Russia's capital, Moscow, was the largest city in the country with over **** million residents as of January 1, 2024. Less than a half of Moscow's population resided in Saint Petersburg, the second-most populous city in the country. The third-largest city, Novosibirsk, was located in the Siberian Federal District, being the highest-populated city in the Asian part of Russia. Why is Moscow so populated? The Russian capital is the center of political, industrial, business, and cultural life in Russia. Despite being one of the most expensive cities worldwide, it continues to attract people from Russia and abroad, with its resident population following a generally upward trend over the past decade. Wages in Moscow are higher than in Russia on average, and more opportunities for employment and investment are available in the capital. Furthermore, the number of people living in Moscow was forecast to continue rising, exceeding **** million by 2035. Urbanization in Russia In 2024, around *** million Russian residents lived in cities. That was approximately three-quarters of the country’s population. The urbanization rate increased steadily over the 20th century, leading to a decline in the rural population. Among the country’s regions, the Northwestern Federal District had the highest share of residents in urban areas, measured at ** percent. In the Central Federal District, the tendency was that more people moved to Moscow and cities in the Moscow Oblast.

Among Russian cities with more than one million inhabitants, the country's capital Moscow received the highest urban environmental quality index score of *** out of 360 points in 2024, based on six criteria and six types of area. The second-leading city in this category was Saint Petersburg, Russia's second-largest city, while Kazan ranked third.

1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate.

How to use

from pathlib import Path
import requests
import pandas as pd

url = ("https://raw.githubusercontent.com/"
   "epogrebnyak/ru-cities/main/assets/towns.csv")

# save file locally
p = Path("towns.csv")
if not p.exists():
  content = requests.get(url).text
  p.write_text(content, encoding="utf-8")

# read as dataframe
df = pd.read_csv("towns.csv")
print(df.sample(5))

Files:

• towns.csv - city information
• regions.csv - list of Russian Federation regions
• alt_city_names.json - alternative city names

Сolumns (towns.csv):

Basic info:

• city - city name (several cities have alternative names marked in alt_city_names.json)
• population - city population, thousand people, Rosstat estimate as of 1.1.2020
• lat,lon - city geographic coordinates

Region:

• region_name - subnational region (oblast, republic, krai or AO)
• region_iso_code - ISO 3166 code, eg RU-VLD
• federal_district, eg Центральный

City codes:

• okato
• oktmo
• fias_id
• kladr_id

Data sources

• City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files.
• City list corresponds to this Wikipedia article.
• Alternative dataset is wiki-based Dadata city dataset (no population data).

Comments

City groups

• Ханты-Мансийский and Ямало-Ненецкий autonomous regions excluded to avoid duplication as parts of Тюменская область.

• Several notable towns are classified as administrative part of larger cities (Сестрорецк is a municpality at Saint-Petersburg, Щербинка part of Moscow). They are not and not reported in this dataset.

By individual city

• Белоозерский not found in Rosstat publication, but should be considered a city as of 1.1.2020

Alternative city names

• We suppressed letter "ё" city columns in towns.csv - we have Орел, but not Орёл. This affected:

  • Белоозёрский
  • Королёв
  • Ликино-Дулёво
  • Озёры
  • Щёлково
  • Орёл

• Дмитриев and Дмитриев-Льговский are the same city.

assets/alt_city_names.json contains these names.

Tests

poetry install
poetry run python -m pytest

How to replicate dataset

1. Base dataset

Run:

• download data stro rar/get.sh
• convert Саратовская область.doc to docx
• run make.py

Creates:

• _towns.csv
• assets/regions.csv

2. API calls

Note: do not attempt if you do not have to - this runs a while and loads third-party API access.

You have the resulting files in repo, so probably does not need to these scripts.

Run:

• cd geocoding
• run coord_dadata.py (needs token)
• run coord_osm.py

Creates:

• coord_dadata.csv
• coord_osm.csv

3. Merge data

Run:

• run merge.py

Creates:

• assets/towns.csv

Dataset

This dataset was created by bluetrain

Released under CC0: Public Domain

Contents

Population in the largest city (% of urban population) in Russia was reported at 11.72 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Population in largest city in Russia was reported at 12712305 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

As of January 1, 2025, ***** million inhabitants lived in Russian cities, opposed to **** million people living in the countryside. The rural population of Russia saw a gradual decrease over the observed time period.

With a score of *****, Moscow was the leading city for startups in Russia in 2024. Saint Petersburg followed, having earned a score of **** in the period observed. Furthermore, the Russia's capital ranked the major city for startups in Central and Eastern Europe (CEE). The score was based on several indicators, such as the number of startups in each city, the startups' qualitative results, and the cities' business and economic indicators.

This database provides construction of Large Urban Regions (LUR) in Russia. A Large Urban Region (LUR) can be defined as an aggregation of continuous statistical units around a core that are economically dependent on this core and linked to it by economic and social strong interdependences. The main purpose of this delineation is to make cities comparable on the national and world scales and to make comparative social-economic urban studies. Aggregating different municipal districts around a core city, we construct a single large urban region, which allows to include all the area of economic influence of a core into one statistical unit (see Rogov & Rozenblat, 2020 for more details) thus, changing a city position in a global urban hierarchy. In doing so we use four principal urban concepts (Pumain et al., 1992): political definition, morphological definition, functional definition and conurbation that we call Large Urban Region. We constructed Russian LURs using criteria such as population distribution, road networks, access to an airport, distance from a core, presence of multinational firms. In this database, we provide population data for LURs and their administrative units.

In 2025, Moscow was the largest city in Europe with an estimated urban agglomeration of 12.74 million people. The French capital, Paris, was the second largest city in 2025 at 11.35 million, followed by the capitals of the United Kingdom and Spain, with London at 9.84 million and Madrid at 6.81 million people. Istanbul, which would otherwise be the largest city in Europe in 2025, is excluded as it is only partially in Europe, with a sizeable part of its population living in Asia. Europe’s population is almost 750 million Since 1950, the population of Europe has increased by approximately 200 million people, increasing from 550 million to 750 million in these seventy years. Before the turn of the millennium, Europe was the second-most populated continent, before it was overtaken by Africa, which saw its population increase from 228 million in 1950 to 817 million by 2000. Asia has consistently had the largest population of the world’s continents and was estimated to have a population of 4.6 billion. Europe’s largest countries Including its territory in Asia, Russia is by far the largest country in the world, with a territory of around 17 million square kilometers, almost double that of the next largest country, Canada. Within Europe, Russia also has the continent's largest population at 145 million, followed by Germany at 83 million and the United Kingdom at almost 68 million. By contrast, Europe is also home to various micro-states such as San Marino, which has a population of just 30 thousand.

On Sunday, September 12, 2021, the highest self-isolation index among Russian cities with over *********** inhabitants was measured in Omsk at *** points, indicating that there was a high number of people on the streets. In the capital Moscow, where most COVID-19 cases in Russia were recorded, the index reached *** points. The non-working period in Russia ended on May 12, 2020.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for Russian Federation (RUSFCACLNUM) from 2008 to 2015 about ATM, Russia, banks, and depository institutions.

Among Russian cities with 250,000 to one million inhabitants, Tyumen received the highest urban environmental quality index score of *** out of 360 points in 2024, based on six criteria and six types of area. Ryazan and Yaroslavl followed with scores of *** and *** points, respectively.

Despite that Moscow accounted for the largest sporting goods online sales share, the highest consumption index of sporting goods in Russia was measured in Krasnodar. To compare, Moscow listed in the ****** place.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

This survey covers the Russian Federation.

Analysis unit

• Household
• Individual

Universe

The WVS for the Russian Federation covers national population, aged 18 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was designed to be representative of the entire adult population, i.e. 18 years and older, of your country. The lower age cut-off for the sample was 18 and there was not an upper age cut-off for the sample. Population: Total non-institutionalized population of the Russian Federation, 18 years and older, without citizens living in the Far North and in inaccessible regions of Siberia.

Five-stage area probability sample: (1) The country is divided into 4 strata. For each stratum the desired number of respondents is defined proportional to population size. (2) Within each stratum 50 primary sampling units (administrative districts) are selected at random proportional to size. (3) Within each primary sampling unit secondary sampling units (towns and rural Soviets as administrative subdistricts) are selected randomly (4) Within each secondary sampling unit third sampling units (voting districts in the towns, villages belonging to a rural Soviet in the rural areas) are randomly selected. The total number of third sampling units was 186. (5) Within each third sampling unit households were selected at random from a household register (fourth sampling unit). (6) Within each household the respondent is randomly selected using the "Kish-selection-grid": all adult family members are listed in a certain order, first males from the oldest to the youngest, than females from the oldest to the youngest; the respondent is selected by a selection key which is randomly composed for each possible type of household composition (fifth sampling unit). Selection is done: 41% Male and 59% Female. 75% Urban and 25% Rural. The sample size is N=2040.

Universe: The universe includes the adult population of Russia residing in 89 regios and republics. The Far North and inaccessible regions of Siberia, military bases and prisons are not included. Primary sampling units: Administrative rayons in regions, krays and republics are used as the primary sampling units (PSUs). Each rayon is a geographically localized territory which in general contains both urban and rural settlements. Either a town or a rural settlement may be a center of rayon. Usually, but not always, it is the largest settlement in a rayon. If a rural settlement is the center of a rayon itself generally consists only of rural settlements and is referred to the category of rural rayhons. Separate towns which are considered by official statistical institutions as rayons are also included in the set of primary sampling units. These towns are not part of rayons though they are situated in the rayon's territory. Sometimes they may also include some suburbs. So separate towns and rural rayons may be considered as two poles of a scale which contains all various rayhons of Russia (primary sampling units, PSUs). On the continuum between these poles there are rayons of mixed type containing urban and rural sttlements of different sizes. Population size of different rayons may vary from 4-5 thousand to several hundred thousand or even several million of people in cities considered as separate rayons. If population size is less than 10.000 the rayon is linked to an adjacent one in a stratum. All PSUs are presented in the form of data base of more than 2.000 records with each record corresponding to one rayon or separate town (later referred to as rayons). The record for each rayon (PSU) contains the following data: - unique identification number and rayon title, - code and title of a region, - central town population size, - rayon population size All data are based on annual statistical reports (Chislennost RSFSR na 1 janvarya 1990) and 1989 census information. Primary sampling units stratification: PSUs stratification is based on two variables: geographical placement and status of the rayon center. All primary sampling units are grouped in strata consisting of homogeneous rayons. Strata are formed so that each stratum has approximately the same population size. They may consist of from one to several dozen PSUs depending on PSUs population size. In this sample the stratum population size is equal approximately 3.000 thousand (tab.1). Two cities in Russia Moscow and St. Petersburg have population size exceeding stratum population size. They form so called self-representing strata. The geographic placement of a rayon is defined by corresponding economic and geographic zone. According to statistical institutions Russia is divided into 11 economic and geopraphic regions. But for sample construction this division seems to be too fractional and can prevent forming strata of equal size in each zone. The main goal for using the geographic factor as a stratification variable is the uniform spreading of PSUs through Russia territory. For these reasons economic and geographic regions in Russia wre grouped in four zones:

• Zone 1 - North and Center of European part of Russia (unites Northern, North Western + Kaliningrad obl., Central and Volgo-´Vjatsky regions of Russia).

• Zone 2 - South of Wuropean part of Russia (unites Tsentralno-Chernozjemny, Povolzhsky and North- Caucasian regions of Russia).

• Zone 3 - Ural and West Siberia (two economic regions)

• Zone 4 - East Siberia and Far East (two economic regions). For economic and geographic division in Russia seven factors are used: nature and resources, population, industry, power engineering, area industry distribution, agriculture, transport and communicftions ( Economicheskaya geographiya SSSR. Moskva, Vishaya shkola, 1983). 11 regions were aggregated in four zones on the basis of two first factors: nature and resources and population. The second variable of PSUs stratification is the status of the rayon center. It is formed on officially accepted statistical classification by type and population size:

• rural settlement,

• urban settlement with populatiton size:

  • below 20.000
  • between 20.000 and 50.000
  • between 50.000 and 200.000
  • between 500.000 and 1000.000
  • more than 1000.000

Remarks about sampling: - Final numbers of clusters or sampling points: 186 - Sample unit from office sampling: Household

Mode of data collection

Face-to-face [f2f]

Research instrument

The WVS questionnaire was in Russian. Some special variable labels have been included, such as: V56 Neighbours: Jews and V149 Institution: The European Union. Special categories labels are: V203/ V204: Geographical affinity, 1. Locality or town where you live, 2. Region of country where you live, 3. Own country as a whole, 4. Europe, 5. The world as whole. Country Specific variables included are: V208: Ethnic identification, 2. Ukranian, 3. Tatarian 4. Komi 5 Mordovia, 6 Karbardian 7 Balkarian; V209: Language at home: 2. Ukranian, 3. Tatarian 4. Komi 5 Mordovia, 6 Karbardian 7 Balkarian; The variables political parties V210 a V212; Region: V 234 and V206 Born in this country are also included as country specific variables. The ethnic group of the respondent was not asked in the interview. In the cases of Eastern Europe Countries where the ethnic group is missing the language chosen for interview is the only indicator available to control the ethnic composition of the samples. Nevertheless, native language indicated in the cesus of 1989 and language chosen for interview are not exactly the same, since the first is rather differentiated whereas for the last the alternatives to choose between where only the national language or Russian.

Response rate

The response rate for the Russian Federation is 74.9% and is calculated as follows: (2040/2723) x 100=74.9%

Sampling error estimates

+/- 2,2%

Russia is a country spanning Eastern Europe and Northern Asia. It is the largest country in the world, covering over 17,125,191 square kilometres (6,612,073 sq mi), and encompassing one-eighth of Earth's inhabitable landmass. Russia extends across eleven time zones, and has the most borders of any country in the world, with sixteen sovereign nations. It has a population of 146.2 million; and is the most populous country in Europe, and the ninth-most populous country in the world. Moscow, the capital, is the largest city in Europe; while Saint Petersburg is the second-largest city and cultural centre. Russians are the largest Slavic and European nation; they speak Russian, the most spoken Slavic language, and the most spoken native language in Europe.

Source: Objaverse 1.0 / Sketchfab

Although repression against elites is a common occurrence in authoritarian regimes, we know little about which elites are targeted. This paper uses an original dataset on the prosecution of mayors in large Russian cities to examine the factors that make elites more likely to be arrested. We argue that in electoral authoritarian regimes like Russia, regime leaders are reluctant to arrest popular officials. Such officials command political capital that is useful to the regime, and arrests of prominent officials can produce popular backlash. We examine this argument using an original dataset on all arrests of municipal leaders in Russia's 221 largest cities between 2002 and 2018. We find that mayors who won their elections by large margins are less likely to be arrested. In addition, we document several other substantively important patterns: 1) a mayor's professional background is not related to the likelihood of arrest, 2) opposition mayors are four times more likely to be arrested, and 3) mobilization of votes for the regime is not protective against arrest.

As of September 2020, Moscow had the biggest amount of public routes, among which the largest number was accounted for bus routes and tram lines. The Russian northern capital Saint Petersburg, followed next with a total number of *** routes of public transport.

Moscow had the highest public transportation quality index among Russian cities, at around **** points in the third quarter of 2023. It was followed by Saint Petersburg and Perm with approximately **** and **** index points, respectively. The average trip price, comfort, convenience, and public transport network's efficiency were taken into account while comprising indexes for each city.

Large Towns of the Russian Empire, as depicted on the Geographical Atlas of the Russian Empire produced by the Military-Topographical Depot of His Imperial Majesty's General Staff, 1820-1827. Component of the Imperiia Project. Documentation and analysis available here (http://dighist.fas.harvard.edu/projects/imperiia/items/show/649)