Approximately half of Russians had a negative attitude toward the United States in February 2025, a significant decrease from the previous survey. Over the observed period from April 1990, the largest share of Russians held a negative opinion of the U.S. in January 2015, at over 80 percent.

About this dataset

Context

The dataset tabulates the Russia town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Russia town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Russia town was 2,268, a 0.40% increase year-by-year from 2022. Previously, in 2022, Russia town population was 2,259, a decline of 0.40% compared to a population of 2,268 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Russia town decreased by 219. In this period, the peak population was 2,603 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Russia town is shown in this column.
• Year on Year Change: This column displays the change in Russia town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Russia town Population by Year. You can refer the same here

In 2023, only nine percent of respondents in the United States reported having a favorable opinion toward Russia. The share of people in the United States viewing Russia positively saw a decrease in recent years.

United States Imports from Russia was US$3.27 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Russia - data, historical chart and statistics - was last updated on July of 2025.

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.

Graph and download economic data for U.S. Imports of Goods by Customs Basis from Russia (IMP4621) from Jan 1992 to May 2025 about Russia, imports, goods, and USA.

The number of visitors to the United States from Russia increased in 2024. In that year, the number of visitors to the U.S. from Russia rose to approximately 87,977, up from the previous year's total of 62,375.

Due to the devastating impact of the Second World War on the Soviet population, the total population in 1950 was almost 15.5 million fewer than in 1940, which is a decrease of eight percent. In Russia (RSFSR), the largest of the Soviet states, the population difference was almost nine million people; also eight percent. It would take until 1955 for the populations of either the USSR or Russia to reach their pre-war levels, which was a decade after the conflict had ended. Urbanization Despite this drop in total population, industrialization and urbanization saw a significant change in the USSR's population distribution between the given years. The Soviet urban population increased by 6.3 million, or ten percent, between 1940 and 1950; 5.8 million of this was in Russia, which was a 15 percent increase. In contrast, the Soviet Union's rural population dropped by 21.8 million (a 17 percent change), 14.5 million of which in Russia (a decrease of 20 percent). In terms of overall population, the urban population of the USSR rose from 33 to 39 percent between 1940 and 1950, and from 34 to 43 percent in Russia. By 1955, 44 percent of the Soviet population, and 49 percent of the Russian population, lived in an urban setting.

The invasive shrub, Russian olive (Elaeagnus augustifolia), is widely established within riparian areas across the western United States (U.S.). Limited information on its distribution and invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: 1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, U.S.) with field data and remote sensing; and 2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. In the study watershed, field data showed Russian olive has reached near equal canopy cover (18.3%) to native plains cottonwood (Populus deltoides; 19.1%), with higher cover closer to the channel and over a broader range of elevations. At the basin scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial imagery, and spectral variables from Sentinel-2 MultiSpectral Instrument using a random forest model. A statistical model linking the resulting Russian olive percent cover detection map (RMSE = 15.42, R2 = 0.64) to environmental variables for the entire watershed indicated Russian olive cover increased with flow accumulation and groundwater depth, decreased with elevation, and was associated with poorer soil types. We attribute the success of Russian olive to its broad habitat suitability combined with changing hydrologic conditions favoring it over natives. This study provides a repeatable Russian olive detection methodology due to the use of Sentinel-2 imagery that is available worldwide, and provides insight into its ecological relationships and success with relevance for management across areas with similar environmental conditions. Methods Model Training Data To predict Russian olive percent cover across the Powder River Basin, we created a spectral detection model for the year 2020. The model was trained using two different data collection methods: (1) field data and (2) ocular samples from NAIP 2019 aerial imagery. Field data were collected in June 2021 (Figure 1A). Ten meter radius plots were placed on transects (25 on the east bank and 17 on the west bank) perpendicular to the river and about 50 m apart, for a total of 276 plots (Figure 1A). Within each plot, vegetation cover was estimated for each woody species, including Russian olive, plains cottonwood (Populus deltoides), and tamarisk (Tamarix ramosissima), and height of the tallest woody plant was measured using a survey rod or clinometer. Of the 276 field data plots, 185 contained Russian olive. To increase the dataset size and spatial representation, we conducted randomized ocular image sampling using NAIP 2019 true and false color imagery following a similar sampling procedure as described in (Woodward et al. 2018ab). NAIP 2019 false color imagery was referenced to help with species classification. We used Google Earth Engine (GEE) to collect 10-meter radial plots, matching the size of the field plots. We visually determined the percentage of Russian olive coverage present on a scale of 0-100 %, with 0 % being no Russian olive present and 100 % being full Russian olive cover, within each 10-meter radial plot (Figure 2). Prior to making formal observations, all five observers went through a calibration process to train and reduce bias. Due to the rarity of Russian olive in our random sample, we also opportunistically collected 478 additional plots with Russian olive. Most opportunistic aerial imagery ocular sampling points fell along the Powder River between Clear Creek and Crazy Woman Creek in Wyoming. In preliminary model runs, low to moderate Russian olive cover was unrealistically predicted in cropland areas, such as areas of Barley (Categorization Code “21”), Winter Wheat (“24”), Alfalfa (“36”), and Other Hay (“37”), so we created a simple mask to remove most crops from the final analysis. The mask was created using land cover classifications from the 2020 USDA National Agricultural Statistics Service Cropland Data Layer (NASSCD; 2021). Land cover types where Russian olive is known to occur such as Shrubland (NASSCD attribute code “152”), Grassland/Pasture (“176”), Woody Wetlands (“190”), and Herbaceous Woodlands (“195”) were retained. Table S1.2 contains a detailed list of land cover types that were not masked from the final model. All NASSCD agricultural land cover types from 0-61, 66-77, and 204-254 were excluded from the Russian olive model. The mask was also used to remove the ocular samples to build the model on sampled points that did not fall within agricultural areas. We built our model on 2,160 points (1,407 random ocular samples, 477 opportunistic ocular samples, 276 field samples), 595 of which had Russian olive present (419 ocular samples). Random Forest Model We created a mosaic of 2020 imagery from Copernicus Sentinel-2 MultiSpectral Instrument Level-1C data to cover the Powder River Basin study area, obtained in GEE. We filtered images for those with low cloud cover (<20-30 %), then created a median composite image for each relevant season – spring (2020-04-01 to 2020-05-15), summer (2020-05-16 to 2020-07-31), and fall (2020-08-01 to 2020-09-30) – to account for seasonal phenological variation (Gorelick et al. 2017). Spectral bands and vegetation indices were derived from the images, which included a Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Simple Ratio (SR), Tasseled Cap transformation, and others (Table S1.1). The resulting Tasseled Cap brightness, greenness, and wetness (BGW) indices, named for the features they emphasize, improve vegetation classifications because they are sensitive to phenological changes (Crist and Cicone 1984). We also differenced indices between summer and spring and summer and fall to capture seasonal variation of different species to aid Russian olive detection (Evangelista et al. 2009). We modeled Russian olive percent cover and evaluated predictor variable performance using the ‘randomForest’ package in RStudio (Liaw et al. 2002) using spectral bands and vegetation indices from GEE. Our independent variable was Russian olive percent cover and all 61 predictor variables are identified in Table S1.1. The number of trees (ntree) and number of variables randomly sampled at each split (mtry) were set to 1,000 and 3, respectively (Liaw et al. 2002). We valued a model with fewer predictor variables and removed predictors that did not improve the model to achieve better model performance and greater interpretability (Evans et al. 2010). We first ran a model using all predictor variables to evaluate initial out-of-bag model performance using the R2 value and root mean squared error (RMSE), a standard measure of the magnitude of model error. We then evaluated correlations between variables, removing one variable from pairs correlated by greater than 0.7 (Dormann et al. 2013), leaving us with 18 variables after the initial run. With the remaining 18 uncorrelated variables, Wwe ran 1276 additional models using backwards selection to remove the one or two variables with the lowest variable importance as measured by the increase in mean squared error. Variables with partial dependence plots that suggested the variable contributed to over-fitting or had a weak relationship were removed (Friedman 2001). The greater the R2 value and the smaller the RMSE, the better the model performed. The final model had six variables. Finally, we summarized the random forest model results by 5 km hexagon to show trends of Russian olive cover across the study area. Species Composition and Russian Olive Habitat Suitability at the Watershed Scale Plot data collected along transects perpendicular to the Powder River channel allowed for additional insights when paired with lidar and topographic data, particularly because a robust suite of woody species was recorded in addition to Russian olive. Topographic position index (TPI) was derived from 2016 lidar data (Ackerman 2016). TPI is a measure of position by comparing elevation at a given point to the mean elevation in a surrounding window (Weiss 2001). In this case, a 100 cell (100 m) radius was used and can be interpreted as position relative to the detrended channel. We also derived a Canopy Height Model (CHM), calculated as the digital terrain model minus the digital surface model, calculated in OpenTopography.org. We extracted mean TPI, CHM, and distance from channel centerline to field plots to investigate how these varied by species by considering variable distributions (i.e., boxpolots and basic statistical moments and distributions) by dominant plot species. Additionally, we used the complete suite of species and cover data in a k-means grouping analysis that included distance from the channel in ArcGIS Map. The k-means grouping method is an unsupervised classification method where every point is assigned a group based on their similarity (Davies and Bouldin, 1979). The Pseudo F-Statistic was used to determine how many groups to include in the final analysis. This allowed inference regarding the spatial relationships among Russian olive, cottonwood, and tamarisk, which was not possible in the watershed-scale modeling. Previous work (Nagler et al. 2011) describes factors at the continental to reach scale known to influence Russian olive distribution. Robust species cover data for an entire watershed is rare. As such, here we have a unique opportunity to bridge the reach and continental scales (Nagler et al. 2011). At watershed scales, surface and groundwater flow conditions, and their regulation, are known to influence native and invasive riparian species distributions across North America (McShane et al. 2015). Surface flows have declined through time in the Powder

Russia Exports to United States was US$17.75 Billion during 2021, according to the United Nations COMTRADE database on international trade. Russia Exports to United States - data, historical chart and statistics - was last updated on July of 2025.

Throughout the Cold War, the United States and the Soviet Union had relatively similar total populations. The U.S.' population grew from around 205 million to almost 250 million people between 1970 and 1990, while the USSR's population grew from around 240 to 290 million in this time. In these years, the Soviet Union had the third largest population in the world, and the U.S. had the fourth largest (behind China and India respectively). Despite their similar sizes, these populations differed in terms of distribution as the U.S.' population was approximately three quarters urban in this period, whereas the Soviet Union's urban population was just 56 percent in 1970 and 66 percent in 1989. Additionally, the Soviet Union's population was much younger than that of the U.S. due to a higher birth rate and lower life expectancy.

In February 2025, the largest share of Russians with a positive attitude toward the United States was registered among the group aged 18 to 24 years, at ** percent. At the same time, over ** percent of the survey respondents aged over 55 years viewed the U.S. negatively.

The public opinion about the probability of a war between the United States and Iran in Russia, in the view of the latest tensions in January 2020 was divided. Over a quarter of survey participants stated that the risk of a war between these sides was high, but the same percentage of respondents assessed the probability as low. Furthermore, over 20 percent of the Russian population believed this conflict could lead to the Third World War as of January 2020.

Alaska had the highest male to female ratio in the United States in 2020, with ***** men for every 100 women. The male to female ratio was lowest in the District of Columbia, with **** men for every 100 women. The final frontier Alaska, which was purchased from the Russian Empire in 1867, is the largest state in the U.S. and one of the newest states, having been admitted to the U.S. in 1959. Although oil production dominates the economy, Alaska has a very high poverty rate and consistently has the highest unemployment rate in the country. It’s a man’s world Alaska is one of 10 states in the U.S. that has more men than women. The male to female ratio in the United States as a whole is about even, but as the population ages, there tend to be more females than males. Even though the sex ratio in the U.S. is almost one to one, a little more than ** percent of all females participated in the labor force in 2021, compared with **** percent of men.

Russian Federation - Total Credit to Households and NPISHs, Adjusted for Breaks, for Russia was 22.10000 Percentage of GDP in October of 2024, according to the United States Federal Reserve. Historically, Russian Federation - Total Credit to Households and NPISHs, Adjusted for Breaks, for Russia reached a record high of 23.30000 in July of 2024 and a record low of 0.40000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for Russian Federation - Total Credit to Households and NPISHs, Adjusted for Breaks, for Russia - last updated from the United States Federal Reserve on July of 2025.

This graph shows the growth in the U.S. export volume of trade goods to Russia from 1992 to 2023. In 2023, U.S. exports to Russia amounted to about 600 million U.S. dollars. RussiaRussia is the largest country in the world, with a combined land and water area of about 17 million square kilometers between their borders. It covers more than one-eighth of the Earth's inhabited land area. It is larger than Antarctica (14 million square kilometers) and larger than all of Europe (10.5 million square kilometers). In comparison to the country’s huge land mass, the population of Russia is rather minute; with a population of only about 144 million, which comes down to a population density of 8.4 inhabitants per square kilometer. Population density in the United States, which is the fourth largest country in the world, has about 33 inhabitants per sqkm. The Russian population is relatively concentrated around the big metropolitan areas of the country. About 75 percent of Russians live in an urban area. Moscow, the capital of Russia, has about 12 million inhabitants. The greater Moscow metropolitan area has about 16 million inhabitants and is the fifteenth largest metro area in the world. Most of the Russian population lives in the part of the country that is part of the European continent. About 74 percent of all Russians live west of the Ural on an area that encompasses only 23 percent of Russia's total land area.

Unemployment Rate: Aged 15 and Over: All Persons for the Russian Federation was 4.29% in October of 2021, according to the United States Federal Reserve. Historically, Unemployment Rate: Aged 15 and Over: All Persons for the Russian Federation reached a record high of 14.30 in January of 1999 and a record low of 4.29 in October of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate: Aged 15 and Over: All Persons for the Russian Federation - last updated from the United States Federal Reserve on June of 2025.

United States Exports of cotton yarn (cotton content less than 85%) to Russia was US$49.29 Thousand during 2011, according to the United Nations COMTRADE database on international trade. United States Exports of cotton yarn (cotton content less than 85%) to Russia - data, historical chart and statistics - was last updated on July of 2025.

From July 2023 to June 2024, more than half of the network intrusions by the Russian state or state-affiliated cyber threat actors were directed against the Middle East & North Africa. A further 23 percent of incidents targeted North America.

United States Imports from Russia of Iron Oxides and Hydroxides, Earth Color was US$2.34 Thousand during 2022, according to the United Nations COMTRADE database on international trade. United States Imports from Russia of Iron Oxides and Hydroxides, Earth Color - data, historical chart and statistics - was last updated on June of 2025.