Germany cities list with latitude and longitude in CSV,SQL,XML,JSON formats

Germany
Germany
CSV, SQL, XML, JSON

Last update : 10 December 2024.

Country
Germany
Capitale
Berlin
Number of cities
67518
Download
Click here

This is the best list of 67518 cities in the Germany 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 Germany file that you are going to retrieve. The data is displayed here in the form of a table:


Geoname_IDCityAlternate_NameCountry_CodeRegionSub_regionLatitudeLongitudeElevationPopulationTimezoneFcode_Name
2813387WegebergDEBrandenburg0053.0333312.550Europe/Berlinpopulated place
2952937BankewitzDELower Saxony0053.0325810.806370Europe/Berlinpopulated place
2855788PaalzowDEBrandenburg0052.9220512.652650Europe/Berlinpopulated place
2955783ApfelstettenDEBaden-WurttembergTübingen Region48.377069.490080Europe/Berlinpopulated place
2954789AuDEBavariaUpper Franconia50.1991411.29270Europe/Berlinpopulated place
2827128StiebitzScijecy,Stiebitz,SćijecyDESaxony0051.1748614.393490Europe/Berlinpopulated place
2804654ZeulenrodaDEThuringia0050.6527811.9837713763Europe/Berlinpopulated place
2866896NaundorfNaundorfDESaxony0050.9372313.42290Europe/Berlinpopulated place
2859644OberndorfDEBavariaLower Bavaria48.5526112.639330Europe/Berlinpopulated place
2833291SelbachDESaarland0049.536377.037620Europe/Berlinpopulated place
11608785WeingartenDEBaden-WurttembergFreiburg Region47.996447.811620Europe/Berlinpopulated place
2908903HausenDEBavariaSwabia48.3268110.297450Europe/Berlinpopulated place
2913538GunzenhausenGunzenhausenDEBaden-WurttembergTübingen Region47.977449.383270Europe/Berlinpopulated place
2875233LühmannsdorfLuehmannsdorf,Luhmannsdorf,LühmannsdorfDEMecklenburg-Vorpommern0054.0087113.6356721Europe/Berlinpopulated place
2919224GöllingenDEBavariaSwabia48.7252610.601290Europe/Berlinpopulated place
2886861KöckernDESaxony-Anhalt0051.6069112.19650Europe/Berlinpopulated place
2884495KreidachDEHesseRegierungsbezirk Darmstadt49.561368.787060Europe/Berlinpopulated place
2950997BenolpeBenolpeDENorth Rhine-WestphaliaRegierungsbezirk Arnsberg51.016897.73810Europe/Berlinpopulated place
2827388SteppachDEBavariaUpper Franconia49.7741710.805050Europe/Berlinpopulated place
2919664GniebitzGniebitzDESaxony0051.611912.785120Europe/Berlinpopulated place
2831516SöllingenDEBaden-WurttembergKarlsruhe Region48.986818.539470Europe/Berlinpopulated place
2866581NeippergNeippergDEBaden-WurttembergRegierungsbezirk Stuttgart49.104979.049420Europe/Berlinpopulated place
2819185UnterlangnauDEBaden-WurttembergTübingen Region47.633339.650Europe/Berlinpopulated place
2900906HohnDEBavariaRegierungsbezirk Unterfranken50.2801510.080510Europe/Berlinpopulated place
2892646KarstedtshofDEBrandenburg0053.0998512.465890Europe/Berlinpopulated place
2873624MarienberghausenMarienberghausenDENorth Rhine-WestphaliaRegierungsbezirk Köln50.919127.495530Europe/Berlinpopulated place
2958404AllerheiligenDEBaden-WurttembergTübingen Region47.733729.407530Europe/Berlinpopulated place
2878341LeupoldsteinDEBavariaUpper Franconia49.6961911.391840Europe/Berlinpopulated place
2866471NeschenDERheinland-Pfalz0050.595237.433610Europe/Berlinpopulated place
2845563RohrmünzDEBavariaLower Bavaria48.8864513.002020Europe/Berlinpopulated place
2944250BrenscheidDENorth Rhine-WestphaliaRegierungsbezirk Arnsberg51.167467.792590Europe/Berlinpopulated place
2878814LengshamDEBavariaLower Bavaria48.4108513.02890Europe/Berlinpopulated place
2851683PunreuthDEBavariaUpper Palatinate49.932411.869560Europe/Berlinpopulated place
2922175GebhardshagenDELower Saxony0052.1061910.358560Europe/Berlinpopulated place
2825951StreithainDEHesseRegierungsbezirk Darmstadt50.431489.140790Europe/Berlinpopulated place
2916625GroßenheerseGrossenheerse,GroßenheerseDENorth Rhine-WestphaliaRegierungsbezirk Detmold52.44678.989560Europe/Berlinpopulated place
2901690HohenhörnDESchleswig-Holstein0054.063489.307830Europe/Berlinpopulated place
2835592SchüttingDELower Saxony0053.52848.465520Europe/Berlinpopulated place
2813691WassingDEBavariaLower Bavaria48.5454412.504270Europe/Berlinpopulated place
2846817Riether StiegeDEMecklenburg-Vorpommern0053.6946314.233120Europe/Berlinpopulated place
2817022VogelbrinkDENorth Rhine-WestphaliaRegierungsbezirk Detmold52.365499.007680Europe/Berlinpopulated place
2936155DörentheDoerenthe,Dorenthe,DörentheDENorth Rhine-WestphaliaRegierungsbezirk Münster52.22017.682470Europe/Berlinpopulated place
2950506BerghamDEBavariaLower Bavaria48.4779612.810250Europe/Berlinpopulated place
2955652ArendseeDEBrandenburg0052.7682913.487610Europe/Berlinpopulated place
2930953EllgassenDEBavariaSwabia47.69.90Europe/Berlinpopulated place
2823147ThalbornDEThuringia0051.0921911.219110Europe/Berlinpopulated place
2813733WasserkurlWasserkurlDENorth Rhine-WestphaliaRegierungsbezirk Arnsberg51.559717.627240Europe/Berlinpopulated place
2855482PapitzPapitz,PopojceDEBrandenburg0051.7815314.199830Europe/Berlinpopulated place
2929196EsbeckDELower Saxony0052.1577210.961950Europe/Berlinpopulated place
2834979SchwarzenbergDEBavariaRegierungsbezirk Mittelfranken49.672510.475620Europe/Berlinpopulated place
2863584NiederbolheimNiederbolheimDENorth Rhine-WestphaliaRegierungsbezirk Köln50.829796.631130Europe/Berlinpopulated place
2849933RaumBielatal-Raum,RaumDESaxony0050.853714.026410Europe/Berlinpopulated place
2919747GlüsingenDELower Saxony0053.4098110.0180Europe/Berlinpopulated place
2821393TrefnitzDEBavariaUpper Palatinate49.4796312.280880Europe/Berlinpopulated place
2866053NeucunnewitzDESaxony0051.1780214.717370Europe/Berlinpopulated place
2882093LaasowDEBrandenburg0051.7133714.087480Europe/Berlinpopulated place
2866163NeuboblitzNeuboblitz,Nowe BobolcyDESaxony0051.143614.412630Europe/Berlinpopulated place
2883945KrieleDEBrandenburg0052.6551212.555710Europe/Berlinpopulated place
2829380StaudachDEBavariaLower Bavaria48.5959812.820250Europe/Berlinpopulated place
2847267RiedeggDEBavariaSwabia47.653910.623770Europe/Berlinpopulated place
2927130FermerslebenFermersleben,Magdeburg-FermerslebenDESaxony-Anhalt0052.0921111.654950Europe/Berlinpopulated place
2886171Kolonie HorstmarDENorth Rhine-WestphaliaRegierungsbezirk Arnsberg51.67.550Europe/Berlinpopulated place
2866856NaurathNaurathDERheinland-Pfalz0049.759826.88259219Europe/Berlinpopulated place
2859179OberscheidDERheinland-Pfalz0050.696347.413060Europe/Berlinpopulated place
2913781GülzowDEMecklenburg-Vorpommern0053.8197812.06814978Europe/Berlinpopulated place
2861382OberauDEBaden-WurttembergTübingen Region47.79.783330Europe/Berlinpopulated place
2888467KleinmünsterDEBavariaUpper Bavaria48.6833311.633330Europe/Berlinpopulated place
2847318RiedDEBavariaSwabia48.5231411.073120Europe/Berlinpopulated place
2814063WarnauDESaxony-Anhalt0052.7398812.19096282Europe/Berlinpopulated place
2930271EngelreuthDEBavariaRegierungsbezirk Mittelfranken49.0937111.074050Europe/Berlinpopulated place
2888556Klein LiebitzDEBrandenburg0051.9536614.265340Europe/Berlinpopulated place
2866538NendlnachDEBavariaLower Bavaria48.8155313.3820Europe/Berlinpopulated place
2913808GüldensteinDESchleswig-Holstein0054.2214610.834780Europe/Berlinpopulated place
2871096MilowMilowDEMecklenburg-Vorpommern0053.1915511.54144349Europe/Berlinpopulated place
2858984OberspitzenbachDEBaden-WurttembergFreiburg Region48.175987.98860Europe/Berlinpopulated place
2850117RatzenriedRatzenriedDEBaden-WurttembergTübingen Region47.7219.900640Europe/Berlinpopulated place
2816033VorseeDEMecklenburg-Vorpommern0053.6721214.195840Europe/Berlinpopulated place
2853487PlackersdorfDEBavariaUpper Bavaria48.3231612.623910Europe/Berlinpopulated place
2817232VillauVillauDENorth Rhine-WestphaliaDüsseldorf District51.099536.671570Europe/Berlinpopulated place
2813621WattsfeldDESchleswig-Holstein0054.759.950Europe/Berlinpopulated place
2925323FrauenrenthDEBavariaUpper Palatinate49.8851812.409570Europe/Berlinpopulated place
2955834AnzingDEBavariaUpper Bavaria48.3014612.200310Europe/Berlinpopulated place
2864891NeuhofDEMecklenburg-Vorpommern0054.3290413.424780Europe/Berlinpopulated place
2862542Nienser DeichstrichDELower Saxony0053.68.333330Europe/Berlinpopulated place
2943632BruchdorfDELower Saxony0053.2333310.850Europe/Berlinpopulated place
2847194RiederingDEBavariaUpper Bavaria48.0755311.869010Europe/Berlinpopulated place
2807333WittendorfWittendorfDEBaden-WurttembergKarlsruhe Region48.420978.497410Europe/Berlinpopulated place
2858794OberthannDEBavariaUpper Bavaria48.5051611.580840Europe/Berlinpopulated place
2949018BietenhausenDEBaden-WurttembergTübingen Region48.409218.86580Europe/Berlinpopulated place
2812068WeislitzDEBavariaUpper Palatinate49.4120912.426570Europe/Berlinpopulated place
2932157EichholzDENorth Rhine-WestphaliaRegierungsbezirk Detmold51.922148.900210Europe/Berlinpopulated place
2836669SchönfeldDEBavariaUpper Bavaria48.8967811.048640Europe/Berlinpopulated place
2938089DerkumDENorth Rhine-WestphaliaRegierungsbezirk Köln50.70986.814850Europe/Berlinpopulated place
2807576WippstettenDEBavariaLower Bavaria48.5151312.376270Europe/Berlinpopulated place
2871202MierscheidMierscheidDENorth Rhine-WestphaliaRegierungsbezirk Köln50.748567.449870Europe/Berlinpopulated place
2915559Groß HalternDELower Saxony0052.303678.175030Europe/Berlinpopulated place
2872942MatheshörlebachMatheshorlebach,MatheshörlebachDEBaden-WurttembergRegierungsbezirk Stuttgart49.114039.826530Europe/Berlinpopulated place
2828804SteinbachDEThuringia0050.3561911.213780Europe/Berlinpopulated place
2933792EchteEchteDELower Saxony0051.7840710.062920Europe/Berlinpopulated place
2928414EulsbachDEHesseRegierungsbezirk Darmstadt49.677218.764860Europe/Berlinpopulated place

Germany: A Geographer’s Perspective on Its Diverse Landscape and Urban Dynamics

A Country of Contrasts: Geography and Landscape of Germany

Germany, located in the heart of Europe, is a country known for its geographical diversity and striking contrasts. From the snow-capped peaks of the Alps in the south to the flat, expansive plains of the north, Germany’s terrain offers an exceptional range of natural environments. It shares borders with nine countries, making it one of the most strategically located nations in Europe. Its landscapes include rolling hills, vast forests, winding rivers, and a dynamic coastline along the North and Baltic Seas.

Geographically, Germany is divided into a number of distinct regions, each with unique characteristics. The southern region is home to the famous Bavarian Alps, offering a more mountainous terrain, while the northern part of the country is dominated by the North German Plain, which stretches from the Netherlands to Poland. The central region of Germany is defined by the hilly terrain of the Central Uplands, which gives rise to some of the country’s most significant river systems, including the Rhine, Elbe, and Danube.

These diverse landscapes not only define the country’s natural beauty but also shape its economy, culture, and urban development. Understanding how these regions and their cities are interconnected is essential to understanding the geographical forces that have shaped modern Germany.

The Regions and Administrative Divisions of Germany

Germany’s administrative divisions are an important feature of the country’s geographic and political organization. The country is divided into 16 federal states, or "Bundesländer," each with its own regional government and administrative structure. These federal states range in size and population, and they often exhibit distinct cultural identities, which are reflected in the cities and towns that populate them.

For instance, Bavaria in the south is known for its distinct culture, rich history, and mountainous landscape, while the state of Saxony in the east is known for its role in Germany’s industrial past and its proximity to the Czech Republic. States like Berlin and Hamburg are major urban centers, serving as political and cultural hubs, while others like Brandenburg and Thuringia are more rural, offering a different look at life in Germany.

Geographers studying Germany must consider these regional differences and how they shape not just the landscape but also the social, economic, and political fabric of the country. The distribution of cities, towns, and rural areas across these states offers a fascinating lens through which to explore the country’s urban development, historical evolution, and contemporary challenges.

The Role of City Data in Understanding Germany’s Urban Dynamics

Germany’s cities are key to understanding the country’s geography. With a population of over 80 million, Germany is one of the most urbanized countries in Europe. Its cities, from the capital Berlin to large industrial centers like Munich, Frankfurt, and Hamburg, serve as economic engines and cultural powerhouses. But beyond these well-known urban centers, smaller towns and villages also play crucial roles in shaping the country's identity.

City data, including geographic coordinates, regional locations, and administrative divisions, is critical for a deeper understanding of Germany's urban dynamics. The spatial distribution of cities across the country reveals patterns of historical development, industrialization, and migration. Additionally, by studying the relationship between cities and their surrounding regions, geographers can gain insights into the country’s infrastructure, transportation networks, and regional economies.

In a geographically diverse country like Germany, it is important to examine how cities are situated in relation to the natural environment, whether they are located near rivers, nestled in valleys, or perched on hilltops. The location of cities and their connections to each other shape their economic and social relationships.

Latitude and Longitude: Mapping Germany’s Cities for Insightful Analysis

Latitude and longitude are essential for precisely mapping the cities and towns of Germany. By obtaining the exact coordinates of each city, geographers can create detailed spatial models that reveal patterns of population distribution, economic activity, and connectivity. These geographic coordinates enable researchers to analyze the spatial relationships between urban centers, transportation corridors, and resource distribution.

For example, the location of cities along the Rhine River—such as Cologne and Düsseldorf—offers a clear indication of how natural waterways have influenced the development of trade and commerce. Similarly, cities in the southern states, such as Munich, benefit from their proximity to the Alps, which shape both the economy (through tourism and natural resources) and the region’s cultural identity.

Obtaining the latitude and longitude data for these cities provides researchers with a deeper understanding of how these geographical factors have influenced urban growth and how cities interact with each other across the country. It also supports urban planning initiatives, such as optimizing transportation networks or determining the best locations for new infrastructure projects.

Unlocking Regional and City Data for Comprehensive Geographic Analysis

Obtaining detailed data on Germany’s cities, regions, and departments offers vast potential for geographic analysis. For geographers, this data is crucial in mapping the various forces at play in the country’s growth and development. By understanding the precise locations of cities within their respective regions, researchers can better analyze the interplay between geography, urbanization, and economics.

This data is also valuable for urban planners, policymakers, and businesses looking to assess the economic potential of specific areas or design strategies for sustainable urban growth. By integrating city-specific data with other forms of geographic and demographic data, it is possible to create more targeted development plans, whether it is for expanding infrastructure, improving public services, or addressing regional disparities.

Germany’s Sustainable Development Through Geographic Data

As Germany continues to balance economic development with environmental sustainability, geographic data plays a pivotal role in shaping future policies. The precise location of cities and regions, combined with their environmental characteristics, can help guide decision-making processes that prioritize sustainability. Whether it’s managing urban sprawl, reducing carbon footprints, or protecting natural resources, detailed city data helps identify the best areas for development while minimizing negative environmental impacts.

For instance, understanding the geographic positioning of cities like Berlin, which is located on flat terrain in the northeastern part of the country, versus cities like Stuttgart, which is nestled in a valley surrounded by hills, provides crucial insights into the potential challenges each city faces regarding urban planning, climate adaptation, and infrastructure development.

The Future of Geographic Research in Germany

Germany’s future, both in terms of urban growth and environmental management, will be shaped by the availability of detailed geographic data. The ability to analyze and understand the geographical layout of cities and regions allows for better decision-making regarding infrastructure, environmental protection, and social well-being.

With access to precise city and regional data, Germany can continue to evolve in a way that maximizes its economic potential while addressing the challenges posed by climate change and sustainable development. By integrating geographic coordinates, urban trends, and environmental factors, Germany can plan for a future that is both prosperous and environmentally responsible.

In conclusion, geographic data is key to understanding Germany’s cities, regions, and their interrelationships. This data offers geographers, planners, and policymakers valuable insights into the country’s spatial organization and helps guide the sustainable development of its urban and rural landscapes.

FaQ about Germany

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Yes, of course, click here to retrieve an archive containing an extract of the 4 CSV, XML, JSON and SQL files for a country.
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  • 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.
You will retrieve a .rar archive containing the country files in csv, sql, xml and json formats.
Yes, you'll be able to download the content you've purchased whenever you want, with no time limit.
Make sure your Internet connection is stable, as some files can be quite large and take a long time to download.
Unfortunately we don't offer files in excel format, but it's really very easy to convert a csv file into excel. There are a plethora of free online sites that allow you to do this, here are a few: click here to convert csv to excel

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