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;

As of 2024, approximately 5.22 million inhabitants in Finland were Finnish citizens, while around 411,100 had a foreign nationality. Over the shown period, the non-Finnish population increased by roughly 191,400.

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.

Demographics data Finland 2022 at municipality level - 200+ attributes, including family size, family composition, occupancy type and more

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

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.

Finland - Distribution of population by household types: Single person was 25.80% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by household types: Single person - last updated from the EUROSTAT on September of 2025. Historically, Finland - Distribution of population by household types: Single person reached a record high of 25.80% in December of 2024 and a record low of 19.00% in December of 2010.

This bar chart displays population (people) by region using the aggregation sum in Finland. The data is filtered where the date is 2021. The data is about countries per year.

Finland - Distribution of population by tenure status, type of household and income group - EU-SILC survey was 68.10% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by tenure status, type of household and income group - EU-SILC survey - last updated from the EUROSTAT on September of 2025. Historically, Finland - Distribution of population by tenure status, type of household and income group - EU-SILC survey reached a record high of 74.30% in December of 2010 and a record low of 68.10% in December of 2024.

Finland - Distribution of population by household types: Two adults younger than 65 years was 16.20% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Distribution of population by household types: Two adults younger than 65 years - last updated from the EUROSTAT on October of 2025. Historically, Finland - Distribution of population by household types: Two adults younger than 65 years reached a record high of 20.20% in December of 2010 and a record low of 16.20% in December of 2024.

Estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

Please note that these data represent 2025 Alpha release versions, constructed in September 2025

This bar chart displays population (people) by ISO 3 country code using the aggregation sum in Finland. The data is about countries per year.

This bar chart displays rural population (people) by ISO 2 country code using the aggregation sum in Finland. The data is filtered where the date is 2021. The data is about countries per year.

This bar chart displays female population (people) by ISO 2 country code using the aggregation sum in Finland. The data is filtered where the date is 2021. The data is about countries per year.

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.

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.