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.

Finland: Population density, people per square km: The latest value from 2021 is 18 people per square km, unchanged from 18 people per square km in 2020. In comparison, the world average is 456 people per square km, based on data from 196 countries. Historically, the average for Finland from 1961 to 2021 is 16 people per square km. The minimum value, 15 people per square km, was reached in 1961 while the maximum of 18 people per square km was recorded in 2009.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

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 July of 2025.

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;

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

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.

In 2023, the share of urban population in Finland remained nearly unchanged at around 85.77 percent. Still, the share reached its highest value in the observed period in 2023. A population may be defined as urban depending on the size (population or area) or population density of the village, town, or city. The urbanization rate then refers to the share of the total population who live in an urban setting. International comparisons may be inconsistent due to differing parameters for what constitutes an urban center.Find more key insights for the share of urban population in countries like Faroe Islands and Sweden.

10,5 (Inhabitants per sq. km) in 2016.

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.

34,6 (Inhabitants per sq. km) in 2016.

Background: The reduction of child and adolescent deaths (defined as decedents aged 0–19 years) remains a crucial public health priority also in high-income countries such as Finland. There is evidence of a relationship between socioeconomic gradients and child mortality, but the association is considered complex and relatively poorly understood. Exploiting a Finnish dataset with nationwide coverage, the present study aimed to shed light on the sociodemographic predictors of child and adolescent mortality at the municipality level.Methods: A public database of Statistics Finland was queried for municipality-level data on sociodemographic traits and child and adolescent deaths in Finland during the years 2011–2018. The sociodemographic indicators included total population size, child and adolescent population size, sex distribution, mean age, education, unemployment, median income, population density, rurality, percentage of individuals living in their birth municipality, household size, overcrowded households, foreign language speakers, divorce rate, car ownership rate, and crime rate. The sociodemographic indicators were modeled against child and adolescent mortality by means of generalized estimating equations.Results: A total of 2,371 child and adolescent deaths occurred during the 8-year study period, yielding an average annual mortality rate of 26.7 per 100,000 individuals. Despite a fluctuating trend, the average annual decline in child and adolescent deaths was estimated to be 3% (95% confidence interval 1–5%). Of the sociodemographic indicators, population density was associated with higher child and adolescent mortality (rate ratio 1.03, 95% confidence interval 1.01–1.06), whereas the percentage of foreign language speakers was associated with lower child and adolescent mortality (0.96, 0.93–0.99).Conclusion: Densely populated areas should be the primary focus of efforts to reduce child and adolescent mortality. Of note is also the apparently protective effect of foreign language speakers for premature mortality. Future studies are welcomed to scrutinize the mediating pathways and individual-level factors behind the associations detected in this study.

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.

Criteria for a framework of ecosystem services quantification based on Boerema, Rebelo [12], comments on the performance and success level in following the criteria for our framework.

39,4 (Inhabitants per sq. km) in 2016.

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.

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..., ,