Brazil cities list with latitude and longitude in CSV,SQL,XML,JSON formats
Last update : 15 January 2025.
This is the best list of 46058 cities in the Brazil available in 4 formats ( CSV, Json, SQL, XML ). We have cleaned up the Geoname database to leave only the towns, capitals and villages. All are all associated with regions and sub-regions (where available).
Each file contains the following data: Geoname_ID, City, Alternate_Name, Country_Code, Region, Sub_region, Latitude, Longitude, Elevation, Population, Timezone, Fcode_Name. See the FAQ below for a detailed explanation.
Here is an example of the data from the Brazil file that you are going to retrieve. The data is displayed here in the form of a table:
Geoname_ID | City | Alternate_Name | Country_Code | Region | Sub_region | Latitude | Longitude | Elevation | Population | Timezone | Fcode_Name |
---|---|---|---|---|---|---|---|---|---|---|---|
3454623 | Pé de Galinha | BR | Bahia | Vitória da Conquista | -14.91667 | -40.86667 | 0 | America/Bahia | populated place | ||
3459258 | Lagoa da Lapinha | BR | Bahia | Lajedinho | -12.45 | -40.96667 | 0 | America/Bahia | populated place | ||
3467074 | Caraíba | BR | Bahia | Marcionílio Souza | -13.15 | -40.75 | 0 | America/Bahia | populated place | ||
6318627 | São Valério do Sul | BR | Rio Grande do Sul | São Valério do Sul | -27.78722 | -53.93694 | 0 | America/Sao_Paulo | populated place | ||
3475083 | Sítio Santa Luzia | Sitio Santa Luzia,Sítio Santa Luzia | BR | São Paulo | Atibaia | -23.08083 | -46.50806 | 0 | America/Sao_Paulo | populated place | |
3395121 | Matinha | BR | Maranhão | Vitorino Freire | -4.16667 | -45.33333 | 0 | America/Fortaleza | populated place | ||
3478856 | Sítio Everaldo | Sitio Everaldo,Sítio Everaldo | BR | Paraná | Mandirituba | -25.87851 | -49.2261 | 0 | America/Sao_Paulo | populated place | |
3468796 | Burietá | Burieta,Burietá,Buris | BR | Bahia | Teolândia | -13.55 | -39.46667 | 0 | America/Bahia | populated place | |
3459589 | José Primo | Jose Primo,José Primo | BR | Minas Gerais | Monte Azul | -15.05 | -42.9 | 0 | America/Sao_Paulo | populated place | |
3390752 | Recreio | BR | Piauí | Isaías Coelho | -7.51667 | -41.71667 | 0 | America/Fortaleza | populated place | ||
3408337 | Açu | Assu | BR | Rio Grande do Norte | Açu | -5.57667 | -36.90861 | 36125 | America/Fortaleza | populated place | |
3407936 | Alice | BR | Amapá | Oiapoque | 3.65 | -51.98333 | 0 | America/Belem | populated place | ||
3389502 | Santa Maria | BR | Rio Grande do Norte | Santana do Matos | -5.83083 | -36.42861 | 0 | America/Fortaleza | populated place | ||
3399725 | Fazendinha | BR | Amazonas | Itacoatiara | -3.5 | -58.83333 | 0 | America/Manaus | populated place | ||
7700196 | Sítio Antônio Dudeque | Sitio Antonio Dudeque,Sítio Antônio Dudeque | BR | Paraná | Lapa | -25.88193 | -49.93329 | 0 | America/Sao_Paulo | populated place | |
11874831 | Piatã | BR | Bahia | Salvador | -12.9454 | -38.38691 | 0 | America/Bahia | populated place | ||
6316879 | Cocal do Sul | BR | Santa Catarina | Cocal do Sul | -28.60111 | -49.32583 | 0 | America/Sao_Paulo | populated place | ||
3473813 | Vendinha | BR | Rio Grande do Sul | Rio Grande | -32.06167 | -52.14333 | 0 | America/Sao_Paulo | populated place | ||
3389154 | Santo Antônio | BR | Piauí | Barras | -4.11667 | -42.56667 | 0 | America/Fortaleza | populated place | ||
3467369 | Capão Bonito | BR | Rio Grande do Sul | Ijuí | -28.26667 | -53.9 | 0 | America/Sao_Paulo | populated place | ||
3409155 | Tamboril | BR | Maranhão | Araioses | -3.08111 | -42.26306 | 0 | America/Fortaleza | populated place | ||
3449630 | Santo Antônio | Ano Nuevo,Fazenda Ano Novo,Fazenda Ano Nuevo,Santo Antonio,Santo Antônio | BR | Mato Grosso do Sul | Rio Brilhante | -21.8 | -54.15 | 0 | America/Campo_Grande | populated place | |
7774485 | Sítio Paraíso | Sitio Paraiso,Sítio Paraíso | BR | Paraná | São Sebastião da Amoreira | -23.52379 | -50.69454 | 0 | America/Sao_Paulo | populated place | |
3455338 | Palmital | BR | Paraná | Mato Rico | -24.65 | -52.26667 | 0 | America/Sao_Paulo | populated place | ||
3478359 | Sítio Victor L. E. John | Sitio Victor L. E. John,Sítio Victor L. E. John | BR | Paraná | Rio Negro | -26.16403 | -49.52689 | 0 | America/Sao_Paulo | populated place | |
3389421 | Santana | BR | Piauí | Dom Inocêncio | -9.06667 | -42.13333 | 0 | America/Fortaleza | populated place | ||
3399982 | Fazenda Pau Ferrado | BR | Pernambuco | Bodocó | -7.35 | -39.93333 | 0 | America/Fortaleza | populated place | ||
6316502 | Barra dAlcântara | BR | Piauí | Barra d’Alcântara | -6.51667 | -42.11444 | 0 | America/Fortaleza | populated place | ||
3452017 | Quilombo | Quilombo | BR | São Paulo | Iacanga | -21.96667 | -49.05 | 0 | America/Sao_Paulo | populated place | |
7775389 | Sítio Neusa | Sitio Neusa,Sítio Neusa | BR | Paraná | Guapirama | -23.50585 | -50.05732 | 0 | America/Sao_Paulo | populated place | |
3388250 | São Paulo | BR | Tocantins | Araguatins | -5.71667 | -48.16667 | 0 | America/Araguaina | populated place | ||
7776897 | Sítio Maurão | Sitio Maurao,Sítio Maurão | BR | Paraná | Joaquim Távora | -23.5045 | -49.9034 | 0 | America/Sao_Paulo | populated place | |
3448490 | São Miguel | Sao Miguel,São Miguel | BR | Minas Gerais | Poços de Caldas | -21.73333 | -46.41667 | 0 | America/Sao_Paulo | populated place | |
7773391 | Sítio Adelício Bonfa | Sitio Adelicio Bonfa,Sítio Adelício Bonfa | BR | Paraná | Santo Antônio da Platina | -23.39688 | -49.98815 | 0 | America/Sao_Paulo | populated place | |
7773771 | Sítio Maria Bpina | Sitio Maria Bpina,Sítio Maria Bpina | BR | Paraná | Carlópolis | -23.46861 | -49.79838 | 0 | America/Sao_Paulo | populated place | |
3395133 | Mata Verde | BR | Alagoas | Pindoba | -9.48333 | -36.35 | 0 | America/Maceio | populated place | ||
6318657 | Senador Rui Palmeira | BR | Alagoas | Senador Rui Palmeira | -9.46639 | -37.45694 | 0 | America/Maceio | populated place | ||
3465157 | Cristianópolis | BR | Goiás | Cristianópolis | -17.19917 | -48.70583 | 0 | America/Sao_Paulo | populated place | ||
7700356 | Sítio Antônio J. E. Portela | Sitio Antonio J. E. Portela,Sítio Antônio J. E. Portela | BR | Paraná | Rio Negro | -25.96805 | -49.84793 | 0 | America/Sao_Paulo | populated place | |
7692294 | Sítio João Veiga L. Pinto | Sitio Joao Veiga L. Pinto,Sítio João Veiga L. Pinto | BR | Paraná | Tijucas do Sul | -25.87193 | -48.99559 | 0 | America/Sao_Paulo | populated place | |
3456164 | Nova Granada | Novo Granaoa | BR | São Paulo | Nova Granada | -20.53389 | -49.31417 | 15715 | America/Sao_Paulo | populated place | |
3446938 | Tanquinho | Tanquinho,Tanquiuho | BR | Bahia | Ibitiara | -12.56667 | -42.25 | 0 | America/Bahia | populated place | |
12034802 | Grota de Areia | BR | Tocantins | Nazaré | -6.39119 | -47.64064 | 0 | America/Araguaina | populated place | ||
7776321 | Sítio Bela Vista | Sitio Bela Vista,Sítio Bela Vista | BR | Paraná | Quatiguá | -23.5605 | -49.9302 | 0 | America/Sao_Paulo | populated place | |
3400220 | Fazenda Espírito Santo | BR | Ceará | Quixadá | -4.93333 | -39.05 | 0 | America/Fortaleza | populated place | ||
7694426 | Sítio Ari Dibas | Sitio Ari Dibas,Sítio Ari Dibas | BR | Paraná | Quitandinha | -25.7977 | -49.3774 | 0 | America/Sao_Paulo | populated place | |
3464303 | Embu-Mirim | Embu-Mirim | BR | São Paulo | Cruzeiro | -22.53333 | -45.08333 | 0 | America/Sao_Paulo | populated place | |
3395440 | Marapanim | BR | Pará | Marapanim | -0.7175 | -47.69972 | 10236 | America/Belem | populated place | ||
3458554 | Limoeiro | BR | Bahia | Feira de Santana | -12.31667 | -38.9 | 0 | America/Bahia | populated place | ||
3453213 | Ponta d’Água | BR | Tocantins | Peixe | -11.91667 | -48.85 | 0 | America/Araguaina | populated place | ||
3460180 | Jambeiro | BR | São Paulo | Jambeiro | -23.25361 | -45.68778 | 0 | America/Sao_Paulo | populated place | ||
12034917 | Santa Paz | BR | Maranhão | São João do Paraíso | -6.44393 | -46.91434 | 0 | America/Fortaleza | populated place | ||
7739230 | Sítio São José | Sitio Sao Jose,Sítio São José | BR | Paraná | Bandeirantes | -23.1365 | -50.2441 | 0 | America/Sao_Paulo | populated place | |
11959882 | Três Barras | Tres Barras,Três Barras | BR | Santa Catarina | Palhoça | -27.89969 | -48.65525 | 0 | America/Sao_Paulo | populated place | |
3397199 | Juá | BR | Pernambuco | Tupanatinga | -8.41667 | -37.36667 | 0 | America/Recife | populated place | ||
8470079 | Cruzeiro | BR | Federal District | Brasília | -15.79473 | -47.92794 | 0 | America/Sao_Paulo | populated place | ||
7700839 | Sítio Abel Schultz | Sitio Abel Schultz,Sítio Abel Schultz | BR | Santa Catarina | Mafra | -26.1077 | -49.8642 | 0 | America/Sao_Paulo | populated place | |
7583503 | Sítio Tapera | Sitio Tapera,Sítio Tapera | BR | São Paulo | Cotia | -23.74846 | -47.02395 | 0 | America/Sao_Paulo | populated place | |
3393243 | Palmeira do Norte | BR | Maranhão | Aldeias Altas | -4.4 | -43.51667 | 0 | America/Fortaleza | populated place | ||
3479860 | Sítio Ladislau P. Olavo | Sitio Ladislau P. Olavo,Sítio Ladislau P. Olavo | BR | Paraná | Campo do Tenente | -25.9975 | -49.71083 | 0 | America/Sao_Paulo | populated place | |
3397090 | Junco | BR | Ceará | Cariús | -6.55 | -39.4 | 0 | America/Fortaleza | populated place | ||
6316406 | Aparecida de Goiânia | Aparesida de Gojanija,Aparesida di Gojanija,Aparesida-di-Gojanija,a pa lei xi da di ge ya ni ya,aparesida de go’iyaniya,Апаресида де Гојанија,Апаресида ди Гояния,Апаресида-ди-Гояния,আপারেসিডা ডে গোইয়ানিয়া,アパレシダ・デ・ゴイアニア,阿帕雷西達迪戈亞尼亞 | BR | Goiás | Aparecida de Goiânia | -16.82333 | -49.24389 | 510770 | America/Sao_Paulo | populated place | |
3471503 | Aristeu | BR | Minas Gerais | Arinos | -15.93333 | -46.2 | 0 | America/Sao_Paulo | populated place | ||
3391660 | Poço do Cachorro | BR | Paraíba | Pedra Branca | -7.51667 | -38.05 | 0 | America/Fortaleza | populated place | ||
3404842 | Brejo do Tucano | BR | Piauí | São João da Varjota | -7.01667 | -41.73333 | 0 | America/Fortaleza | populated place | ||
7739523 | Sítio Pedro Rui | Sitio Pedro Rui,Sítio Pedro Rui | BR | Paraná | Cambará | -23.0589 | -50.1535 | 0 | America/Sao_Paulo | populated place | |
3400910 | Ema | BR | Pernambuco | Serra Talhada | -8.28333 | -38.38333 | 0 | America/Recife | populated place | ||
3455260 | Panelas | BR | Bahia | Cardeal da Silva | -12.01667 | -37.91667 | 0 | America/Bahia | populated place | ||
3390130 | Sabonete | BR | Piauí | Isaías Coelho | -7.71667 | -41.73333 | 0 | America/Fortaleza | populated place | ||
3471146 | Baianos | BR | Paraná | Ubiratã | -24.41667 | -52.98333 | 0 | America/Sao_Paulo | populated place | ||
3458093 | Malhador | BR | Sergipe | Malhador | -10.65778 | -37.30472 | 5574 | America/Maceio | populated place | ||
3397301 | João França | BR | Pará | Prainha | -2.25 | -53.58333 | 0 | America/Santarem | populated place | ||
7775970 | Sítio João Pereira | Sitio Joao Pereira,Sítio João Pereira | BR | Paraná | Arapoti | -23.98893 | -50.12543 | 0 | America/Sao_Paulo | populated place | |
3466918 | Carlos Pinheiro | Carlos Pinheiro | BR | Bahia | Guaratinga | -16.53333 | -39.91667 | 0 | America/Bahia | populated place | |
7614723 | Sítio São José | Sitio Sao Jose,Sítio São José | BR | São Paulo | Mairiporã | -23.2425 | -46.5696 | 0 | America/Sao_Paulo | populated place | |
3457402 | Matoso | BR | Mato Grosso do Sul | Aral Moreira | -22.9 | -55.46667 | 0 | America/Campo_Grande | populated place | ||
3389787 | Salto da Pedra | BR | Piauí | Brasileira | -4.05 | -41.75 | 0 | America/Fortaleza | populated place | ||
3398148 | Ipané | BR | Maranhão | Alto Parnaíba | -9.55 | -46.15 | 0 | America/Fortaleza | populated place | ||
3468357 | Cacimbinhas | Cacimbinha,Cacimbinhas | BR | Mato Grosso do Sul | Ponta Porã | -21.83333 | -55.71667 | 0 | America/Campo_Grande | populated place | |
7555386 | Bairro Cafundo | Bairro Cafundo | BR | São Paulo | Santa Isabel | -23.3564 | -46.235 | 0 | America/Sao_Paulo | populated place | |
3392139 | Piedade | BR | Pernambuco | Jaboatão dos Guararapes | -8.18333 | -34.91667 | 0 | America/Recife | populated place | ||
3468091 | Caldeirão | BR | Bahia | Santa Inês | -13.31667 | -39.81667 | 0 | America/Bahia | populated place | ||
7773164 | Sítio João Carmelino | Sitio Joao Carmelino,Sítio João Carmelino | BR | Paraná | Santo Antônio da Platina | -23.38036 | -50.08786 | 0 | America/Sao_Paulo | populated place | |
3449163 | São Francisco | Sao Francisco,São Francisco | BR | Minas Gerais | Delfim Moreira | -22.6 | -45.3 | 0 | America/Sao_Paulo | populated place | |
7776127 | Sítio Miguel L. da Costa | Sitio Miguel L. da Costa,Sítio Miguel L. da Costa | BR | Paraná | Pinhalão | -23.82132 | -50.05389 | 0 | America/Sao_Paulo | populated place | |
7616974 | Sítio Martins | Sitio Martins,Sítio Martins | BR | São Paulo | Eldorado | -24.5591 | -48.409 | 0 | America/Sao_Paulo | populated place | |
3472215 | Ângelo | Angelo,Ângelo | BR | Minas Gerais | Uberaba | -19.38333 | -48.33333 | 0 | America/Sao_Paulo | populated place | |
3392033 | Pintadinha | BR | Paraíba | Camalaú | -7.93333 | -36.66667 | 0 | America/Fortaleza | populated place | ||
3468292 | Café Ralo | Cafe Ralo,Café Ralo | BR | Espírito Santo | Água Doce do Norte | -18.66667 | -41 | 0 | America/Sao_Paulo | populated place | |
3445579 | Valinhos | Valinhos | BR | Minas Gerais | Conceição do Rio Verde | -21.93333 | -45.18333 | 0 | America/Sao_Paulo | populated place | |
3399096 | Gameleira de São Sebastião | Gameleira,Gameleira de Sao Sebastiao,Gameleira de São Sebastião | BR | Ceará | Missão Velha | -7.25 | -39.11667 | 0 | America/Fortaleza | populated place | |
7776646 | Sítio Silvestre Simões | Sitio Silvestre Simoes,Sítio Silvestre Simões | BR | Paraná | Siqueira Campos | -23.6518 | -49.8171 | 0 | America/Sao_Paulo | populated place | |
3403951 | Cajueiro | BR | Ceará | Barbalha | -7.4 | -39.3 | 0 | America/Fortaleza | populated place | ||
7700398 | Sítio Roseira | Sitio Roseira,Sítio Roseira | BR | Paraná | Lapa | -25.91216 | -49.75992 | 0 | America/Sao_Paulo | populated place | |
3467826 | Campo Alegre | BR | Mato Grosso do Sul | Sidrolândia | -20.61667 | -55.2 | 0 | America/Campo_Grande | populated place | ||
3401423 | Cuipeua | BR | Pará | Alenquer | -1.88333 | -54.88333 | 0 | America/Santarem | populated place | ||
3475027 | Sítio São Jorge | Sitio Sao Jorge,Sítio São Jorge | BR | São Paulo | Atibaia | -23.07 | -46.6525 | 0 | America/Sao_Paulo | populated place | |
3404715 | Buriti de Laje | BR | Piauí | Alto Longá | -5.63333 | -42.18333 | 0 | America/Fortaleza | populated place | ||
3388884 | São Domingos | BR | Bahia | Casa Nova | -9.46667 | -41.7 | 0 | America/Bahia | populated place | ||
3461650 | Humaitá | Humaita,Humaitá,Humayta | BR | Minas Gerais | Juiz de Fora | -21.76972 | -43.49028 | 0 | America/Sao_Paulo | populated place |
Exploring Brazil: A Geographical Insight and Data Resource
Brazil’s Vast Geography: The Heart of South America
Brazil, the largest country in South America, spans over 8.5 million square kilometers, making it the fifth-largest country in the world. It is bordered by every South American nation except Chile and Ecuador, and has a vast coastline along the Atlantic Ocean to the east. Brazil’s geography is as diverse as its culture, with expansive rainforests, sprawling savannas, rugged highlands, and vibrant urban centers, all contributing to the nation’s ecological richness and economic activities.
The Amazon Rainforest, the world’s largest tropical rainforest, covers much of the northern part of Brazil and is an integral part of the country's biodiversity. In contrast, the southern regions of Brazil experience more temperate climates, with a mix of subtropical forests, rolling hills, and agricultural plains. The country’s numerous rivers, including the mighty Amazon River, provide essential waterways for transportation and trade, and also support Brazil’s rich agricultural and fishing industries.
Understanding how Brazil’s geography shapes its urbanization, economic activities, and environmental policies is crucial. My geographic database of Brazil provides detailed data on the country’s cities, regions, and departments, including the latitude and longitude coordinates for each, allowing users to gain a comprehensive understanding of Brazil’s spatial organization and the interrelationship between its natural features and human settlements.
Brazil’s Regions: From the Amazon to the Pampas
Brazil is divided into five major geographic regions: North, Northeast, Central-West, Southeast, and South. Each of these regions is distinct in terms of geography, climate, population density, and economic activity.
- **North Region**: Home to the vast Amazon Rainforest, the North region of Brazil is characterized by dense jungles, rivers, and an array of unique ecosystems. The region is sparsely populated compared to the more urbanized regions of Brazil, but its natural resources, including timber, rubber, and minerals, play a significant role in the economy. Manaus, the capital of Amazonas state, serves as a key commercial hub in the region, particularly in industries like electronics and shipbuilding.
- **Northeast Region**: The Northeast is a land of stark contrasts, with tropical beaches, arid plains, and fertile agricultural lands. Cities like Salvador and Recife are important cultural and economic centers. The region is known for its rich colonial history, vibrant festivals, and growing tourism industry. Despite the challenges posed by droughts, agriculture—especially sugarcane and coconut production—remains a critical part of the economy.
- **Central-West Region**: Dominated by the vast cerrado (tropical savanna) and the Pantanal wetlands, this region is the agricultural powerhouse of Brazil. The Central-West is also home to Brasília, the capital of Brazil, strategically located in the interior of the country to promote regional development. Agriculture, particularly cattle ranching and soy production, is a major economic driver here.
- **Southeast Region**: The Southeast is Brazil's economic and industrial heartland. It includes the two largest cities in Brazil—São Paulo, the financial capital, and Rio de Janeiro, the cultural capital. The region’s favorable geography, including coastal access and a well-developed infrastructure, has made it the primary engine of Brazil’s economy. The Southeast is home to Brazil’s automotive, steel, and petrochemical industries, and its cities are among the most populous and influential in the world.
- **South Region**: Known for its temperate climate, the South region of Brazil is home to the country’s most developed agricultural sector, with large-scale production of grains, beef, and dairy products. The region is also known for its European heritage, with a significant German and Italian immigrant population. Cities like Porto Alegre and Curitiba are centers for commerce and innovation, particularly in sustainability and urban planning.
The geographic database I have created provides detailed data on the cities and regions of Brazil, including their latitude and longitude coordinates, offering users a deep understanding of the country’s diverse landscapes and regional development patterns.
Latitude and Longitude: Mapping Brazil’s Urban Centers
Latitude and longitude coordinates are essential for understanding the exact location of cities and regions in Brazil. These coordinates not only reveal where cities like São Paulo, Rio de Janeiro, and Brasília are positioned geographically but also help to analyze how the country’s natural features—such as rivers, mountains, and forests—interact with human settlements and infrastructure.
For example, São Paulo, located at 23.55° S latitude and 46.64° W longitude, is strategically positioned along the Tietê River, providing access to both domestic and international markets. The exact latitude and longitude of other cities, such as Manaus in the Amazon or Porto Alegre in the South, reveal how Brazil’s geographic diversity influences settlement patterns, transportation routes, and economic activities.
By using the geographic database, users can pinpoint the exact locations of all major cities, regions, and key geographical features of Brazil, enabling a better understanding of the country’s urban and rural distribution. This data is invaluable for analyzing spatial trends in population, resource distribution, and infrastructure development.
Geography and Economy: How Brazil’s Natural Features Shape Industry
Brazil’s geography plays a significant role in shaping its economic activities. The dense forests of the Amazon, for example, provide raw materials for industries such as timber, pharmaceuticals, and rubber. However, deforestation has raised concerns about sustainable land use and environmental preservation, which in turn affect Brazil’s economic policies and development strategies.
The country’s vast river systems, particularly the Amazon River and its tributaries, are crucial for both transportation and hydroelectric power generation. These waterways connect the interior to the coast and facilitate trade in commodities such as soy, minerals, and oil. Cities like Manaus, which are located along the rivers, benefit from this waterway access, driving local economies and industries.
The Southeast region, home to Brazil’s major urban centers, is the country’s industrial heartland. São Paulo, the largest city in Brazil, is a major financial and industrial hub, benefiting from its strategic location and well-established infrastructure. The city serves as the center of Brazil’s financial markets, while industries in the region focus on automobile manufacturing, textiles, electronics, and chemicals.
The geographic database I’ve developed can help users map the spatial distribution of economic activities across Brazil, from the agricultural heartlands in the Central-West to the high-tech industries in São Paulo. This data is crucial for understanding how geography influences Brazil’s economic development and how different regions specialize in various sectors based on their natural resources and infrastructure.
Environmental Conservation and Geographic Data in Brazil
Brazil’s environmental challenges are closely linked to its geography. The Amazon Rainforest, in particular, plays a vital role in regulating the global climate, and its preservation is a major concern for both Brazil and the world. The geographic database I’ve created can help monitor environmental changes, track deforestation patterns, and assess the impact of human activity on key ecosystems.
By providing latitude and longitude data for critical environmental zones, such as national parks and conservation areas, the database supports efforts to protect Brazil’s natural heritage. It also provides the necessary data for sustainable land use planning, enabling decision-makers to balance economic growth with environmental preservation.
Conclusion: Unlocking Brazil’s Geographic Potential
Brazil’s vast and varied geography is a key driver of its economic, cultural, and environmental identity. By using the geographic database I’ve developed, which includes detailed data on cities, regions, and natural features across the country, users can gain valuable insights into how geography influences urban development, economic activities, and environmental conservation. This data is essential for informed decision-making in urban planning, resource management, and sustainability, helping to ensure that Brazil’s growth is both environmentally responsible and economically viable. Through the use of geographic data, Brazil can continue to harness its immense potential while preserving its natural wonders for future generations.
FaQ about Brazil
- Geoname_ID: This is a unique identifier for each place or geographical name in the Geoname database.
- City: The name of the place, which can be a town, village or any other form of human settlement.
- Alternate_Name: Other names or appellations that the place may have. These alternative names may be in different languages, dialects or even local names.
- Country_Code: This is the ISO 3166-1 alpha-2 code for the country in which the place is located. For example, "US" for the United States, "FR" for France.
- Region: This represents the first-order administrative division in which the location is situated. For example, this could be a state, province or territory.
- Sub_region: This is a second-order administrative division, such as a county or district, within the region.
- Latitude: The geographical latitude of the location, usually in decimal degrees.
- Longitude: The geographical longitude of the location, also usually in decimal degrees.
- Elevation: The elevation or altitude of the location in relation to sea level, usually measured in metres.
- Population: The estimated number of inhabitants or population of the location.
- Timezone: The time zone in which the location is located, in accordance with global time zone standards.
- Fcode_Name: This is a code that categorises the type of location. For example, "PPL" could mean a populated place, while "PPLC" could be the capital of a political entity.