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.

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

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.

Dataset

This dataset was created by bluetrain

Released under CC0: Public Domain

Contents

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.

This database provides a 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, 2019 for more details). 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 implemented 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.

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.

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 August 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.

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.

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.

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 horizontal bar chart displays agricultural land (km²) by capital city using the aggregation sum in Russia. The data is about countries per year.

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.

This horizontal bar chart displays alternative and nuclear energy (% of total energy use) by capital city using the aggregation average in Russia. The data is about countries per year.

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.

This horizontal bar chart displays carbon dioxide emissions (CO2) (Mt of CO2 equivalent) by capital city using the aggregation sum in Russia. The data is about countries per year.

The data is to evaluate the impact of restrictive measures introduced in connection with COVID-19 on consumption and, accordingly, on electricity generation in Russian cities, which were most susceptible to outbreaks of the coronavirus infection (Moscow, St. Petersburg, Yekaterinburg and Nizhny Novgorod). Currently, based on available data, the mobility of the population has decreased resulting in lower demand for electricity during self-isolation. Therefore, the study will be based on the hypothesis that similar changes in human behavior can be expected in the future with further spread of COVID-19 and/or the introduction of additional quarantine measures in major cities around the world. The analysis also yielded additional results: the strongest reductions in energy generation occurred in cities with high building density (7% in Moscow, 14% in Yekaterinburg). Furthermore, the decrease in energy generation in cities with low building density was not so dramatic (1% in St. Petersburg, 0% - Nizhny Novgorod). The study uses two models created with Keras LSTM. The first model forecasts power generation and uses 76 parameters. The second LSTM model forecasts new COVID-19 cases across countries, in which 10 parameters are involved.

This horizontal bar chart displays vulnerable employment (% of total employment) by capital city using the aggregation average in Russia. The data is filtered where the date is 2021. The data is about countries per year.

Population: SB: Altay Territory: Biysk: Non Working Age: Age 1 to 6 data was reported at 14.900 Person th in 2019. This records a decrease from the previous number of 15.700 Person th for 2018. Population: SB: Altay Territory: Biysk: Non Working Age: Age 1 to 6 data is updated yearly, averaging 14.600 Person th from Dec 2003 (Median) to 2019, with 17 observations. The data reached an all-time high of 16.100 Person th in 2016 and a record low of 11.800 Person th in 2003. Population: SB: Altay Territory: Biysk: Non Working Age: Age 1 to 6 data remains active status in CEIC and is reported by Federal State Statistics Service. The data is categorized under Russia Premium Database’s Demographic and Labour Market – Table RU.GA022: Population: by City: Siberian Federal District.