In 2022, the population density in Russia stood at 8.81 people. Between 1992 and 2022, the figure dropped by 0.25 people, though the decline followed an uneven course rather than a steady trajectory.

Historical dataset showing Russia population density by year from 1992 to 2022.

Population density (people per sq. km of land area) in Russia was reported at 8.8074 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on October of 2025.

View yearly updates and historical trends for Russia Population Density. Source: World Bank. Track economic data with YCharts analytics.

Population density of Russian Federation slipped by 0.35% from 8.8 people per sq. km in 2021 to 8.8 people per sq. km in 2022. Since the 0.04% improve in 2019, population density declined by 0.84% in 2022. Population density is midyear population divided by land area in square kilometers.

Comprehensive socio-economic dataset for Russia including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

Density of nursing and midwifery personnel of Russian Federation plummeted by 29.76% from 8.9 number per thousand population in 2019 to 6.2 number per thousand population in 2020. Since the 1.46% rise in 2014, density of nursing and midwifery personnel sank by 32.90% in 2020.

Context

I've collected this for the Avito contest. I got translation of Russian regions (to English - written version). I attatched those names at the end of Wikipedia url, and scraped useful information (population, density, rural/urban, time zone, etc) of each region.

Acknowledgements

Description of Data

Period: 01/03–30/03 Object: City or Region (842 cities — some data N/A, 84 regions) Target: inf_rate — infection rate, calculated as Log(infected_at_30.03 + 1) — Log( infected_at_15.03 + 1)

Explanatory Variables - population — number of citizens - density — population density (http://worldgeo.ru/russia/lists/?id=26) - lat — latitude - lng — longtitude - cleanness, public_services, neighbourhood, children_places, sport_and_outdoor, shops_and_malls, public_transport, security, life_costs — survey responses for life quality evaluation survey (https://www.domofond.ru/statya/polnyy_reyting_250_gorodov_rossii_po_kachestvu_zhizni/6764) - ivl_per_100k, ivl_number, ekmo_per_100k, ekmo_number — number of ventilators absolute and per 100k population, number of ECMO equipments — absolute and per 100k population. - infected_3003, died_3003, recovered_3003, sick_3003, infected_1503, died_1503, recovered_1503, sick_1503 — measure for calcularing infection_rate. We assume incubation period as two weeks and calculate log-transformed increment as proxy of reproduction rate (https://ru.wikipedia.org/wiki/%D0%A0%D0%B0%D1%81%D0%BF%D1%80%D0%BE%D1%81%D1%82%D1%80%D0%B0%D0%BD%D0%B5%D0%BD%D0%B8%D0%B5_COVID-19_%D0%B2_%D0%A0%D0%BE%D1%81%D1%81%D0%B8%D0%B8) - avg_temp_min, avg_temp_max, avg_temp_std, avg_temp_median, humidity_min, humidity_max, humidity_std, humidity_median, pressure_min, pressure_max, pressure_std, pressure_median, wind_speed_ms_min, wind_speed_ms_max, wind_speed_ms_std, wind_speed_ms_median — weather archive data for March 2020 from http://weatherarchive.ru/catalog1 - urban_50–54_years, urban_55–59_years, urban_60–64_years, urban_65–69_years, urban_70–74_years, urban_75–79_years, urban_80–84_years, urban_85–89_years, urban_90–94_years, rural_50–54_years, rural_55–59_years, rural_60–64_years, rural_65–69_years, rural_70–74_years, rural_75–79_years, rural_80–84_years, rural_85–89_years, rural_90–94_years — number of inhabitants by age groups and urban/rural areas, (http://showdata.gks.ru/, measure name — 23110000100030200007_Численность_постоянного_населения_России_по_возрасту_на_1_январ.) - work_ratio_15–72_years, work_ratio_55–64_years, work_ratio_15–24_years, work_ratio_15–64_years, work_ratio_25–54_years — percentage of people working by age groups (http://showdata.gks.ru/, measure name — 11242000300080200004_Уровень_занятости_по_полу_и_возрастным_группам) - num_patients_tubercul_1992 .. 2017 — number of tuberculosis patients by years (better or worse vaccination? vaccination data not available by regions, http://showdata.gks.ru/, measure name — 22420000100070200001_Численность_выявленных_пациентов_с_впервые_в_жизни_установленным_диагнозом_акт) - volume_serv_household_2017, volume_serv_chargeable_2017, volume_serv_transport_2017, volume_serv_post_2017, volume_serv_accommodation_2017, volume_serv_telecom_2017, volume_serv_others_2017, volume_serv_veterinary_2017, volume_serv_housing_2017, volume_serv_education_2017, volume_serv_medicine_2017, volume_serv_disabled_2017, volume_serv_culture_2017, volume_serv_sport_2017, volume_serv_hotels_2017, volume_serv_tourism_2017, volume_serv_sanatorium_2017 — volume of services by different types in RUB currency (http://showdata.gks.ru/, measure name — 21373000200010200001_Объем_платных_услуг_населению_с_2017_г_). Could be also useful as lower estimate of lost money due to lockdown. - num_phones_rural_2018, num_phones_urban_2018— number of phones by urban/rural area, (http://showdata.gks.ru/, measure name — 11111132100050200001_Число_телефонных_аппаратов_(включая_таксофоны). Could be also useful for evaluating possibility of contact tracing — at least at the level of availability of phones in risky areas. - bus_march_travel_18, bus_april_travel_18 — number of passenger kilometers * 1000 for buses in March/April 2018 (http://showdata.gks.ru/, measure name — Пассажирооборот автобусов по маршрутам регулярных перевозок (тысяча пассажиро-километров)) - epirank_avia, epirank_bus, epirank_train, epirank_avia_cat, epirank_bus_cat, epirank_train_cat — epirank indexes, calculated based on the following paper (prefix …cat — head/tail breaks, as described in the paper, daytime =0.55, d=1.0) https://www.researchgate.net/publication/332131602_EpiRank_Modeling_Bidirectional_Disease_Spread_in_Asymmetric_Commuting_Networks based on the following implementation: https://github.com/wcchin/EpiRank, based on the following data Origin-Destination data for Aviation, Trains and Buses from Tutu.ru (Thank you!): https://github.com/ods-ai-ml4sg/covid19-tutu whole_population, urban, rural — risky population from 65+group in the whole, and by urban/rural, http://showdata.gks.ru/)

Recording and evaluation of the first Russian census regarding the socio-ethnic structure of the Russian Empire.

Topics: Government data (area, population density, temporary residents, foreigners etc.), composition of the population of both sexes, foreigners according to country of origin, population according to native language, religious denomination, literacy, age groups, marital status, physical handicaps, profession, status.

In 2023, there were around ** hospital beds per 10,000 population available in Russian round-the-clock clinics. The figure has decreased since 2020. To compare, in 2012, the hospital bed density was measured at approximately ** beds per 10,000 inhabitants.

Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

Recording and evaluation of the first Russian census regarding the socio-ethnic structure of the Russian Empire. Topics: Government data (area, population density, temporary residents, foreigners etc.), composition of the population of both sexes, foreigners according to country of origin, population according to native language, religious denomination, literacy, age groups, marital status, physical handicaps, profession, status. Erfassung und Auswertung der ersten russischen Volkszählung in Hinblick auf die sozio-ethnische Struktur des Russischen Reiches. Themen: Gouvernementdaten (Fläche, Bevölkerungsdichte, vorübergehend Anwesende, Ausländer etc.), Zusammensetzung der Bevölkerung beider Geschlechts, Ausländer nach Herkunftsland, Bevölkerung nach Muttersprache, Konfession, Lesefähigkeit, Altersgruppen, Familiens körperlichen Gebrechen, Beruf, Stand.

In 2023, Lineage Logistics expanded its footprint by acquiring a major cold storage facility near Moscow, enhancing its capacity to cater to rising demand in the food and pharma sectors. Moscow and St. Petersburg remain key operational hubs due to their population density and industrial concentration. The Russia cold chain market reached a valuation of RUB 280 Billion in 2023, driven by the increasing demand for temperature-sensitive goods, growth in pharmaceutical exports, and expansion in organized food retailing. The market is characterized by prominent players such as X5 Logistics, Magnit, Lineage Logistics, RZD Logistics, and Global Cold Chain. These companies are known for their strong infrastructure, integrated supply chain services, and focus on maintaining product quality across the distribution process. Russia Cold Chain Market Overview and Size

We assessed changes in the population size, density, and diet composition of wolves inhabiting the Romincka Forest (RF), an area of 480 km2 situated along the state border between Poland, Russian Federation (Kaliningrad), and Lithuania. We compared the results of our research in 2020-2021 with data from other projects conducted since 1999. We found that both packs living in RF had transboundary territories. The number of packs was stable over 21 years, the average pack size almost doubled (from 4-4.5 to 7.5-8 wolves per pack), the total wolf number increased 1.8 times, reaching 15-16 wolves, the density increased 1.5 times up to 3.1-3.3 wolves/100 km2 in winter 2020/2021. Our analyses of 165 scats revealed that beavers Castor fiber made up 45.6% of food biomass in the wolf diet in 2020, which was 3.4 times more than in 1999-2004 (n=84 scats, 13.4%). Wild ungulates constituted 44.8% of the wolf food biomass in 2020, 1.6 times less than before (71.1%). In our study, among wild ungulates, ..., Tracking. We tracked wolves by foot or by car, using the regular and dense network of dirt roads, routes, and other linear structures, and the plowed strip of soil along the borderline, across the whole Polish portion of RF, that wolves used for traveling and scent-marking. In snow-free seasons, we found tracks on mud or sand and followed them as far as were visible, usually at distances of 100-300 m, while in winter, snow cover allowed us to follow wolf tracks up to 10 km. Species identification was based on the shape and size of tracks and evidence of animal behavior during scent-marking. Additionally, track identification was verified with genetic analysis of scat and urine samples collected during tracking. In winter, we estimated the number of wolves in the tracked group on snow by counting the number of individual trails when wolves split, which usually happened on road junctions and was associated with intense scent-marking. We measured the length of the footprint of the front pa..., , # Dataset for paper: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders

The dataset provides data to assess the wolf numbers and diet in the Romincka Forest in northern Poland.

Description of the data and file structure

Data are grouped into three files:

Nowak_Repository_genotyping.txt. Results of genetic fingerprinting based on 13 DNA microsatellite markers for non-invasive samples found during the fieldwork in the Romincka Forest, along with reference samples from Baltic, Central European, and Carpathian wolf subpopulations. This is a TAB-separated file that contains the following columns:

(1) ID - identification number of the sample;

(2) sex - sex of the individual based on the analysis of DBX intron 6 and DBY intron 7;

Followed by columnes with numerical data for allele sizes of 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140,...

In 2023, the population of the United Kingdom was around **** million, with approximately **** million women and **** million men. Since 1953, the male population of the UK has grown by around *** million, while the female population has increased by approximately *** million. Throughout this provided time period, the female population of the UK has consistently outnumbered the male population. UK population one of the largest in Europe As of 2022, the population of the United Kingdom was the largest it has ever been, and with growth expected to continue, the forecasted population of the United Kingdom is expected to reach over ** million by the 2030s. Despite the relatively small size of its territory, the UK has one of the largest populations among European countries, slightly larger than France but smaller than Russia and Germany. As of 2022, the population density of the UK was approximately *** people per square kilometer, with London by far the most densely populated area, and Scotland the most sparsely populated. Dominance of London As seen in the data regarding population density, the population of the United Kingdom is not evenly distributed across the country. Within England, London has a population of almost **** million, making it significantly bigger than the next largest cities of Birmingham and Manchester. As of 2022, Scotland's largest city, Glasgow had a population of around *** million, with the largest cities in Northern Ireland, and Wales being Belfast and Cardiff, which had populations of ******* and ******* respectively.

Dataset for paper: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders

https://doi.org/10.5061/dryad.qfttdz0qn

The dataset provides data to assess the wolf numbers and diet in the Romincka Forest in northern Poland.

Description of the data and file structure

Data are grouped into three files:

Nowak_Repository_genotyping.txt. Results of genetic fingerprinting based on 13 DNA microsatellite markers for non-invasive samples found during the fieldwork in the Romincka Forest, along with reference samples from Baltic, Central European, and Carpathian wolf subpopulations. This is a TAB-separated file that contains the following columns:

(1) ID - identification number of the sample;

(2) sex - sex of the individual based on the analysis of DBX intron 6 and DBY intron 7;

Followed by columnes with numerical data for allele sizes of 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054,...

The diversity and population density of small soil arthropods – springtails were studied in relict broad-leaf – cedar pine forests of the Primorskii krai of Russia. The records were carried out on the territory of Ussuriiskii, Sikhote-Alinsky and Kedrovaya Pad’ natural reserves. A multiscale and intensive sampling was used to reveal species diversity. The data on the numbers for 175 species (about 24 thousand individuals in the 648 cores) from 8 forest sites are presented.

Разнообразие коллембол – мелких почвенных членистоногих – изучали в реликтовых кедрово-широколиственных лесах Дальнего Востока России. Учеты проводили на территории заповедников: Уссурийского, Сихотэ-Алинского и Кедровая Падь. Использована фрактальная схема расположения почвенных проб, позволяющая изучать разнообразие коллембол на различных пространственных шкалах в диапазоне от нескольких см до 10 м. Представлены данные по численностям в пробах для 175 видов (около 24 тыс.экз.).

Species distribution models can predict the suitable climatic range of a potential biological control agent (BCA), but they provide little information on the BCA's potential impact. To predict high population build-up, a pre-requisite of impact, studies are needed which assess the effect of environmental factors on vital rates of a BCA across the environmental gradient of the BCA’s suitable habitats, especially for the region where the BCA is considered for field release. We extended a published species distribution model with climate-dependent vital rates of Ophraella communa, a recently and accidentally introduced potential BCA of common ragweed, Ambrosia artemisiifolia in Europe. In field and laboratory experiments, we collected data on climate-dependent parameters assumed to be the most relevant for the population build-up of O. communa, i.e. temperature driving the number of generations per year and relative humidity (RH) determining egg hatching success. We found that O. communa concluded one generation in 334 cumulative degree days, and that egg hatching success strongly decreased from >80% to <20% when RH drops from 55% to 45% during the day. We used these values to spatially explicitly project population densities across the European range suitable for both common ragweed and the beetle and found that the present distribution of the beetle in Europe is within the range with the highest projected population growth. The highest population density of O. communa was predicted for northern Italy and parts of western Russia and western Georgia. Field observations of high impact on common ragweed with records of 80% aerial pollen reduction in the Milano area since the establishment of O. communa are in line with these predictions. The relative importance of temperature and RH on the population density of O. communa varies considerably across its suitable range in Europe. We propose that the combined statistical and mechanistic approach outlined in this paper helps to more accurately predict the potential impact of a weed BCA than a species distribution model alone. Identifying the factors limiting the population build-up of a BCA across the suitable range allows implementation of more targeted release and management strategies to optimize biocontrol efficacy.