World cities database with latitude and longitude | Download CSV,SQL,JSON,XML formats
Last update : 13 December 2024.
Presentation of our World cities database of 4 510 988 towns around the world, accessible in four distinct formats: CSV, Json, SQL and XML. We have refined the Geoname database to show only metropolises, administrative centres and villages. All this data is carefully linked to geographical areas and sub-divisions, where available.
Each document provides information such as: Geoname_ID, City, Alternate_Name, Country_Code, Region, Sub_region, Latitude, Longitude, Elevation, Population, Timezone, Fcode_Name. For clarification on these elements, please consult our FAQ section below.
The World cities database contains data for every city in the world in separate files. Each archive contains data for a specific country. Here is an example of what can be found in a file:
Geoname_ID | City | Alternate_Name | Country_Code | Region | Sub_region | Latitude | Longitude | Elevation | Population | Timezone | Fcode_Name |
---|---|---|---|---|---|---|---|---|---|---|---|
1139546 | Jangal-e Mukhnōy | Jangal-e Mukhnoy,Jangal-e Mukhnuy,Jangal-e Mukhnōy,Jangal-e Mukhnūy,jngl mkhnwy,جنگل مخنوی | AF | Takhar | Warsaj | 36.18685 | 70.13691 | 0 | Asia/Kabul | populated place | |
7582712 | Āhangarān | Ahangaran,ahngran,Āhangarān,آهنگران | AF | Kabul | Surobi | 34.58367 | 69.73311 | 0 | Asia/Kabul | populated place | |
1423395 | Bāqī Khēl | Bakikheyl’,Bakikheyl’,Baqi Khel,Baqikheyl,Bāqī Khēl,Bāqīkheyl,baqy khyl,باقی خيل,باقی خېل | AF | Parwan | Jabal us Sarāj | 35.12791 | 69.26708 | 0 | Asia/Kabul | populated place | |
1138123 | Karīm Khēl | Karim Khel,Karim Kheyl,Karimkhel,Karimkheyl’,Karimkheyl’,Karīm Kheyl,Karīm Khēl,Kaṟīmkhēl,krym khyl,كريم خيل,کریم خېل | AF | Kapisa | Ḩişah-ye Awal-e Kōhistān | 35.10698 | 69.36288 | 0 | Asia/Kabul | populated place | |
1121453 | Wunchalāt | Ownchalat,Ownchalāt,Wuncalat,Wuncalāt,Wunchalat,Wunchalāt,awnchlat,wnchlat,اونچلات,ونچلات | AF | Faryab | Maīmanah | 35.89863 | 64.82508 | 0 | Asia/Kabul | populated place | |
1462103 | Yārdād | Yardad,Yārdād,yardad,یارداد | AF | Ghazni | Nāwur | 34.04597 | 67.33878 | 0 | Asia/Kabul | populated place | |
1143316 | Dilakchī | Delakchi,Delakchī,Delakci,Delakči,Dilakchi,Dilakchī,dlk chy,dlkchy,Дилакчи,دلكچی,دلک چی | AF | Ghazni | Jāghūrī | 33.03546 | 67.62692 | 0 | Asia/Kabul | populated place | |
1149523 | Āb-e Khānah | Ab-e Khanah,Abi Khana,Abi Khaneh,Abi Khāna,Abī Khāneh,ab khanh,Āb-e Khānah,آب خانه | AF | Balkh | Chahār Kent | 36.43414 | 67.14405 | 0 | Asia/Kabul | populated place | |
1457224 | Bōjī | Bodzhi,Boji,Buji,Bōji,Bōjī,Būjī,bwjy,بوجی | AF | Maidan Wardak Province | Nirkh | 34.32503 | 68.70457 | 0 | Asia/Kabul | populated place | |
1132641 | Mūrichāq | Mar-o-Chak,Maricaq,Maruchak,Marūchāk,Muricaq,Murichaq,Mār-o-Chāk,Māricāq,Mūrichāq,Mūrīchāq,Mūṟicāq,mwrchaq,mwrychaq,موريچاق,مورچاق | AF | Badghis | Murghāb | 35.8104 | 63.14265 | 0 | Asia/Kabul | populated place | |
1120922 | Zard Jōy | Zard Joy,Zard Jōy,Zarddzhoy,Zardjoy,Zardjuy,Zardjūy,Zaṟḏjōy,zrd jwy,زرد جوی | AF | Maidan Wardak Province | Markazi Bihsud | 34.17432 | 67.88157 | 0 | Asia/Kabul | populated place | |
1435689 | Wārêh | Vara,Wareh,Waṟeh,Wārêh,warh,واره | AF | Ghazni | Gēlān | 32.84448 | 67.58904 | 0 | Asia/Kabul | populated place | |
1142510 | Iraghlī | Eraghli,Eraghlī,Iraghli,Iraghlī,arghly,ارغلی | AF | Jowzjan | Shibirghān | 36.74967 | 65.86841 | 0 | Asia/Kabul | populated place | |
1121973 | Ūlastān | Owlastan,Owlastān,Ulastan,awlstan,Ūlastān,اولستان | AF | Farah | Pur Chaman | 33.35059 | 63.37863 | 0 | Asia/Kabul | populated place | |
1132075 | Nāw-e Tang | Naw-e Tang,Nawe Tang,Now-e Tang,Nāw-e Tang,naw tng,nw tng,ناو تنگ,نو تنگ | AF | Ghowr | Chahār Şadah | 35.19259 | 65.13404 | 0 | Asia/Kabul | populated place | |
1467949 | Khwāb Sārī | Khvab Sari,Khvāb Sāṟi,Khwab Sari,Khwāb Sārī,Khwāb Sāṟi,khwab sary,خواب ساری | AF | Badakhshan | Argō | 37.01038 | 70.31445 | 0 | Asia/Kabul | populated place | |
1146930 | Bārīkī | Barik,Barik-e,Bariki,Bārīk-e,Bārīkī,Bāṟik,Bāṟiki,baryky,باریکی | AF | Ghowr | La‘l wa Sar Jangal | 34.43229 | 66.05037 | 0 | Asia/Kabul | populated place | |
1468912 | Ḏāg Kêlay | Dag Kalay,Dag Kelay,Dagkalay,Dāg Kalay,Dāg Kêlay,قريۀ داگ,ډاگ کلی,Ḏāg Kêlay | AF | Nangarhar | Kāmah | 34.42902 | 70.55127 | 0 | Asia/Kabul | populated place | |
1462156 | Bālānah Takhak | Balana Takhak,Balanah Takhak,Balanatakhak,Balaneh Takhak,Bālāna Takhak,Bālānah Takhak,Bālāneh Takhak,balanh tkhk,بالانه تخک | AF | Bamyan | Waras̄ | 34.21885 | 67.02183 | 0 | Asia/Kabul | populated place | |
1138580 | Kalān Qōl | Kalan Qol,Kalanqol,Kalanqowl,Kalān Qōl,Kalānqowl,Kalānqōl,klan qwl,کلان قول | AF | Daykundi | Mīr Āmōr | 33.88806 | 66.81344 | 0 | Asia/Kabul | populated place |
To give you an idea of the content, here is a sample of the information included in the global file. You can retrieve a representative extract for a particular nation, in formats such as CSV, XML, JSON and SQL :
>> DOWNLOAD AN ARCHIVE CONTAINING A SAMPLE <<
World cities database: Your Ultimate World Country Database
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Download data files for the World cities in CSV, SQL, XML and JSON formats
The Global City Database: Harnessing the Potential of Geographic Information
The significance of comprehensive city-level data in geographic analysis is paramount. Comprehending the specific details of each city is essential for research, business, or policy-making. Envision possessing a comprehensive database that encompasses every city globally, together with their administrative divisions, departments, and, crucially, the precise latitude and longitude for each site. This data opens up new potential for geospatial research, providing a clearer picture of how cities fit into the larger global landscape.
For geographers, urban planners, and data scientists, access to granular data in several formats—CSV, SQL, JSON, and XML—provides exceptional flexibility and integration ease. It beyond mere knowledge of city locations; it involves a profound comprehension of the complex interrelations among them, their regions, and the global context they occupy.
The Significance of City-Level Data
Municipal-level data is the basis for numerous analyses. From urbanization trends and economic modeling to disaster response planning and environmental studies, the data from cities offers insights into the dynamics of human settlements. City data encompasses not only the cities themselves but also their connections with other regions and agencies. These administrative entities provide context, helping to clarify local governing structures, economic policies, and social variables that influence each urban region.
Acquiring precise and current information about cities, including regions and departments, facilitates a deeper comprehension of urban development, growth, and functionality within both national and international frameworks. This knowledge is essential for various professionals, including policy analysts and corporate leaders, seeking to make educated judgements grounded in geographical and political contexts.
The Significance of Latitude and Longitude Data
The geographic coordinates—latitude and longitude—constitute one of the most valuable elements of urban data. These data points enable the precise identification of any city's location on the globe, which is crucial for mapping, navigation, and spatial analysis. This information enables academics and developers to produce extremely accurate maps, develop location-based applications, and examine the spatial relationships between cities and their surroundings.
Latitude and longitude serve as essential coordinates, offering spatial context for comprehending the interrelations of cities worldwide. The geographic coordinates of cities are essential data points for conducting significant, large-scale analysis in studies of global transportation networks, climate change, or migration patterns.
Diverse Formats for Versatility and Accessibility
The ability to obtain city data in numerous formats is vital for maximizing its utility. Having many alternatives for manipulating data in spreadsheets, querying it in databases, or integrating it into web applications guarantees that you can deal with the data in a manner that best meets your requirements.
CSV files offer a straightforward and accessible method for storing and analysing municipal data via applications like as Excel or Google Sheets. SQL databases provide robust querying functionalities for intricate analysis, enabling users to filter and arrange data based on defined criteria. JSON and XML formats are ideal for developers requiring the integration of city data into apps or websites, facilitating easy parsing and dynamic utilisation in real-time contexts.
These formats guarantee that regardless of the project type, the data can be readily tailored and modified to satisfy your particular needs. Whether you’re establishing a geographic information system (GIS), creating an e-commerce platform, or evaluating patterns in urbanization, the availability of city-level data in various formats is indispensable.
A Comprehensive Global Database for All Cities
Access to a comprehensive worldwide database encompassing every city, together with its areas and agencies, presents numerous options. This data is essential for professionals in international development, disaster management, environmental research, and tourism. The capacity to reference and utilise this data globally facilitates a more profound and comprehensive comprehension of cities and their distinct attributes.
Businesses seeking worldwide expansion can utilise this data to get insights into regional marketplaces, local regulations, and the socio-economic situations of various areas. Governments can utilise it to strategise infrastructure initiatives, distribute resources, and evaluate the effects of policy modifications. Researchers can track urban growth patterns, analyse the implications of climate change, or evaluate the effectiveness of urban planning programs.
Consolidating Data for Enhanced Insights
The intrinsic worth of an extensive global city database resides in its capacity to amalgamate and scrutinise data across many cities and regions. Integrating city-level data with details regarding administrative divisions, geographic locations, and many socio-economic characteristics enables users to acquire profound insights into global patterns, regional disparities, and the interrelations among urban centres.
By examining cities in connection to their physical locations, one can discern how proximity to the coast, mountains, or other natural elements affects urban development. Analysing the correlation between a city's economic production and its administrative region can assist firms and governments in identifying growth or intervention opportunities.
The Prospects of Geographic Data
With the world becoming more interconnected, the demand for precise and comprehensive geospatial data is growing. Accessing a comprehensive database of global city data, encompassing latitude, longitude, regional divisions, and administrative contexts, is essential for professionals in geospatial analysis, urban planning, or similar disciplines. Acquiring municipal data in many formats—CSV, SQL, JSON, and XML—unlocks numerous opportunities for innovation and exploration.
This data transcends mere world mapping, it facilitates a precise, actionable, and meaningful comprehension of the universe. Accessing comprehensive city-level data is essential for making educated and transformational decisions in academic research, commercial strategy, or government policy, ultimately benefiting the future of our cities and the world.
FaQ about World cities database
- 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.