Among the regions in Sweden, the the capital region Stockholm county had the highest population density in 2022, with 374.6 inhabitants per square kilometers. In 2021, more than 2.4 million people lived in Stockholm. In terms of highest population density, Stockholm county was followed by Skåne, with 129 inhabitants per square kilometer. The least populated county was Norrbotten, with only 2.6 inhabitants per square kilometer.

Increasing population density

The population in Sweden is increasing steadily and reached 10.52 million inhabitants in 2022. Because of the growing population, the population density in Sweden increased as well over the past 10 years. In 2012, there were 23.4 inhabitants per square kilometer and in 2022 the number had increased to 25.8. Despite this, Sweden is a relatively sparsely populated country.

Highest rent per square meter in Stockholm

As the most densely populated county, the rents for rented dwellings in Stockholm were higher than in Sweden’s other counties. In 2020, the average rent per square meter in Stockholm county amounted to almost 1,300 Swedish kronor, while the rent in Norrbotten, the least populated county, reached an average of 999 Swedish kronor per square meter.

Sweden SE: Population Density: People per Square Km data was reported at 24.718 Person/sq km in 2017. This records an increase from the previous number of 24.362 Person/sq km for 2016. Sweden SE: Population Density: People per Square Km data is updated yearly, averaging 20.697 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 24.718 Person/sq km in 2017 and a record low of 18.326 Person/sq km in 1961. Sweden SE: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sweden – Table SE.World Bank: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Population density (people per sq. km of land area) in Sweden was reported at 25.75 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Sweden - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

The population density in Sweden increased over the past 10 years, reaching 25.9 inhabitants per square kilometer in 2023. During that year, the population of Sweden reached 10.55 million.

Stockholm county had the highest population density

Sweden consists of 21 counties, administrative regions that primarily control public healthcare, public transport, and culture within the county. Among these, the most populated county is the capital region, Stockholm county, with a population density of 375 inhabitants per square kilometer in 2022. Stockholm county is followed by Skåne, with 129 inhabitants per square kilometer. The least populated county is Norrbotten, with only 2.6 inhabitants per square kilometer.

Land area of the Scandinavian countries

Though the population density in Sweden is increasing, the country still has a lot of surface area compared to its population. Of the Scandinavian countries, Sweden is the largest with a land area of over 447,000 square kilometers, but Norway is larger if the islands of Svalbard and Jan Mayen are taken into account.

Historical dataset showing Sweden population density by year from 1961 to 2022.

Of the total population in Sweden of 10.55 million people, around half resided in the counties Stockholm, Västra Götaland or Skåne. This is also the three counties where the three largest cities in Sweden, Stockholm, Göteborg, and Malmö, are located. In the capital region Stockholm county, there lived nearly 2.5 million inhabitants in 2023. Västra Götaland county had close to 1.8 million inhabitants, while Skåne county, the southernmost region, had roughly 1.4 million inhabitants. The island Gotland had the lowest number of inhabitants with only 60,000.

The highest population density

Stockholm, Skåne and Västra Götaland were also the three counties in Sweden with the highest population density. In 2022, 374.6 inhabitants per square kilometer lived in Stockholm county, while the corresponding figures for Skåne and Västra Götaland were 129 and 73.9, respectively.

The highest rents

Unsurprisingly. Stockholm county is the county in Sweden with the highest rents for rented dwellings, with average prices for one square meter amounting to over 1,400 Swedish kronor in 2022. The lowest average renting prices were in the northwestern region Jämtland, one square meter costing 1,000 Swedish kronor.

Sweden SE: Population Density: Inhabitants per sq km data was reported at 25.750 Person in 2022. This records an increase from the previous number of 25.570 Person for 2021. Sweden SE: Population Density: Inhabitants per sq km data is updated yearly, averaging 22.290 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 25.750 Person in 2022 and a record low of 21.010 Person in 1990. Sweden SE: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Sweden – Table SE.OECD.GGI: Social: Demography: OECD Member: Annual.

Comprehensive socio-economic dataset for Sweden 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.

The population density in Stockholm increased steadily since 2010, reaching ***** inhabitants per square kilometer in 2022. This corresponds with the constant population growth in the city during the same period.

Denmark has, by far, the highest population density of the Nordic countries. This is related to the fact that it is the smallest Nordic country in terms of land area. Meanwhile, Iceland, which has the smallest population of the five countries, also has the lowest population density. As the total population increased in all five countries over the past decade, the population density also increased.

58.5 (Inhabitants per sq. km) in 2016.

Background

Native Swedish sheep breeds are part of the North European short-tailed sheep group; characterized in part by their genetic uniqueness. Our objective was to study the population structure of native Swedish sheep. Five breeds were genotyped using the 600 K SNP array. Dalapäls and Klövsjö sheep are from the middle of Sweden; Gotland and Gute sheep from Gotland, an island in the Baltic Sea; and Fjällnäs sheep from northern Sweden. We studied population structure by: principal component analysis (PCA), cluster-based analysis of admixture, and an estimated population tree.

Results

The analyses of the five Swedish breeds revealed that these breeds are five distinct breeds, while Gute and Gotland are more closely related to each other as seen in all analyses. All breeds had long branch lengths in the population tree indicating they’ve been subjected to drift. We repeated our analyses using 39 K SNP and including 50 K SNP genotypes from other European and southwestern Asian ...

Synthetic populations for regions of the World (SPW) | Sweden

Dataset information

A synthetic population of a region as provided here, captures the people of the region with selected demographic attributes, their organization into households, their assigned activities for a day, the locations where the activities take place and thus where interactions among population members happen (e.g., spread of epidemics).

License

CC-BY-4.0

Acknowledgment

This project was supported by the National Science Foundation under the NSF RAPID: COVID-19 Response Support: Building Synthetic Multi-scale Networks (PI: Madhav Marathe, Co-PIs: Henning Mortveit, Srinivasan Venkatramanan; Fund Number: OAC-2027541).

Contact information

Henning.Mortveit@virginia.edu

Identifiers

Statistics

Sources

Files description

Base data files (swe_data_v_0_9.zip)

Derived data files

Validation and measures files

The data contains three variables, that is, 1)YEAR, 2) estimates of ADULT DENSITY from line transect counts of willow grouse in central Sweden between 1964 and 2019, and 3) the standardized brood size in the same period. Note that data on brood size is missing between 1995 and 2003. Abstract of article Decisions on management policies require insights in how populations respond to different actions. The form and strength of negative density feedback is central in understanding population response, but identifying the proper function can be challenging when the relative magnitude of stochastic variation is high. Observation error in addition to natural process error will bias population variability and estimate of density dependence. Hierarchical state-space models can be used to separate process and observation errors, and recent advances in Bayesian framework and MCMC methods have increased their popularity. Here we develop a hierarchical state-space model, where the process equation is a Gompertz model with per capita variation in breeding success added as a stochastic process. We use data from a 56 year line transect monitoring program of a willow grouse (Lagopus lagopus) population in south central Sweden. The model fit resulted in a carrying capacity of 8.608 adults per km2 and a λ_max of 3.20, which is close to the 4.5 based on maximum survival and fecundity values. Accounting for a stochastic density-independent per captita breeding success resulted in a substantially reduced processes error (standard deviation), 0.2410 compared to 0.1638. Maximum growth is expected to occur at 2.208 adults per km2. Combining both process and observation error resulted in a CV-value of 0.163 at carrying capacity, which is similar to previously reported range of CV-values of many bird populations. However, only using the reduced process error result in a CV of 0.076. A bootstrap test for monotonic trend was statistically insignificant, probably due to a steady increase in density the last six years of the time series. We conclude that state-space models to separate observation and process error can provide important information on population dynamics, but that effort should be made to estimate measurement error independently. There is a lack of data on population dynamics at low densities, and we suggest that additional experimental harvest should be considered to improve the understanding of negative density feedback in relation to stochastic processes at low densities.

人口密度：每平方公里的居民在12-01-2022达25.750人，相较于12-01-2021的25.570人有所增长。人口密度：每平方公里的居民数据按年更新，12-01-1990至12-01-2022期间平均值为22.290人，共33份观测结果。该数据的历史最高值出现于12-01-2022，达25.750人，而历史最低值则出现于12-01-1990，为21.010人。CEIC提供的人口密度：每平方公里的居民数据处于定期更新的状态，数据来源于Organisation for Economic Co-operation and Development，数据归类于全球数据库的瑞典 – Table SE.OECD.GGI: Social: Demography: OECD Member: Annual。

Stockholm is the Capital of Sweden and in 2023, close to 985,000 people lived in the municipality. Since 2010, the population there has been growing consistently. While more people are moving to Stockholm, the city area is not growing at the same speed, leading the population density to increase as well. Forecasts for the city expect continuous growth of population over the next forty years.

Economy

In Stockholm, the Gross Domestic Product (GDP) per capita was around 734,000 Swedish kronor in 2021. That was much higher than the average GDP per capita in all of Sweden with around 517,000 SEK in 2021. Though it must be noted that living costs are very high in the city and have been increasing in the last years. For example, the average rent per square meter in Stockholm has been rising every single year.

Employment A high majority of people living in Stockholm have a workplace. The employment rate in Stockholm is at 73.6 percent as of 2021.  The sector with the highest number of employees in Stockholm is professional, scientific, technical, and administrative activities, followed by wholesale and retail trade.

SE：人口密度：每平方公里人口在12-01-2017达24.718Person/sq km，相较于12-01-2016的24.362Person/sq km有所增长。SE：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为20.697Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达24.718Person/sq km，而历史最低值则出现于12-01-1961，为18.326Person/sq km。CEIC提供的SE：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的瑞典 – 表 SE.世界银行：人口和城市化进程统计。

Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

Publication available at: https://doi.org/10.1038/s41598-021-01857-4

Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

After centuries of intense persecution, several large carnivore species in Europe and North America have experienced a rebound. Today's spatial configuration of large carnivore populations has likely arisen from the interplay between their ecological traits and current environmental conditions, but also from their history of persecution and protection. Yet, due to the challenge of studying population-level phenomena, we are rarely able to disentangle and quantify the influence of past and present factors driving the spatial distribution and density of these controversial species. Using spatial capture-recapture models and a data set of 742 genetically identified wolverines Gulo gulo collected over ½ million km2 across their entire range in Norway and Sweden, we identify landscape-level factors explaining the current population density of wolverines in the Scandinavian Peninsula. Distance from the relict range along the Swedish-Norwegian border, where the wolverine population survived a ...

This dataset has been prepared by Szilárd Erhart & Kornél Erhart for a research paper on 'Application of North European characterisation factors, population density and distance-to-coast grid data for refreshing the Swedish human toxicity and ecotoxicity footprint analysis', published by the Environmental Assessment Review,

ABSTRACT Here, we develop further the national chemical footprint assessment methods using Sweden as an example to enhance the precision of calculations. First, we integrate grid data on population density and distance-to-seacoast into the analytical framework to better match the European Pollutant Release and Transfer Register on the sub-compartment level with USEtox toxicity characterisation factors. Second, we use the latest USEtox 2.12 model version and its more punctual North European characterisation factors. Third, we conduct trend and geographic analysis and rank Swedish facilities in terms of toxicity potential. We show that total human toxicity potential in Sweden was smaller than previously estimated when using the North European USEtox landscape settings and sloped downwards over time. We confirm toxicity potential of major pollutants in previous research papers (Zn, Hg, Pb, Ni) and find that Hg’s relative human toxicity potential in a longer period can be larger than previously estimated on shorter periods. Human toxicity is estimated to be mostly non-cancer type in Sweden. Results are largely invariant to the choice of air sub-compartments. Companies in the metals manufacturing sector are estimated to have the largest human toxicity potential in Sweden in the period between 2001 and 2017 and companies in the paper manufacturing industry have the largest ecotoxicity potential.

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