ||
| UA | Ukraine | EN | 2589241 | 2 | Cities | Kyiv | Kyiv | Kyiv | UA-30 | 20187120 | |
| UA | Ukraine | EN | 2589241 | 3 | Municipalities | Kyiv | Kyiv | Kyiv | Kyiv | UA-30 | 66475799 |
| UA | Україна | UK | 38744087 | 0 | Country | Україна | 20186415 | ||||
| US | United States | EN | 343482526 | 0 | Country | United States | 20188365 | ||||
| US | United States | EN | 7210139 | 1 | States | Massachusetts | Massachusetts | US-MA | 20189603 | ||
| US | United States | EN | 266679 | 2 | Counties | Barnstable | Massachusetts | Barnstable | US-MA | 20189604 | |
| US | United States | EN | 162810 | 2 | Counties | Berkshire | Massachusetts | Berkshire | US-MA | 20189605 | |
| US | United States | EN | 658917 | 2 | Counties | Bristol | Massachusetts | Bristol | US-MA | 20189606 | |
| UY | Uruguay | ES | 3415392 | 0 | Country | Uruguay | 20191570 | ||||
| UY | Uruguay | ES | 1350302 | 1 | Departamentos | Montevideo | Montevideo | UY-MO | 20191580 | ||
| ZA | Suid-Afrika | AF | 61664553 | 0 | Country | Suid-Afrika | 20205732 | ||||
| ZA | Suid-Afrika | AF | 16792277 | 1 | Provinsies | Gauteng | Gauteng | ZA-GP | 20205800 | ||
| ZA | Suid-Afrika | AF | 3987683 | 2 | Metropolitaanse Munisipaliteit | Stad Tshwane | Gauteng | Stad Tshwane | ZA-GP | 20205802 | |
| ZA | Suid-Afrika | AF | 84856 | 3 | Main places | Akasia | Gauteng | Stad Tshwane | Akasia | ZA-GP | 60757321 |
| ZA | Suid-Afrika | AF | 87937 | 3 | Main places | Atteridgeville | Gauteng | Stad Tshwane | Atteridgeville | ZA-GP | 60757329 |
| ZA | Suid-Afrika | AF | 205 | 3 | Main places | Babelegi | Gauteng | Stad Tshwane | Babelegi | ZA-GP | 60757298 |
| ZA | South Africa | EN | 139722 | 3 | Main places | Saulsville | Gauteng | City of Tshwane | Saulsville | ZA-GP | 60757314 |
| ZA | South Africa | EN | 545206 | 3 | Main places | Soshanguve | Gauteng | City of Tshwane | Soshanguve | ZA-GP | 60757341 |
| ZA | South Africa | EN | 3395 | 3 | Main places | Soutpan | Gauteng | City of Tshwane | Soutpan | ZA-GP | 60757283 |
| ZA | South Africa | EN | 55759 | 3 | Main places | Stinkwater | Gauteng | City of Tshwane | Stinkwater | ZA-GP | 60757282 |
| ZA | South Africa | EN | 15946 | 3 | Main places | Suurman | Gauteng | City of Tshwane | Suurman | ZA-GP | 60757296 |
| ZA | South Africa | EN | 82998 | 3 | Main places | Temba | Gauteng | City of Tshwane | Temba | ZA-GP | 60757287 |
| ZA | South Africa | EN | 2586 | 3 | Main places | Thembisile | Gauteng | City of Tshwane | Thembisile | ZA-GP | 60757342 |
| ZA | South Africa | EN | 1664 | 3 | Main places | Tierpoort | Gauteng | City of Tshwane | Tierpoort | ZA-GP | 60757300 |
| ZA | South Africa | EN | 4010 | 3 | Main places | Tsebe | Gauteng | City of Tshwane | Tsebe | ZA-GP | 60757286 |
SOLUTIONS
Typical use cases for population by ZIP code
Sales & Marketing
Ad targeting
Market intelligence
Customer analytics
Marketing campaign analysis
Territory mapping
Demand forecasting
Sales territory mapping
Retail site selection
Reporting
Historical population data
Historical, present, and future estimates for 247 countries. Leverage Population Trends which record changes in Population Density, Tracks Migration and Urbanization Patterns with data spanning 55 years.
From 1975 to 2030 with 5 year intervals
Population at ZIP code and administrative level
Optimized for easy integration. Our population data is aggregated following a seamless hierarchical structure starting from the most detailed layer to the broadest.
Highest quality
Our population data results from aggregating reliable census sources with our curated Boundaries Database.
HOW DOES IT WORK
Why choose our Population database?
![]() | Other providers | |
|---|---|---|
| Optimized at all administrative levels | YES | NO |
| Historical and future trends | 55 YEARS | LIMITED |
| Easy customization | YES | LIMITED |
| Ready to use | YES | NO |
| Additional attributes | YES | NO |
Seamless integration in your system
Our self-hosted database is standardized and pre-aggregated by zip code and administrative areas, making it ready to use in your visualization tools.

Rich attributes
Our population by zip code data can be enriched with country-specific information, time zones, multi-language, UNLOCODE, and IATA codes.

Unique expertise
Our team has 15 years of experience in Enterprise integration with an in-depth knowledge of each country's postal structure.
Reduce integration time by 30%
Why choose GeoPostcodes
Global coverage
Complete coverage across 247 countries, including hard-to-source geographies like China, Japan, Brazil, and Russia.
Highest quality
Built on extensive, authoritative sourcing with robust data engineering and quality control. Standardized and up-to-date.
Expert Consulting
With 15 years of experience, we guide your implementation and deliver data in the format that fits your system.
Complete your geodataset

ZIP code database
Global dataset containing all administrative divisions, cities and ZIP codes for 247 countries.

Boundary data
A global data set of polygons representing postal codes and administrative areas.

Address database
International data set with all administrative areas, localities, streets, and zip codes.
Frequently Asked Questions
Historical population estimates depend on the source methodology. GeoPostcodes provides global population data linked to ZIP codes, postal codes, and administrative divisions, with coverage spanning multiple decades. Public datasets—such as those from the United States Census Bureau—use the decennial census or the American Community Survey to generate aggregated demographic data.
Our global dataset fills international gaps using trusted sources and interpolation techniques. You can explore the historical year coverage on our Population Data product page.
Yes. Our dataset includes forward-looking population projections for many regions. While public U.S. datasets such as ACS and Census ZCTAs report historical or current values, our global population dataset provides long-term projections even for countries that do not use ZIP Code Tabulation Areas.
These projections help teams understand how populations evolve across ZIP codes, postal codes, and administrative subdivisions. Learn more in the Population Data product details.
Organizations rely on ZIP-level and postal-code-level population counts for:
- Market sizing
- Demand forecasting
- Demographic segmentation
- Planning and territory optimization
- Site selection and service-area modeling
Analysts often combine demographic indicators—such as median age, household structure, or age ranges—with postal geographies to support planning projects. When working in the U.S., population counts at ZIP level are often compared with ZCTA-based ACS data, keeping in mind that ZCTAs are statistical approximations.
For more on how geography and postal structures support demographic analysis, see Zip Code Analysis.
You can match population values by joining ZIP code or administrative identifiers to geographic shapes such as polygons or shapefiles.
- ZCTAs (from the Census Bureau) are designed for statistical analysis
- USPS ZIP codes are designed for operational routing and do not act as demographic units
Because ZIPs and ZCTAs differ, analysts must choose the correct geography based on whether they prioritize delivery accuracy or demographic precision.
For boundary layers compatible with ZIP-based or admin-based analysis, see our Postal and Administrative Boundaries products.
No. USPS ZIP codes are built for postal routing, not demographic reporting. The Census Bureau creates ZIP Code Tabulation Areas (ZCTAs) to approximate ZIP-based population statistics. ZCTAs may:
- Include areas not served by the USPS
- Exclude boundaries used in routing
- Generalize shapes that don’t correspond to real delivery zones
Because ZIPs and ZCTAs differ, analysts must choose datasets based on their needs: operational routing (ZIPs) or demographic estimation (ZCTAs).
For a clear overview of the differences between postal and statistical geographies, see our guide ZIP Code vs Postcode: Key Differences.
