Brazil Cities with Latitude & Longitude – Download in Excel, CSV, SQL, JSON, XML
Last update : 23 March 2026.
Here you’ll find a curated sample of 100 key cities from Brazil, each with essential data points such as latitude, longitude, administrative region, and other relevant attributes.
This preview is extracted from our full dataset, which includes a total of 46058 geographic locations across Brazil.
Whether you’re working on mapping, analytics, or app development, the data is available for both personal and commercial use.
All entries can be downloaded in five formats: Excel (.xlsx), CSV, SQL, JSON, and XML.
Capital Highlight: The official capital city of Brazil is Brasília.
| Geoname_ID | City | Alternate_Name | Country_Code | Region | Sub_region | Latitude | Longitude | Elevation | Population | Timezone | Fcode_Name |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 3477942 | Sítio Afonso Bulabauer | Sitio Afonso Bulabauer,Sítio Afonso Bulabauer | BR | Santa Catarina | São Bento do Sul | -26.2307 | -49.3288 | 0 | America/Sao_Paulo | populated place | |
| 7615330 | Bairro Santo Antônio | Bairro Santo Antonio,Bairro Santo Antônio | BR | São Paulo | Mairiporã | -23.3073 | -46.56308 | 0 | America/Sao_Paulo | populated place | |
| 3392355 | Penedo | BR | Maranhão | São Francisco do Maranhão | -6.05 | -43.08333 | 0 | America/Fortaleza | populated place | ||
| 3405960 | Batoque | Batoque,Cajazeiras,Cajazeiros | BR | Ceará | Hidrolândia | -4.4 | -40.36667 | 0 | America/Fortaleza | populated place | |
| 3463081 | Forquílha | BR | Paraná | Palmeira | -25.3 | -50.2775 | 0 | America/Sao_Paulo | populated place | ||
| 3450318 | Santa Cruz | Santa Cruz | BR | Minas Gerais | Virgínia | -22.4 | -45.16667 | 0 | America/Sao_Paulo | populated place | |
| 7775017 | Sítio União | Sitio Uniao,Sítio União | BR | Paraná | Jaboti | -23.69812 | -50.15098 | 0 | America/Sao_Paulo | populated place | |
| 3398401 | Ibimirim | Mirim | BR | Pernambuco | Ibimirim | -8.54056 | -37.69028 | 12272 | America/Recife | populated place | |
| 12432908 | Lago Norte | Lago Norte | BR | Federal District | Brasília | -15.73948 | -47.85686 | 0 | America/Sao_Paulo | populated place | |
| 7740374 | Sítio São José | Sitio Sao Jose,Sítio São José | BR | Paraná | Jacarezinho | -23.21796 | -49.87006 | 0 | America/Sao_Paulo | populated place | |
| 3402751 | Carne de Vaca | BR | Pernambuco | Goiana | -7.58333 | -34.83333 | 0 | America/Recife | populated place | ||
| 3390193 | Rosadia | BR | Piauí | Bom Jesus | -9.11667 | -44.65 | 0 | America/Fortaleza | populated place | ||
| 7777667 | Sítio Boqueirão | Sitio Boqueirao,Sítio Boqueirão | BR | Paraná | Ibaiti | -23.92775 | -50.28468 | 0 | America/Sao_Paulo | populated place | |
| 3664177 | Foz do Embira | BR | Amazonas | Eirunepé | -7.33333 | -70.23333 | 0 | America/Eirunepe | populated place | ||
| 3461907 | Guarani | BR | Mato Grosso | Barão de Melgaço | -17.03333 | -56.11667 | 0 | America/Cuiaba | populated place | ||
| 3467951 | Camburi | Camburi | BR | Espírito Santo | Vitória | -20.26667 | -40.26667 | 0 | America/Sao_Paulo | populated place | |
| 7640929 | Sítio Lucidio C. da Cruz | Sitio Lucidio C. da Cruz,Sítio Lucidio C. da Cruz | BR | Paraná | Bocaiúva do Sul | -25.07371 | -48.82744 | 0 | America/Sao_Paulo | populated place | |
| 7583566 | Sítio Samambaia | Sitio Samambaia,Sítio Samambaia | BR | São Paulo | Vargem Grande Paulista | -23.60045 | -47.0185 | 0 | America/Sao_Paulo | populated place | |
| 3407825 | Alto do Doca | Alto da Doca,Alto do Doca | BR | Maranhão | Amarante do Maranhão | -5.71667 | -46.4 | 0 | America/Fortaleza | populated place | |
| 12542471 | Cepilho | Cepilho | BR | Paraíba | -6.99239 | -35.77293 | 0 | America/Fortaleza | populated place | ||
| 7775180 | Sítio João Brás | Sitio Joao Bras,Sítio João Brás | BR | Paraná | Jaboti | -23.69589 | -50.11317 | 0 | America/Sao_Paulo | populated place | |
| 3470704 | Barra do Itariri | Barra do Itariri | BR | Bahia | Conde | -11.96361 | -37.62832 | 0 | America/Bahia | populated place | |
| 3387419 | Sítio Ôlho d’Água | BR | Paraíba | Boqueirão | -7.6 | -36.13333 | 0 | America/Fortaleza | populated place | ||
| 8431624 | Catequeses | Catequeses | BR | Minas Gerais | Água Boa | -18.09857 | -42.2179 | 0 | America/Sao_Paulo | populated place | |
| 3457357 | Medique | BR | Rio Grande do Sul | Rio Grande | -32.08333 | -52.2 | 0 | America/Sao_Paulo | populated place | ||
| 3467969 | Camboguê | BR | Bahia | Itanagra | -12.38333 | -37.96667 | 0 | America/Bahia | populated place | ||
| 3401595 | Cossó | Casso,Cassó,Cosso,Cossó | BR | Maranhão | Belágua | -3.01667 | -43.35 | 0 | America/Fortaleza | populated place | |
| 7775657 | Sítio Chapecó | Sitio Chapeco,Sítio Chapecó | BR | Paraná | Ibaiti | -23.92249 | -50.15856 | 0 | America/Sao_Paulo | populated place | |
| 3408345 | Aciline | BR | Amapá | Laranjal do Jari | -0.38333 | -52.1 | 0 | America/Belem | populated place | ||
| 3447123 | Tabuleiro | BR | Bahia | Amargosa | -12.98333 | -39.6 | 0 | America/Bahia | populated place | ||
| 3409920 | Cacimba II | BR | Maranhão | Buriti | -3.95722 | -43.03833 | 0 | America/Fortaleza | populated place | ||
| 3390570 | Riachão | BR | Alagoas | Junqueiro | -9.83333 | -36.41667 | 0 | America/Maceio | populated place | ||
| 7773193 | Sítio Boa Sorte | Sitio Boa Sorte,Sítio Boa Sorte | BR | Paraná | Guapirama | -23.42361 | -50.11548 | 0 | America/Sao_Paulo | populated place | |
| 3452811 | Pôrto Domingos | BR | Mato Grosso do Sul | Paranhos | -23.75 | -55.3 | 0 | America/Campo_Grande | populated place | ||
| 8543283 | Vila Pontes | BR | Tocantins | Araguaína | -7.28634 | -48.2819 | 0 | America/Araguaina | populated place | ||
| 3393987 | Nazaré | BR | Maranhão | Grajaú | -5.98333 | -45.98333 | 0 | America/Fortaleza | populated place | ||
| 8477586 | Nova Colômbia | Nova Colombia,Nova Colômbia | BR | São Paulo | Ocauçu | -22.40502 | -49.89425 | 0 | America/Sao_Paulo | populated place | |
| 3404115 | Caieira | Caieira,Caieiras | BR | Maranhão | Caxias | -4.9 | -42.98333 | 0 | America/Fortaleza | populated place | |
| 3447092 | Tacimirim | BR | Bahia | Cairu | -13.58333 | -38.9 | 0 | America/Bahia | populated place | ||
| 7693973 | Sítio Pedro Ruvinski | Sitio Pedro Ruvinski,Sítio Pedro Ruvinski | BR | Paraná | Quitandinha | -25.8108 | -49.4045 | 0 | America/Sao_Paulo | populated place | |
| 3663117 | Onças | BR | Amazonas | Manicoré | -5.91667 | -61.56667 | 0 | America/Manaus | populated place | ||
| 3406041 | Barroquinha | BR | Ceará | Barroquinha | -3.01889 | -41.13611 | 12410 | America/Fortaleza | populated place | ||
| 3410878 | Maçaranduba | BR | Piauí | Cajueiro da Praia | -2.96111 | -41.42639 | 0 | America/Fortaleza | populated place | ||
| 7777616 | Sítio Santo Antônio | Sitio Santo Antonio,Sítio Santo Antônio | BR | Paraná | Ibaiti | -23.93143 | -50.30736 | 0 | America/Sao_Paulo | populated place | |
| 7776934 | Sítio Olaria | Sitio Olaria,Sítio Olaria | BR | Paraná | Carlópolis | -23.556 | -49.7829 | 0 | America/Sao_Paulo | populated place | |
| 3464970 | Currais | Currais | BR | Minas Gerais | Almenara | -16.06667 | -40.81667 | 0 | America/Sao_Paulo | populated place | |
| 3453080 | Ponte Nova | Fazenda Ponte Novo,Ponte Nova | BR | Minas Gerais | Medeiros | -20.05 | -46.13333 | 0 | America/Sao_Paulo | populated place | |
| 3448444 | São Paulo | BR | Bahia | Uauá | -10.1 | -39.48333 | 0 | America/Bahia | populated place | ||
| 7773531 | Sítio Aristides Paulino | Sitio Aristides Paulino,Sítio Aristides Paulino | BR | Paraná | Joaquim Távora | -23.4134 | -49.8625 | 0 | America/Sao_Paulo | populated place | |
| 7775351 | Sítio Marcelo | Sitio Marcelo,Sítio Marcelo | BR | Paraná | Guapirama | -23.52564 | -50.03232 | 0 | America/Sao_Paulo | populated place | |
| 3399958 | Fazenda Pirai | BR | Ceará | Sobral | -3.71667 | -40.45 | 0 | America/Fortaleza | populated place | ||
| 3401663 | Córrego do Meio | BR | Ceará | Jijoca de Jericoacoara | -2.93333 | -40.46667 | 0 | America/Fortaleza | populated place | ||
| 3924751 | Senegal | BR | Acre | Assis Brasil | -10.58333 | -69.96667 | 0 | America/Rio_Branco | populated place | ||
| 3478344 | Sítio Izidoro Gruber | Sitio Izidoro Gruber,Sítio Izidoro Gruber | BR | Paraná | Rio Negro | -26.17966 | -49.52496 | 0 | America/Sao_Paulo | populated place | |
| 3460580 | Itaúna do Sul | Itauna,Itaúna | BR | Paraná | Itaúna do Sul | -22.73056 | -52.88722 | 0 | America/Sao_Paulo | populated place | |
| 3463760 | Estrêla | Estrela,Estrêla,Porto Estrela | BR | Rio Grande do Sul | Camaquã | -31.25 | -51.75 | 0 | America/Sao_Paulo | populated place | |
| 8543868 | Boa Sorte | BR | Pará | Marabá | -5.42303 | -49.27063 | 0 | America/Belem | populated place | ||
| 3665023 | Belo Monte | Bello Monte,Belo Monte | BR | Acre | Feijó | -8.11667 | -70.33333 | 0 | America/Rio_Branco | populated place | |
| 3397411 | Jenipapo | BR | Maranhão | Formosa da Serra Negra | -6.73765 | -46.4296 | 0 | America/Fortaleza | populated place | ||
| 3386238 | Torrões | BR | Paraíba | Poço de José de Moura | -6.6 | -38.48333 | 0 | America/Fortaleza | populated place | ||
| 3408802 | Queimada I | BR | Maranhão | Urbano Santos | -3.22417 | -43.38667 | 0 | America/Fortaleza | populated place | ||
| 3388562 | São Joãzinho | BR | Paraíba | Coxixola | -7.68333 | -36.61667 | 0 | America/Fortaleza | populated place | ||
| 3450735 | Saicã | Saica,Saican,Saicã,Saycan | BR | Rio Grande do Sul | Cacequi | -29.85 | -54.96667 | 0 | America/Sao_Paulo | populated place | |
| 3462360 | Goitizeiro | Goitizeiro | BR | Espírito Santo | Serra | -20.2 | -40.36667 | 0 | America/Sao_Paulo | populated place | |
| 3472495 | Altamira | BR | Bahia | Matina | -13.96667 | -42.81667 | 0 | America/Bahia | populated place | ||
| 3457791 | Marcelo | BR | Bahia | Pindobaçu | -10.81667 | -40.31667 | 0 | America/Bahia | populated place | ||
| 3465138 | Crubixá | Crubixa,Crubixe,Crubixá,Crubixê | BR | Espírito Santo | Alfredo Chaves | -20.65 | -40.85 | 0 | America/Sao_Paulo | populated place | |
| 8622518 | Maloca da Raposa | BR | Roraima | Normandia | 3.81196 | -60.08983 | 0 | America/Boa_Vista | populated place | ||
| 7615376 | Sítio Cachoeirinha | Sitio Cachoeirinha,Sítio Cachoeirinha | BR | São Paulo | Mairiporã | -23.26176 | -46.50759 | 0 | America/Sao_Paulo | populated place | |
| 3465413 | Correia | Correia | BR | Minas Gerais | Cristais | -20.81667 | -45.66667 | 0 | America/Sao_Paulo | populated place | |
| 3467857 | Campinho | Campinho | BR | Bahia | Prado | -17.3 | -39.3 | 0 | America/Bahia | populated place | |
| 3398927 | Gira Mundo | BR | Piauí | Altos | -5.15 | -42.45 | 0 | America/Fortaleza | populated place | ||
| 3472095 | Anta Gorda | BR | Bahia | Iaçu | -12.71667 | -39.9 | 0 | America/Bahia | populated place | ||
| 3478647 | Sítio Eulâmpio Pereira | Sitio Eulampio Pereira,Sítio Eulâmpio Pereira | BR | Santa Catarina | Mafra | -26.1749 | -49.9191 | 0 | America/Sao_Paulo | populated place | |
| 3400420 | Fazenda Arirão | BR | Ceará | Canindé | -4.3 | -39.46667 | 0 | America/Fortaleza | populated place | ||
| 3471778 | Arara | Arara | BR | Bahia | Mucuri | -18.11667 | -39.83333 | 0 | America/Bahia | populated place | |
| 3410839 | Córrego de Dentro | BR | Ceará | Jijoca de Jericoacoara | -2.92278 | -40.52417 | 0 | America/Fortaleza | populated place | ||
| 3468155 | Cajàzeira | BR | Bahia | Ruy Barbosa | -12.31667 | -40.8 | 0 | America/Bahia | populated place | ||
| 3661923 | Tigre | BR | Amazonas | Novo Aripuanã | -6.86667 | -60.3 | 0 | America/Manaus | populated place | ||
| 3394699 | Mocambo | Mocambo,Mocambo (3) | BR | Ceará | Itapipoca | -3.53226 | -39.64872 | 0 | America/Fortaleza | populated place | |
| 11745884 | Groslândia | BR | Mato Grosso | Lucas do Rio Verde | -12.80075 | -56.21799 | 0 | America/Cuiaba | populated place | ||
| 3464960 | Curral das Pedras | BR | Bahia | Rafael Jambeiro | -12.36667 | -39.53333 | 0 | America/Bahia | populated place | ||
| 7700713 | Sítio Inácio Schelbauer | Sitio Inacio Schelbauer,Sítio Inácio Schelbauer | BR | Santa Catarina | Mafra | -26.1548 | -49.9335 | 0 | America/Sao_Paulo | populated place | |
| 7641076 | Sítio João C. Viana | Sitio Joao C. Viana,Sítio João C. Viana | BR | Paraná | Bocaiúva do Sul | -25.17648 | -48.96752 | 0 | America/Sao_Paulo | populated place | |
| 3457066 | Moacir | Moacir | BR | Espírito Santo | Governador Lindenberg | -19.23333 | -40.48333 | 0 | America/Sao_Paulo | populated place | |
| 7615010 | Sítio Comburir | Sitio Comburir,Sítio Comburir | BR | São Paulo | Mairiporã | -23.38001 | -46.64515 | 0 | America/Sao_Paulo | populated place | |
| 3445606 | Valão dos Porcos | Valao dos Porcos,Valão dos Porcos | BR | Rio de Janeiro | São Fidélis | -21.75 | -41.9 | 0 | America/Sao_Paulo | populated place | |
| 3400747 | Esperanca | BR | Pernambuco | Bodocó | -7.86667 | -39.8 | 0 | America/Recife | populated place | ||
| 7555591 | Stio A. Colossal | Stio A. Colossal | BR | São Paulo | Mogi das Cruzes | -23.38763 | -46.16264 | 0 | America/Sao_Paulo | populated place | |
| 3478331 | Sítio Davi A. Filho | Sitio Davi A. Filho,Sítio Davi A. Filho | BR | Paraná | Rio Negro | -26.15168 | -49.569 | 0 | America/Sao_Paulo | populated place | |
| 3398692 | Gravatá | BR | Pernambuco | Parnamirim | -8.15 | -39.93333 | 0 | America/Recife | populated place | ||
| 3409331 | Currais | BR | Maranhão | São Bernardo | -3.38389 | -42.4975 | 0 | America/Fortaleza | populated place | ||
| 7777068 | Sítio João Munhoz | Sitio Joao Munhoz,Sítio João Munhoz | BR | Paraná | Siqueira Campos | -23.6822 | -49.7918 | 0 | America/Sao_Paulo | populated place | |
| 3477758 | Sítio Savio Nogueira | Sitio Savio Nogueira,Sítio Savio Nogueira | BR | Santa Catarina | Campo Alegre | -26.1307 | -49.2895 | 0 | America/Sao_Paulo | populated place | |
| 3401105 | Demétrio Lemos | Boa Esperanca,Bôa Esperança,Demetrio Lemos,Demétrio Lemos | BR | Rio Grande do Norte | Antônio Martins | -6.21667 | -37.9 | 0 | America/Fortaleza | populated place | |
| 3387549 | Sítio Bonito | BR | Rio Grande do Norte | Pedro Avelino | -5.36639 | -36.35639 | 0 | America/Fortaleza | populated place | ||
| 3457257 | Melquíades | BR | Minas Gerais | Jacinto | -15.93333 | -40.41667 | 0 | America/Sao_Paulo | populated place | ||
| 3390782 | Recanto | BR | Ceará | Santana do Acaraú | -3.6 | -40.16667 | 0 | America/Fortaleza | populated place | ||
| 12179442 | Triângulo | Triangulo,Triângulo | BR | Minas Gerais | Sabará | -19.91093 | -43.82288 | 0 | America/Sao_Paulo | populated place | |
| 6318459 | São João do Ivaí | BR | Paraná | São João do Ivaí | -23.98 | -51.81806 | 0 | America/Sao_Paulo | populated place |
Brazil: The Geographer’s Puzzle of Diversity and Scale
Understanding Brazil Requires Precision, Not Generalization
Brazil is more than a nation—it is a geographical symphony. As the fifth-largest country in the world, stretching across time zones and climates, Brazil challenges the geographer not with its complexity alone, but with its subtle spatial rhythms. From the urban sprawl of São Paulo to the remote tranquility of Acre’s forests, each city, municipality, and district forms part of a living map where environment, history, and human ambition converge.
No serious analysis of Brazil can afford to rely on vague generalities. The key lies in data—meticulously structured, geo-referenced, and deeply granular data that reflects the true mosaic of Brazilian territory.
A Nation of Contrasts and Continuity
The Amazon Basin and the Pampas are often presented as opposing images of Brazil, but the geographer sees continuity in these contrasts. Migration flows from North to South, the coastal megacities evolving differently from the rural interiors, and state-level divisions shaping cultural and economic behavior—all these patterns are visible only when one maps every city in relation to its official region and department.
This is where raw geographic beauty meets strategic necessity: understanding how each city is nested within the administrative structure gives insight not only into governance but into Brazil’s infrastructural logic. Cities don’t exist in isolation; they function within federal units, shaped by decisions made at state and municipal levels.
Why City-Level Data in Brazil Is More Than Just Numbers
With over 5,500 municipalities, Brazil is impossible to understand at a macro scale without a clean and consistent microstructure. Whether you're assessing healthcare distribution, educational coverage, logistics planning, or digital infrastructure, the first step is identifying where people live and under which jurisdiction.
We have developed a complete dataset that catalogs every city in Brazil, identifying its region, department (state), and precise geographic coordinates. But the real innovation lies not just in collecting this data—it’s in how it’s delivered.
Excel (.xlsx) Format: Precision Meets Accessibility
The newly added Excel format is a leap forward. For thousands of professionals, from policymakers to GIS beginners, Excel remains the most intuitive and flexible way to engage with structured data. It allows immediate filtering, pivoting, and visualization without any technical barrier.
Want to isolate all cities in the North Region that lie above a certain latitude? Want to compare the density of municipalities per state? With Excel, you’re seconds away from actionable insights. And with the full compatibility of the .xlsx format, this data is ready to plug into models, dashboards, or reports.
Other Formats Available: For Every Technical Ecosystem
Beyond Excel, the database is also provided in CSV for universal raw access, SQL for relational database integration, JSON for web-based applications, and XML for systems requiring hierarchical data structure. The goal is to allow any type of user—from a desktop analyst to a cloud engineer—to work with the data in their preferred environment.
Real-World Applications of the Brazil Dataset
* **Urban Development:** Align public service infrastructure with actual city distributions and growth patterns.
* **Environmental Monitoring:** Map deforestation alerts in proximity to small towns and agricultural frontiers.
* **Disaster Preparedness:** Model risk zones by latitude and proximity to key water bodies or floodplains.
* **Commercial Strategy:** Build geo-targeted logistics networks based on state boundaries and road proximity.
* **Academic Research:** Use structured city-level data to study spatial inequality or population density gradients.
Conclusion
Brazil demands more from geography than most countries. Its vastness can overwhelm, and its diversity can obscure patterns—unless one looks closely, and with structured intent. Mapping Brazil accurately means not just knowing where cities are, but how they belong within regions and departments, and how they relate to each other spatially.
With our updated dataset now available in Excel format—alongside CSV, SQL, JSON, and XML—we provide a gateway into the geographic logic of Brazil. If your work requires precision, scale, and contextual richness, this is the dataset built for that purpose. Dive into Brazil not just with curiosity—but with tools designed to make sense of its vast and beautiful intricacy.
