Cities near Águas de Lindóia
Águas de Lindóia is at (-22.4764°, -46.6328°). Showing cities within the selected radius, sorted by distance.
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Nearby cities 90 cities found
| City | Country | Distance | Population |
|---|---|---|---|
| Monte Sião | Brazil | 7.9 km | 24,089 |
| Serra Negra | Brazil | 16.6 km | 29,894 |
| Socorro | Brazil | 16.7 km | 41,352 |
| Itapira | Brazil | 19.9 km | 72,022 |
| Jacutinga | Brazil | 21.3 km | 25,525 |
| Amparo | Brazil | 28.4 km | 72,677 |
| Bueno Brandão | Brazil | 29.2 km | 10,911 |
| Santo Antônio de Posse | Brazil | 32.8 km | 23,244 |
| Espírito Santo do Pinhal | Brazil | 33.4 km | 39,816 |
| Mogi Mirim | Brazil | 33.8 km | 78,244 |
| Pinhalzinho | Brazil | 34.0 km | 15,224 |
| Mogi Guaçu | Brazil | 34.3 km | 153,658 |
| Ouro Fino | Brazil | 34.6 km | 32,094 |
| Arcadas | Brazil | 35.1 km | 11,614 |
| Estiva Gerbi | Brazil | 39.3 km | 11,295 |
| Pedreira | Brazil | 40.4 km | 43,112 |
| Jaguariúna | Brazil | 44.3 km | 58,722 |
| Andradas | Brazil | 45.9 km | 40,553 |
| Terra Preta | Brazil | 46.3 km | 15,605 |
| Holambra | Brazil | 46.8 km | 15,094 |
| Morungaba | Brazil | 47.8 km | 13,720 |
| Bom Repouso | Brazil | 50.1 km | 12,649 |
| Vargem | Brazil | 51.1 km | 10,512 |
| Extrema | Brazil | 53.0 km | 53,482 |
| Borda da Mata | Brazil | 53.1 km | 17,404 |
| Itapeva | Brazil | 53.3 km | 12,692 |
| Bragança Paulista | Brazil | 53.7 km | 176,811 |
| Souzas | Brazil | 56.2 km | 18,152 |
| Artur Nogueira | Brazil | 56.5 km | 53,157 |
| Conchal | Brazil | 57.8 km | 28,101 |
| Aguaí | Brazil | 58.4 km | 32,888 |
| Camanducaia | Brazil | 58.9 km | 26,097 |
| São João da Boa Vista | Brazil | 58.9 km | 92,547 |
| Engenheiro Coelho | Brazil | 59.5 km | 19,566 |
| Cosmópolis | Brazil | 60.8 km | 59,773 |
| Cambuí | Brazil | 61.0 km | 29,536 |
| Paulínia | Brazil | 62.2 km | 112,003 |
| Joanópolis | Brazil | 62.4 km | 12,815 |
| Itatiba | Brazil | 62.6 km | 122,581 |
| Estiva | Brazil | 63.3 km | 11,502 |
| Campinas | Brazil | 64.9 km | 1,031,554 |
| Valinhos | Brazil | 66.4 km | 126,373 |
| Caldas | Brazil | 66.5 km | 14,217 |
| Piracaia | Brazil | 70.1 km | 26,029 |
| Jarinu | Brazil | 70.2 km | 37,535 |
| Vinhedo | Brazil | 70.8 km | 80,111 |
| Congonhal | Brazil | 70.9 km | 11,083 |
| Atibaia | Brazil | 71.7 km | 144,088 |
| Hortolândia | Brazil | 73.7 km | 234,259 |
| Bom Jesus dos Perdões | Brazil | 75.2 km | 22,006 |
| Louveira | Brazil | 75.2 km | 51,847 |
| Sumaré | Brazil | 75.6 km | 286,211 |
| Nova Odessa | Brazil | 75.8 km | 62,019 |
| Vargem Grande do Sul | Brazil | 76.5 km | 40,133 |
| Pouso Alegre | Brazil | 76.7 km | 152,217 |
| Poços de Caldas | Brazil | 76.9 km | 168,641 |
| Americana | Brazil | 77.4 km | 246,655 |
| Araras | Brazil | 78.4 km | 135,331 |
| Limeira | Brazil | 79.6 km | 291,869 |
| Nazaré Paulista | Brazil | 82.1 km | 18,217 |
| Campo Limpo Paulista | Brazil | 82.5 km | 77,632 |
| Jundiaí | Brazil | 83.0 km | 443,221 |
| Várzea Paulista | Brazil | 84.1 km | 115,771 |
| Leme | Brazil | 84.4 km | 98,161 |
| Cordeirópolis | Brazil | 84.7 km | 24,514 |
| Santa Bárbara d'Oeste | Brazil | 85.9 km | 188,000 |
| Conceição dos Ouros | Brazil | 86.1 km | 10,880 |
| Itupeva | Brazil | 86.9 km | 20,605 |
| São Sebastião da Grama | Brazil | 87.3 km | 10,441 |
| Monte Mor | Brazil | 87.4 km | 64,662 |
| Paraisópolis | Brazil | 88.0 km | 20,445 |
| Cachoeira de Minas | Brazil | 88.8 km | 11,883 |
| Indaiatuba | Brazil | 90.3 km | 256,223 |
| Francisco Morato | Brazil | 90.3 km | 165,139 |
| Casa Branca | Brazil | 91.0 km | 28,083 |
| Divinolândia | Brazil | 91.3 km | 11,158 |
| Iracemápolis | Brazil | 91.7 km | 21,967 |
| Santa Gertrudes | Brazil | 92.2 km | 23,611 |
| Mairiporã | Brazil | 93.8 km | 101,937 |
| Campestre | Brazil | 93.9 km | 20,696 |
| Franco da Rocha | Brazil | 94.5 km | 144,849 |
| Igaratá | Brazil | 94.6 km | 10,605 |
| São Bento do Sapucaí | Brazil | 95.6 km | 11,674 |
| Rio Claro | Brazil | 95.7 km | 201,418 |
| Santa Cruz das Palmeiras | Brazil | 96.1 km | 28,864 |
| Botelhos | Brazil | 96.9 km | 14,828 |
| Pirassununga | Brazil | 97.5 km | 73,545 |
| Elias Fausto | Brazil | 98.7 km | 17,699 |
| Santa Rita do Sapucaí | Brazil | 98.8 km | 40,635 |
| Caieiras | Brazil | 99.3 km | 102,775 |
What is this tool?
The Cities Near tool lets you explore all populated places within a chosen radius of any city in the world. Enter a city, pick a radius (50, 100, 200, or 500 km), and get an instant interactive map and ranked list — sorted from closest to farthest.
This is useful for trip planning, geography research, or simply satisfying your curiosity about what lies around a given city. The underlying dataset comes from GeoNames and covers 70,000+ populated places worldwide.
How does the distance calculation work?
Distances are calculated using the Haversine formula, which gives the great-circle distance between two points on a sphere — the shortest path over the Earth's surface. The result is accurate to within a few kilometres for most city pairs. Note that the Haversine formula assumes a perfectly spherical Earth; the true Earth is slightly flattened, so very long distances may differ by up to 0.5% from the exact geodetic distance.
Cities near famous places
- Within 100 km of Paris: Versailles, Chartres, Reims, Rouen — all classic day-trip destinations.
- Within 200 km of Tokyo: Yokohama, Kawasaki, Sagamihara, Chiba, Shizuoka — the vast Kantō metropolitan area.
- Within 500 km of Istanbul: Ankara, Bursa, Thessaloniki, Sofia, Izmir — crossing into three countries.
Frequently Asked Questions
What is the maximum radius I can search?
The tool offers radii of 50, 100, 200, and 500 km. For reference, a 500 km radius around Paris would cover most of France plus parts of Belgium, Germany, and Switzerland. Use the minimum population filter to narrow down the results to cities of the size you care about.
How is distance measured — straight line or road distance?
The distance shown is the straight-line great-circle distance (as the crow flies), not the road distance. Road distances are always longer due to routes, terrain, and urban layouts. This tool is intended for geographic exploration, not navigation.
What data source is used?
City data comes from the GeoNames geographical database, which aggregates official government sources worldwide. It includes name, coordinates, population, country, and timezone for over 70,000 populated places. Coordinates are accurate to 4–6 decimal places.