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.

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.

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.

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.

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 100,000 to 250,000 inhabitants, Reutov, located in the Moscow Oblast, received the highest urban environmental quality index score of 301 out of 360 points in 2024, based on six criteria and six types of area. The second-leading city in this category was Krasnogorsk.

On Sunday, September 12, 2021, the highest self-isolation index among Russian cities with over one million inhabitants was measured in Omsk at 2.8 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 two 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.

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.

The 2014 Arctic Human Development Report identified “Arctic settlements, cities, and communities” as one of the main gaps in knowledge of the region. This research looks at circumpolar urbanization trends, with a special focus on Russia.

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.

The Russia Office Furniture Market size was valued at USD 2.24 Billion in 2024 and is projected to reach USD 3.57 Billion by 2032, growing at a CAGR of 5.17% from 2025 to 2032.

Key Market Drivers:

Growing Commercial Real Estate Development: The expansion of commercial real estate and business centers across major Russian cities is driving significant demand for office furniture and workspace solutions. According to Rosstat (Russian Federal State Statistics Service), commercial real estate development in Russia increased by 15.8% in 2023, with 4.2 million square meters of new office space added across major cities, requiring an estimated $850 million in office furniture investments. The commercial real estate sector in Russia continues to show robust growth, particularly in Moscow and St. Petersburg. New office developments have maintained strong momentum, with occupancy rates averaging 85% in premium locations, driving sustained demand for modern office furnishing solutions. The majority of new developments are adopting contemporary workspace designs.

The Russia luxury residential real estate market, while exhibiting resilience, faces a complex interplay of factors influencing its growth trajectory. The market, estimated at XX million in 2025 (assuming a logical extrapolation from the provided CAGR of 3.00% and the unspecified 2019-2024 market size), is projected to experience steady expansion throughout the forecast period (2025-2033). Key drivers include increasing high-net-worth individual (HNWI) populations in major cities like Moscow and St. Petersburg, a preference for upscale living among the burgeoning middle class, and ongoing government initiatives to improve infrastructure and attract foreign investment. The preference for apartments and condominiums within these cities significantly contributes to the market's structure, although villas and landed houses remain a niche segment catering to specific preferences. However, macroeconomic conditions, including geopolitical instability and fluctuations in the ruble's exchange rate, pose considerable restraints. Further, international sanctions and domestic economic policies could significantly impact buyer sentiment and investment flows. Competition among major developers like PIK Group, SU-, Samolet Group, Glavstroy, and others is fierce, necessitating innovation and targeted marketing strategies to secure market share. The segmentation of the market across apartment and condominium types versus villas and landed houses offers opportunities for specialization. Moscow and St. Petersburg, as primary luxury hubs, are expected to dominate the market share, while Novosibirsk and other cities present developing secondary markets with growth potential. The consistent CAGR of 3.00% suggests moderate but steady growth, indicating potential for strategic investments in prime locations and innovative project developments. However, a thorough due diligence process is crucial for investors given the geopolitical uncertainties and potential economic volatility. The market’s future hinges on both national and global economic stability, aligning closely with broader Russian economic performance and international relations. Precise forecasting necessitates detailed financial modeling, considering fluctuations in currency values and economic sanctions, which are beyond the scope of this brief analysis. This comprehensive report provides an in-depth analysis of the Russia luxury residential real estate market, covering the period from 2019 to 2033. It offers invaluable insights into market size, trends, drivers, challenges, and key players, with a focus on the high-growth segments. The report is essential for investors, developers, and businesses operating or planning to enter this lucrative market. Key aspects covered include luxury apartments Moscow, luxury condos St. Petersburg, high-end villas Russia, and the overall Russia real estate market forecast. Key drivers for this market are: Increasing construction spending by governments, Growing popularity of interior design and architecture is likely to increase the demand for polymer sheets. Potential restraints include: Shortage of Raw Materials. Notable trends are: Growth in the Apartment Buildings Driving the Market.

Customers of bookstores located in Moscow and Saint Petersburg spent more than those in other regions of Russia in January 2023. Over ** percent of consumers in the country's two largest cities spent at least *** Russian rubles on average in bookstores, while the largest share of buyers in other localities nationwide expended less than *** Russian rubles.

Data format and access

The database consists of full-text patient reviews, reflecting their dissatisfaction with healthcare quality. Materials in Russian have been posted in the «Review list» of the site infodoctor.ru. Publication period: July 2012 to August 2023. The database consists of 18,492 reviews covering 16 Russian cities with population of over one million. Data format: .xlsx.

Data access: 10.5281/zenodo.15257447

Data collection methodology

Based on the fact that negative reviews may be more reliable than positive ones, the authors carried out negative reviews from 16 Russian cities with a population of over one million, for which it was possible to collect representative samples (at least 1000 reviews for each city). We have extracted reviews from the one-star section of this site's guestbook, as they are reliably identified as negative. Duplicates were removed from the database. Personal data in comment texts have been replaced with "##########". The author's gender was determined manually based on his/her name or gender endings in the texts of reviews. Otherwise, we indicated "0" - gender cannot be determined.

For Moscow reviews, classification was carried out using manual markup methods - based on the majority of votes for the review class from 3 annotators (if at least one annotator indicated that it was impossible to determine, the review was classified as #N/A - impossible to clearly determine). For reviews from other cities, classification was made into 3 classes using machine learning methods based on logistic regression. The classification accuracy was 88%.

The medical specialties were distributed into large groups for the convenience of further analysis. The correspondence of medical specialties to large groups is presented in detail in Appendix 1.

Sample structure and description of variables

· CITY – the name of a city with a population of over a million (on a separate sheet – Moscow), the other 15 are Volgograd, Voronezh, Yekaterinburg, Kazan, Krasnodar, Krasnoyarsk, Nizhny Novgorod, Novosibirsk, Omsk, Perm, Rostov-on-Don, Samara, St. Petersburg, Ufa, Chelyabinsk

· TEXT – review text

· GENDER – gender of the review author (2 – female, 1 – male, 0 – cannot be determined)

· CLASS_1 – group of reasons for dissatisfaction with medical care (M – issues of medical content, O – issues of organizational support and economic aspect, C – mixed (combined) class, #N/A – cannot be clearly determined)[1]

· CLASS_2 – group of reasons for dissatisfaction with medical care (0 – issues of medical content, 1 – issues of organizational support and economic aspect, 2 – mixed (combined) class, #N/A – cannot be clearly determined)

· DAY – day of the month the review was posted

· MONTH – month the review was posted

· YEAR – year the review was posted

· DOCTOR_OR_CLINIC – what or who is the review dedicated to – the doctor or the clinic

· SPEC – physician specialty (for observations where the review is dedicated to the physician)

· GROUP_SPEC – a large group of a physician’s specialty

· ID – observation identifier

Database application

The data are suitable for analyzing patient dissatisfaction trends with medical services in Russia over the period from July 2012 to August 2023. This dataset could be particularly useful for healthcare providers, policymakers, and researchers interested in understanding patient experiences and identifying areas for quality improvement in Russian healthcare. Some potential applications include:

• Analyzing geographic patterns of patient complaints across different cities in Russia
• Examining trends in patient dissatisfaction over time
• Identifying common reasons for dissatisfaction with medical care
• Comparing dissatisfaction levels between different medical specialties
• Assessing gender differences in patient complaints

The database provides rich qualitative data through full-text review texts, allowing for in-depth analysis of patient experiences. The structured variables like city, date, doctor/clinic information, etc. enable quantitative analysis as well. This combination of qualitative and quantitative data makes it possible to gain a comprehensive understanding of patient dissatisfaction patterns in Russia's healthcare system over more than a decade.

For researchers specifically interested in healthcare quality issues, this dataset could serve as an important resource for studying patient experiences and outcomes in Russia's medical system. The longitudinal nature of the data (2012-2023) also allows for analysis of changes over time in patient satisfaction.

Overall, this database provides valuable insights into patient perceptions of healthcare quality that could inform policy decisions, quality improvement

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.

The dataset includes entrepreneurship development indicators: - Number of enterprises and organizations (at the end of the year) (2020), units and thousand units; - Turnover of organizations (2020), billion rubles; - Balanced financial result (profit minus loss) of organizations' activities (2020), million rubles and billion rubles; - Share of organizations using special software (2020), % The dataset also contains indicators such as Ыhare of organizations implementing technological innovations (2020), %; Gross regional product (GRP) (2019), million rubles. The values of indicators are given for 96 main territories of the Russian Federation allocated by Rosstat, including regions, federal districts and largest cities (federal centers). The data are given by regions for 2020, as well as for Russia as a whole for 2010, 2015 and 2020. Source of the data: Regions of Russia. Socio-economic indicators - 2021 [Electronic resource]. - Rosstat. – Access mode: https://rosstat.gov.ru/folder/210/document/13204 The dataset is available in Russian (на Русском) and English (in separate files).

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.

In 2024, the total population of Russia was around 146.1 million people. Only a fraction of them live in the major Russian cities. With almost 12.5 million inhabitants, Moscow is the largest of them. In the upcoming years until 2030, the population was forecast to decline.Russia's economy Russia is one of the major economies in the world and is one of the wealthiest nations. Following the 1998 Russian financial crisis, Russia introduced several structural reforms that allowed for a fast economic recovery. Following these reforms, Russia experienced significant economic growth from the early 2000s and improved living standards in general for the country. A reason for the momentous economical boost was the rise in commodity prices as well as a boom in the total amount of consumer credit. Additionally, Russia is highly dependent on the mining and production of natural resources, primarily in the energy department, in order to promote economic growth in the country. Due to large energy reserves throughout the country, Russia has developed a stable economy capable of sustaining itself for many years into the future. The majority of Russian oil and energy reserves are located in the Western Siberian areas. These natural gas liquids, along with oil reserves that consist of crude oil, shale oil and oil sands are constantly used for the production of consumable oil, which is an annually growing industry in Russia. Oil products are one of Russia’s primary exports and the country is able to profit entirely off of sales due to high prices as well as high demand for such goods.