In 2024, the population density of Finland was 18.5 inhabitants per square kilometer. The most densely populated region was Uusimaa with approximately 195.7 inhabitants per square kilometer. Lapland was the most scarcely populated region with roughly two inhabitants per square kilometer.

Population density (people per sq. km of land area) in Finland was reported at 18.28 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Finland - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

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

The most populous area in Finland is the capital region of Uusimaa, with almost 1.8 million inhabitants as of 2024. Almost one third of the 5.64 million population lived in the capital city and the surrounding Greater Helsinki area. The second-largest region in terms of population was Pirkanmaa, inhabited by 545,406 people. Three out of the ten largest cities located in Uusimaa The Uusimaa region also has Finland's highest population density with roughly 195.7 inhabitants per square kilometer. Pirkanmaa's population density is only 41.2 inhabitants per square meter. Out of the 10 largest cities in the country, three are located in the Uusimaa region, including the capital city Helsinki. Changing population structure The population of Finland is expected to grow in the following decade, reaching 6.18 million in 2050. However, the population is aging rapidly, as the number of inhabitants aged 75 years and older continues to increase in the future. At the same time, the population aged 14 and younger is estimated to constantly decline.

Finland FI: Population Density: People per Square Km data was reported at 18.136 Person/sq km in 2017. This records an increase from the previous number of 18.083 Person/sq km for 2016. Finland FI: Population Density: People per Square Km data is updated yearly, averaging 16.299 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 18.136 Person/sq km in 2017 and a record low of 14.646 Person/sq km in 1961. Finland FI: 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 Finland – Table FI.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;

Comprehensive socio-economic dataset for Finland 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.

Density of nursing and midwifery personnel of Finland increased by 2.38% from 18.9 number per thousand population in 2019 to 19.3 number per thousand population in 2020. Since the 0.53% fall in 2016, density of nursing and midwifery personnel rose by 3.09% in 2020.

The white-tailed deer is an important game species in Finland. We evaluated the potential of estimating the white-tailed deer pre-harvest density using non-invasive DNA collection within a spatial capture–recapture (SCR) framework. We sampled faeces during three weekly visits in autumn 2015 from 180, 20 x 20 m plots clustered in groups of four. Individual identification was based on 12–14 microsatellites. Of the 245 faecal samples collected, an individual could be identified from only 36 (15%). We identified 27 white-tailed deer individuals of which seven were recaptured. The SCR model produced a plausible density estimate (3.5 indiv. km–2) which was similar to estimates based on dung count and large-scale national estimates, although a posteriori simulation showed the SCR estimate was likely positive biased. Although we found that SCR based on faecal DNA can provide pre-harvest density estimates of the white-tailed deer, the approach is not without challenges and we discuss these suggesting possible solutions.

With 450,295 square kilometers, Sweden is the largest Nordic country by area size, followed by Finland and Norway. This makes it the fifth largest country in Europe. Meanwhile, Denmark is the smallest of the five Nordic countries with only 43,094 square kilometers, however, the Danish autonomous region of Greenland is significantly larger than any of the Nordic countries, and is almost double the size of the other five combined.

Population

Sweden is also the Nordic country with the largest population. 10.45 million people live in the country. Denmark, Finland, and Norway all have between five and six million inhabitants, whereas only 370,000 people live in Iceland. Meanwhile, Denmark has the highest population density of the five countries. Greenland is the most sparsely populated permanently-inhabited country in the world, followed by the regions of Svalbard and Jan Mayen.

Geography

The five Nordic countries vary geographically. While Denmark is mostly flat, its highest point only stretching around 170 meters above sea level, Norway's highest peak is nearly 2,500 meters high. Moreover, Finland is known for its many lakes and is often called the land of a thousand lakes, whereas Iceland is famous for its volcanoes.

Random encounter models can be used to estimate population abundance from indirect data collected by non-invasive sampling methods, such as track counts or camera-trap data. The classical Formozov–Malyshev–Pereleshin (FMP) estimator converts track counts into an estimate of mean population density, assuming that data on the daily movement distances of the animals are available. We utilize generalized linear models with spatio-temporal error structures to extend the FMP estimator into a flexible Bayesian modelling approach that estimates not only total population size, but also spatio-temporal variation in population density. We also introduce a weighting scheme to estimate density on habitats that are not covered by survey transects, assuming that movement data on a subset of individuals is available. We test the performance of spatio-temporal and temporal approaches by a simulation study mimicking the Finnish winter track count survey. The results illustrate how the spatio-temporal modelling approach is able to borrow information from observations made on neighboring locations and times when estimating population density, and that spatio-temporal and temporal smoothing models can provide improved estimates of total population size compared to the FMP method.

Study site descriptions showing size and land use for forest, agriculture, water bodies, urban area and nature reserves as percentage of the total area, as well as average population density and the proximity of the closest city to the catchment.

In heterogeneous landscapes, resource selection constitutes a crucial link between landscape and population-level processes such as density. We conducted a non-invasive genetic study of white-tailed deer in southern Finland in 2016 and 2017 using fecal DNA samples to understand factors influencing white-tailed deer density and space use in late summer prior to the hunting season. We estimated deer density as a function of landcover types using a spatial capture-recapture (SCR) model with individual identities established using microsatellite markers. The study revealed second-order habitat selection with highest deer densities in fields and mixed forest, and third-order habitat selection (detection probability) for transitional woodlands (clear-cuts) and closeness to fields. Including landscape heterogeneity improved model fit and increased inferred total density compared with models assuming a homogenous landscape. Our findings underline the importance of including habitat covariates w..., ,

Physicians (per 1,000 people) in Finland was reported at 4.381 in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. Finland - Physicians - actual values, historical data, forecasts and projections were sourced from the World Bank on September of 2025.

Quantified ecosystem services, including corresponding CICES code [9] for reference, and their estimated monetary annual values in € ha-1 year-1 in each study site.

Different FES values (top row) are dependent variables, and five study site characteristics (percentage clay soil, average terrain slope, average landscape diversity (SDI), average population in a 5 km radius around the cell and the fraction of water of total land cover in the subcatchment) are independent variables.

Activation date: 2014-04-22
Event type: Other

Activation reason:
Service Request: The nation-wide asset mapping for Finland provides a detailed regional geospatial dataset for the quick and uncomplicated calculation of potential damages either in the preparedness phase or during the immediate response phase of crises caused by natural hazard events. The concept follows the Basic European Asset Map (BEAM) data model developed under the Copernicus precursor project SAFER (Services and Applications for Emergency Response) and extended in the FP7 project IncREO (Increasing Resilience through Earth Observation).BEAM Finland is a comprehensive dataset comprising of a set of spatialized economic indicator values and a population density value. All economic attributes are expressed in EURO/m². By using GIS methods for intersecting BEAM data with hazard intensity information and appropriate vulnerability functions quick regional estimates can be made for exposure of assets and population, damage assessments and cost/benefit analysis.The wall-to-wall map and vector dataset depicts assets for various economic categories as well as for population density. The data are derived by combining socioeconomic data and land use/cover data. Fourteen distinct contributing attributes for the asset mapping are provided (e.g. buildings, households, industry, agriculture, etc.). Assets information is made available not only as a cumulative layer of different types of assets (e.g. private households, industry, commerce, vehicles, agriculture, etc.), but as accessible single contributing layers as well, each of them expressing its value.

Ticks and tick-borne diseases (TBDs) form a significant and growing threat to human health and well-being in Europe, with increasing numbers of tick-borne encephalitis (TBE) and Lyme borreliosis cases being reported during the past few decades. Increasing knowledge of tick risk areas and seasonal activity remains the primary method for preventing TBDs. Crowdsourcing provides the best alternative for rapidly obtaining data on tick occurrence on a national level. In order to produce and share up-to-date data about tick risk areas in Finland, an online platform, Punkkilive (www.punkkilive.fi/en), was launched in April 2021. On the website, users can submit and browse tick observations, report tick numbers and hosts, and upload pictures of ticks. Here, we looked at trends in the crowdsourced data from 2021, assessed the effect of local tick species on seasonality of observations, and examined sampling bias in the data. The high number of tick observations (n=78 837) highlights that there was demand for such a service. Approximately 97% of 5573 uploaded pictures represented ticks. Seasonal patterns of tick observations varied across Finland, highlighting variability in the risk associated with the two human-biting tick species Ixodes ricinus and I. persulcatus, the latter having a shorter, unimodal activity peak in late spring–early summer. Tick numbers were low and the proportion of new sightings was high in northern Finland, as may be expected near the latitudinal distribution limits of both species. While the number of inhabitants generally explained the number of tick observations well, geographically weighted regression models also identified areas that deviated from this general pattern. This study offers a prime example of how crowdsourcing can be applied to track vectors of zoonotic diseases, to the benefit of both researchers and the public. Areas with more or fewer observations than predicted based on number of inhabitants were revealed, wherein more specific analyses may reveal factors contributing to lower or higher risk levels that may be used in increasing awareness. We hope that the success of Punkkilive serves to highlight the usefulness of citizen science in the prevention of vector-borne diseases. Methods The data was collected through an open website for reporting tick observations in Finland, Punkkilive (www.punkkilive.fi/en). Data is presented on the level of Finnish administrative regions, as it is presented also in the manuscript. Data is number of tick observations and answers to different categories of questions that are asked when reporting tick observations. Data used in geographically weighted regression models is tick observations and population density on the grid level (10 x 10 km) in three chosen administrative regions.

Gray wolf (Canis lupus) predation on domestic dogs (Canis familiaris) is a considerable wolf-human conflict issue in several regions of Europe and North America but has not been well documented in the scientific literature. Livestock depredations by wolves may be related to the abundance of wild prey. Regardless of the presumed motivations of wolves for attacking dogs (likely due to interference competition and predation), the abundance of wild prey populations may also influence the risk of wolf attacks on dogs. We examined whether the annual number of tatal attacks by wolves on dogs was related to the abundance of primary prey, including wild boar (Sus scrofa) and roe deer (Capreolus capreolus) in Estonia, as well as the abundance of moose (Alces alces) in Finland. Statistical models resulted in significant negative relationships, thus providing evidence that the risk of attacks in both house yards (Estonia) and hunting situations (Finland) was highest when the density of wild prey wa...

a Position according to genome build GRCh37.b Fisher’s exact test for enrichment of allele between the Finnish SISU exome samples (N > 2026) and CDGP samples.DNAH6 variants in CDGP probands.

This data is produced for white-tailed eagle population study at the University of Turku, Finland, to study density-dependence in natal dispersal. Data consists of 285 white-tailed eagle individuals in Finland. For each individual the natal dispersal distance and local breeder density (number of active territories) within 10 km and 30 km buffer is included.