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:

1. HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.
2. HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.
3. HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.
4. 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

1. 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.
2. 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

Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Technology Hardware and Equipment data was reported at 1,183.121 NA in Feb 2025. This records an increase from the previous number of 946.497 NA for Jan 2025. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Technology Hardware and Equipment data is updated monthly, averaging 1,176.226 NA from Jul 2020 (Median) to Feb 2025, with 56 observations. The data reached an all-time high of 2,114.090 NA in Jan 2023 and a record low of 779.468 NA in Aug 2024. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Technology Hardware and Equipment data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Finland – Table FI.EDI.SE: Nasdaq Helsinki: Monthly.

Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki General Industrials data was reported at 5,989.490 NA in Feb 2025. This records an increase from the previous number of 5,977.884 NA for Jan 2025. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki General Industrials data is updated monthly, averaging 4,291.625 NA from Jan 2012 (Median) to Feb 2025, with 158 observations. The data reached an all-time high of 7,115.970 NA in Aug 2021 and a record low of 1,047.530 NA in Jan 2012. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki General Industrials data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Finland – Table FI.EDI.SE: Nasdaq Helsinki: Monthly.

Helsinki Region Travel Time Matrix contains travel time and distance information for routes between all 250 m x 250 m grid cell centroids (n = 13231) in the Helsinki Region, Finland by walking, cycling, public transportation and car. The grid cells are compatible with the statistical grid cells used by Statistics Finland and the YKR (yhdyskuntarakenteen seurantajärjestelmä) data set. The Helsinki Region Travel Time Matrix is available for three different years:

• 2018
• 2015
• 2013

The data consists of travel time and distance information of the routes that have been calculated between all statistical grid cell centroids (n = 13231) by walking, cycling, public transportation and car.

The data have been calculated for two different times of the day: 1) midday and 2) rush hour.

The data may be used freely (under Creative Commons 4.0 licence). We do not take any responsibility for any mistakes, errors or other deficiencies in the data.

Organization of data

The data have been divided into 13231 text files according to destinations of the routes. The data files have been organized into sub-folders that contain multiple (approx. 4-150) Travel Time Matrix result files. Individual folders consist of all the Travel Time Matrices that have same first four digits in their filename (e.g. 5785xxx).

In order to visualize the data on a map, the result tables can be joined with the MetropAccess YKR-grid shapefile (attached here). The data can be joined by using the field ‘from_id’ in the text files and the field ‘YKR_ID’ in MetropAccess-YKR-grid shapefile as a common key.

Data structure

The data have been divided into 13231 text files according to destinations of the routes. One file includes the routes from all statistical grid cells to a particular destination grid cell. All files have been named according to the destination grid cell code and each file includes 13231 rows.

NODATA values have been stored as value -1.

Each file consists of 17 attribute fields: 1) from_id, 2) to_id, 3) walk_t, 4) walk_d, 5) bike_f_t, 6) bike_s_t, 7) bike_d, 8) pt_r_tt, 9) pt_r_t, 10) pt_r_d, 11) pt_m_tt, 12) pt_m_t, 13) pt_m_d, 14) car_r_t, 15) car_r_d, 16) car_m_t, 17) car_m_d, 18) car_sl_t

The fields are separated by semicolon in the text files.

Attributes

• from_id: ID number of the origin grid cell
• to_id: ID number of the destination grid cell
• walk_t: Travel time in minutes from origin to destination by walking
• walk_d: Distance in meters of the walking route
• bike_f_t: Total travel time in minutes from origin to destination by fast cycling; Includes extra time (1 min) that it takes to take/return bike
• bike_s_t: Total travel time in minutes from origin to destination by slow cycling; Includes extra time (1 min) that it takes to take/return bike
• bike_d:Distance in meters of the cycling route
• pt_r_tt: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account including the waiting time at home
• pt_r_t: Travel time in minutes from origin to destination by public transportation in rush hour traffic; whole travel chain has been taken into account excluding the waiting time at home
• pt_r_d: Distance in meters of the public transportation route in rush hour traffic
• pt_m_tt: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account including the waiting time at home
• pt_m_t: Travel time in minutes from origin to destination by public transportation in midday traffic; whole travel chain has been taken into account excluding the waiting time at home
• pt_m_d: Distance in meters of the public transportation route in midday traffic
• car_r_t: Travel time in minutes from origin to destination by private car in rush hour traffic; the whole travel chain has been taken into account
• car_r_d: Distance in meters of the private car route in rush hour traffic
• car_m_t: Travel time in minutes from origin to destination by private car in midday traffic; the whole travel chain has been taken into account
• car_m_d: Distance in meters of the private car route in midday traffic
• car_sl_t: Travel time from origin to destination by private car following speed limits without any additional impedances; the whole travel chain has been taken into account

METHODS

For detailed documentation and how to reproduce the data, see HelsinkiRegionTravelTimeMatrix2018 GitHub repository.

THE ROUTE BY CAR have been calculated with a dedicated open source tool called DORA (DOor-to-door Routing Analyst) developed for this project. DORA uses PostgreSQL database with PostGIS extension and is based on the pgRouting toolkit. MetropAccess-Digiroad (modified from the original Digiroad data provided by Finnish Transport Agency) has been used as a street network in which the travel times of the road segments are made more realistic by adding crossroad impedances for different road classes.

The calculations have been repeated for two times of the day using 1) the “midday impedance” (i.e. travel times outside rush hour) and 2) the “rush hour impendance” as impedance in the calculations. Moreover, there is 3) the “speed limit impedance” calculated in the matrix (i.e. using speed limit without any additional impedances).

The whole travel chain (“door-to-door approach”) is taken into account in the calculations:
1) walking time from the real origin to the nearest network location (based on Euclidean distance),
2) average walking time from the origin to the parking lot,
3) travel time from parking lot to destination,
4) average time for searching a parking lot,
5) walking time from parking lot to nearest network location of the destination and
6) walking time from network location to the real destination (based on Euclidean distance).

THE ROUTES BY PUBLIC TRANSPORTATION have been calculated by using the MetropAccess-Reititin tool which also takes into account the whole travel chains from the origin to the destination:
1) possible waiting at home before leaving,
2) walking from home to the transit stop,
3) waiting at the transit stop,
4) travel time to next transit stop,
5) transport mode change,
6) travel time to next transit stop and
7) walking to the destination.

Travel times by public transportation have been optimized using 10 different departure times within the calculation hour using so called Golomb ruler. The fastest route from these calculations are selected for the final travel time matrix.

THE ROUTES BY CYCLING are also calculated using the DORA tool. The network dataset underneath is MetropAccess-CyclingNetwork, which is a modified version from the original Digiroad data provided by Finnish Transport Agency. In the dataset the travel times for the road segments have been modified to be more realistic based on Strava sports application data from the Helsinki region from 2016 and the bike sharing system data from Helsinki from 2017.

For each road segment a separate speed value was calculated for slow and fast cycling. The value for fast cycling is based on a percentual difference between segment specific Strava speed value and the average speed value for the whole Strava data. This same percentual difference has been applied to calculate the slower speed value for each road segment. The speed value is then the average speed value of bike sharing system users multiplied by the percentual difference value.

The reference value for faster cycling has been 19km/h, which is based on the average speed of Strava sports application users in the Helsinki region. The reference value for slower cycling has been 12km/, which has been the average travel speed of bike sharing system users in Helsinki. Additional 1 minute have been added to the travel time to consider the time for taking (30s) and returning (30s) bike on the origin/destination.

More information of the Strava dataset that was used can be found from the Cycling routes and fluency report, which was published by us and the city of Helsinki.

THE ROUTES BY WALKING were also calculated using the MetropAccess-Reititin by disabling all motorized transport modesin the calculation. Thus, all routes are based on the Open Street Map geometry.

The walking speed has been adjusted to 70 meters per minute, which is the default speed in the HSL Journey Planner (also in the calculations by public transportation).

All calculations were done using the computing resources of CSC-IT Center for Science (https://www.csc.fi/home).

This dataset collection consists of one or more dataset tables sourced from the website of 'Helsingin kaupunkiympariston toimiala' in Finland. The dataset collection contains data related to streets and roads in Helsinki. The tables provide information about the geographic locations, names, and other attributes of the streets and roads in the city. The dataset collection is named 'avoindatamelualueet_2017_12_kadut_maant_hki_lyo'.

With 1.3 Million Businesses in Finland , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

Province of Nyland and Tavastehus Uudenmaan ja Hämeen lääni Nylands och Tavastehus län Helsinki / Hämeenlinna

Province of Ostrobothnia Pohjanmaan lääni Österbottens län Oulu / Vaasa

Province of Viborg and Nyslott Viipurin ja Savonlinnan lääni Viborgs och Nyslotts län Vyborg

Province of Kexholm Käkisalmen lääni Kexholms län Kexholm

Province of Kymmenegård and Nyslott Savonlinnan ja Kymenkartanon lääni Kymmenegårds och Nyslotts län Lappeenranta

Province of Savolax and Kymmenegård Kymenkartanon ja Savon lääni Savolax och Kymmenegårds län Loviisa

Province of Vaasa Vaasan lääni Vasa län Vaasa

Province of Oulu Oulun lääni Uleåborgs län Oulu

Province of Kymmenegård Kymenkartanon lääni Kymmenegårds län Heinola

Province of Savolax and Karelia Savon ja Karjalan lääni Savolax och Karelens län Kuopio

Province of Viipuri Viipurin lääni Viborgs län Vyborg

Province of Uusimaa Uudenmaan lääni Nylands län Helsinki

Province of Häme Hämeen lääni Tavastehus län Hämeenlinna

Province of Mikkeli Mikkelin lääni St. Michels län Mikkeli

Province of Kuopio Kuopion lääni Kuopio län Kuopio

Province of Åland Ahvenanmaan lääni Ålands län Mariehamn

Province of Petsamo Petsamon lääni Petsamo län Pechenga

Province of Lapland Lapin lääni Lapplands län Rovaniemi

Province of Kymi Kymen lääni Kymmene län Kouvola

Province of Central Finland Keski-Suomen lääni Mellersta Finlands län Jyväskylä

Province of North Karelia Pohjois-Karjalan lääni Norra Karelens län Joensuu

Province of Southern Finland Etelä-Suomen lääni Södra Finlands län Hämeenlinna

Province of Western Finland Länsi-Suomen lääni Västra Finlands län Turku

Province of Eastern Finland Itä-Suomen lääni Östra Finlands län Mikkeli

The main stock market index in Finland (Helsinki 25) increased 409 points or 9.47% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks this benchmark index from Finland. Finland Stock Market Index (Helsinki 25) - values, historical data, forecasts and news - updated on March of 2025.

Helsinki is on course to meet its target to reduce its greenhouse gas emissions by 30 percent by 2020 having already achieved 25 percent reductions since 1990. The city is now preparing to meet its next important target of carbon neutrality by 2050 Relying predominantly on the supply of cleaner electricity and heating in the future.

As a way of modelling the city’s progress towards this long term goal, Helsinki’s 2030 Climate Technologies — City Performance Tool -report looks to 2030 as an observation year to identify the most cost effective technologies that can be driven by the municipality. The study concerns CO2-emissions, air pollutants, employment and cost efficiency of the measures that were selected by the city experts.

In this study we modelled two scenarios for Helsinki’s emission development in 2015-2030: the Business As Usual1 (BAU) scenario and the City Performance Tool (CyPT) scenario, which features additional technologies that will bring greenhouse gas savings. The measures excluded actions related to centralised energy production, which are considered in the Helen development plan.

The results of the study are in open usage for all interests groups. The excel below includes the fundamental data set that was generated to calculate the current CO2 emissions and the future scenarios. The Excel consists of various sheets, contact information, general city data, energy data, transport data and building data. Research in English: http://www.stadinilmasto.fi/en

Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Financial Services data was reported at 5,217.489 NA in Feb 2025. This records an increase from the previous number of 4,847.313 NA for Jan 2025. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Financial Services data is updated monthly, averaging 2,745.645 NA from Jan 2012 (Median) to Feb 2025, with 158 observations. The data reached an all-time high of 6,373.430 NA in Aug 2021 and a record low of 622.010 NA in Jul 2012. Finland Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Financial Services data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s Finland – Table FI.EDI.SE: Nasdaq Helsinki: Monthly.

634 Global export shipment records of Helsinki with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

This dataset collection consists of one or more dataset tables sourced from the website of Helsingin kaupunkiympäristön toimiala in Finland.

5117 Global import shipment records of Helsinki with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Supplementary data used in the analysis of distributed temperature sensing (DTS) measurements from Helsinki, Finland, as described in a journal article manuscript "Quantifying coastal urban surface layer structure using distributed temperature sensing in Helsinki, Finland".

Eddy covariance, radiation and precipitation data is provided from the SMEAR III station by the Institute for Atmospheric and Earth System Research at the University of Helsinki under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). The data can also be accessed programmatically via https://smear.avaa.csc.fi/. All SMEAR III data is time referenced to UTC+2.

The 2-metre temperature data is provided by the Finnish Meteorological Institute under Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). All Finnish Meteorological Institute data is referenced to UTC.

Data source: Finnish Meteorological Institute

This is wave and meteorological data collected in the Helsinki archipelago and GoF durin 2012-2018. Each file contains data and metadata for one location. The Gulf of Finland (GoF) site has a separate file for all integrated data (WaveData_GoF_integrated.nc), while separate files (WaveData_GoF_spectra_2016a.nc etc) exist for the spectra. This is because both a DWR Mk-III and DWR4/ACM wave buoy was used, and they have different sampling frequencies. Coinciding wind data is embedded in each file.

The data are described in the publication "The wave spectrum in archipelagos", Ocean Science, 2019, DOI: 10.5194/os-15-1469-2019

The data contain data on the population aged 15 or over in Helsinki by age, sex and level of education as of 31 December 2015.

The graduated population means graduates in upper secondary schools, vocational colleges, universities of applied sciences and universities. Those who have completed a degree at a separate examination event in accordance with the Act on Vocational Qualifications also belong to the graduated population. The classification is based on the international ISCED classification.

The data are based on Statistics Finland’s Register of Qualifications. Graduates are classified by level of education by highest/last completed vocational qualification. The statistics contain data on graduates in upper secondary schools, vocational education and training institutions, universities of applied sciences, universities of applied sciences and higher education institutions, as well as on those who have completed a vocational undergraduate, vocational or special vocational qualification as a competence degree. The register of qualifications is based on the degree data collected in the 1970 census, which are updated annually.

Degree of Education

Statistics Finland introduced a revised classification of education in the classification of data relating to 1998. The longer the education is, the higher the level of education. The level of education is determined primarily according to the target level of the degree. The target level is based, among other things, on official curricula, indicative training lengths, basic training requirements and further study qualifications.

— 1-2) Basic education covers the performance of the entire comprehensive school, previous middle school and old primary school. — 3) Secondary education includes matriculation examinations, vocational qualifications of one to three years, basic vocational qualifications, vocational qualifications and specialist vocational qualifications.

Higher education includes:

5) Lowest level. This includes degrees in agrologist, hortone, artenom and nurse, which are not polytechnic degrees. — 6) Lower tertiary level. This includes polytechnic degrees and lower university degrees, as well as engineers, forestry engineers and sea captains, among others. 7) Higher level of higher education. This includes Master’s Degrees (master’s degrees) as well as doctoral specialisations. —8) Graduate level of research. Degrees are scientific licentiate and doctoral degrees.

Reference date

The data for Helsinki are from 2015 onwards.

Dataset Card for United Nations Parallel Corpus

  Dataset Summary

The United Nations Parallel Corpus is the first parallel corpus composed from United Nations documents published by the original data creator. The parallel corpus consists of manually translated UN documents from the last 25 years (1990 to 2014) for the six official UN languages, Arabic, Chinese, English, French, Russian, and Spanish. The corpus is freely available for download under a liberal… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/un_pc.

Employed labour force in the Helsinki region by age and gender as of 31 December 1980.

Lower ranges

The Helsinki region consists of four municipalities in the Helsinki metropolitan area: Helsinki, Espoo, Vantaa and Kauniainen, and from 2000 onwards 10 framing municipalities: Hyvinkää, Järvenpää, Kerava, Kirkkonummi, Mäntsälä, Nurmijärvi, Pornainen, Sipoo, Tuusula and Vihti. In addition, there is information about the whole country. The series contains data from Mäntsälä and Pornnainen since 2000. They are also missing from the sum data of the Helsinki region and the municipalities in the peri-urban area. The Helsinki region is a housing and labour market area.

Data source

Statistics Finland Employment statistics. The data are inferred from administrative and statistical registers and other data.

Target set and cropping

The employed labour force includes all persons aged 18 to 74 who, during the last week of the year, were in gainful employment and were not unemployed as jobseekers at an employment office or performing military or non-military service. Information on employment is based on data from the earnings-related pension and tax authorities. Before 2005, the age group was 15-74 years old.

As of 2005, persons aged 18 to 68 are covered by earnings-related pension insurance, whereas previously the obligation to take out earnings-related pension insurance has already started from the age of 14. This is reflected in the employment statistics from 2005 onwards as a decrease in youth employment and an increase in the number of students. Statistics on the employment of minors cannot be reliably compiled on the basis of register data.

This dataset provides information about the number of properties, residents, and average property values for Helsinki Road cross streets in Green Bay, WI.

This dataset contains files that show the climate change velocity metrics calculated for three climate variables across Finland. The climate velocities were used to study the magnitude of projected climatic changes in a nation-wide Natura 2000 protected area (PA) network (Heikkinen et al., 2020). Using fine-resolution climate data that describes the present-day and future topoclimates and their spatio-temporal variation, the study explored the rate of climatic changes in protected areas on an ecologically relevant, but yet poorly explored scale. The velocities for the three climate variables were developed in the following work, where in-depth description of the different steps in velocity metrics calculation and a number of visualisations of their spatial variation across Finland are provided: Risto K. Heikkinen 1, Niko Leikola 1, Juha Aalto 2,3, Kaisu Aapala 1, Saija Kuusela 1, Miska Luoto 2 & Raimo Virkkala 1 2020: Fine-grained climate velocities reveal vulnerability of protected areas to climate change. Scientific Reports 10:1678. https://doi.org/10.1038/s41598-020-58638-8 1 Finnish Environment Institute, Biodiversity Centre, Latokartanonkaari 11, FI-00790 Helsinki, Finland 2 Department of Geosciences and Geography, University of Helsinki, FI-00014, Helsinki, Finland 3 Finnish Meteorological Institute, FI-00101, Helsinki, Finland The dataset includes GIS compatible geotiff files describing the nine spatial climate velocity surfaces calculated across the whole of Finland at 50 m × 50 m spatial resolution. These nine different velocity surfaces consist of velocity metric values measured for each 50-m grid cell separately for the three different climate variables and in relation to the three different future climate scenarios (RCP2.6, RCP4.5 and RCP8.5). The baseline climate data for the study were the monthly temperature and precipitation data averaged for the period from 1981 to 2010 modelled at a resolution of 50-m, based on which estimates for the annual temperature sum above 5 °C (growing degree days, GDD, °C), the mean January temperature (TJan, °C) and the annual climatic water balance (WAB, the difference between annual precipitation and potential evapotranspiration; mm) were calculated. Corresponding future climate surfaces were produced using an ensemble of 23 global climate models for the years 2070–2099 (Taylor et al. 2012) and the three RCPs. The data for the three climate variables for 1981–2010 and under the three RCPs will be made available in separately via METIS - FMI's Research Data repository service (Aalto et al., in prep.). The climate velocity surfaces included in the present data repository were developed using climate-analog approach (Hamann et al. 2015; Batllori et al. 2017; Brito-Morales et al. 2018), whereby velocity metrics for the 50-m grid cells were measured based on the distance between climatically similar cells under the baseline and the future climates, calculated separately for the three climate variables. In Heikkinen et al. (2020), the spatial data for the Natura 2000 protected areas were used to assess their exposure to climate change. The full data on N2K areas can be downloaded from the following link: https://ckan.ymparisto.fi/dataset/%7BED80465E-135B-4391-AA8A-FE2038FB224D%7D. However, note that the N2K areas including multiple physically separate patches were treated as separate polygons in Heikkinen et al. (2020), and a minimum size requirement of 2 hectares were requested. Moreover, the digital elevation model (DEM) data for Finland (which were dissected to Natura 2000 polygons to examine their elevational variation and its relationships to topoclimatic variation) can be downloaded from the following link: https://ckan.ymparisto.fi/en/dataset/dem25_astergdem25. The coordinate system for the climate velocity data files is: ETRS-TM35FIN (EPSG: 3067) (or YKJ Finland/Finnish Uniform Coordinate System (EPSG: 2393)). Summary of the key settings and elements of the study are provided below. A detailed treatment is provided in Heikkinen et al. (2020). Code to the files (four files per each velocity layer: *.tif, *.tfw. *.ovr and .tif.aux.xml) in the dataset: (a) Velocity of GDD with respect to RCP2.6 future climate (Fig 2a in Heikkinen et al. 2020). Name of the file: GDDRCP26. (b) Velocity of GDD with respect to RCP4.5 future climate (Fig. 2b in Heikkinen et al. 2020). Name of the file: GDDRCP45.* (c) Velocity of GDD with respect to RCP8.5 future climate (Fig. 2c in Heikkinen et al. 2020). Name of the file: GDDRCP85.* (d) Velocity of mean January temperature with respect to RCP2.6 future climate (Fig. 2d in Heikkinen et al. 2020). Name of the file: TJanRCP26.* (e) Velocity of mean January temperature with respect to RCP4.5 future climate (Fig. 2e in Heikkinen et al. 2020). Name of the file: TJanRCP45.* (f) Velocity of mean January temperature with respect to RCP8.5 future climate (Fig. 2f in Heikkinen et al. 2020). Name of the file: TJanRCP85.* (g) Velocity of climatic water balance with respect to RCP2.6 future climate (Fig. 2g in Heikkinen et al. 2020). Name of the file: WABRCP26.* (h) Velocity of climatic water balance with respect to RCP4.5 future climate (Fig. 2h in Heikkinen et al. 2020). Name of the file: WABRCP45.* (i) Velocity of climatic water balance with respect to RCP8.5 future climate (Fig. 2i in Heikkinen et al. 2020). Name of the file: WABRCP85.* Note that velocity surfaces e and f include disappearing climate conditions. Summary of the study: Climate velocity is a generic metric which provides useful information for climate-wise conservation planning to identify regions and protected areas where climate conditions are changing most rapidly, exposing them to high rates of climate displacement (Batllori et al. 2017), causing potential carry-over impacts to community structure and ecosystem functions (Ackerly et al. 2010). Climate velocity has been typically used to assess the climatic risks for species and their populations, but velocity metrics can also be used to identify protected areas which face overall difficulties in retaining ecological conditions that promote present-day biodiversity. Earlier climate velocity assessments have focussed on the domains of the mesoclimate (resolutions of 1–100 km) or macroclimate (>100 km scales), and fine-grained (<100 m) local climatic conditions created by variation in topography ('topoclimate'; Ackerly et al. 2010; 2020) have largely been overlooked (Heikkinen et al. 2020). This omission may lead to biased exposure assessments especially in rugged terrain (Dobrowski et al. 2013; Franklin et al. 2013), as well as a limited ability to detect sites decoupled from the regional climate (Aalto et al. 2017; Lenoir et al. 2017). This study provided the first assessment of the climatic exposure risks across a national PA (Natura 2000) network based on very fine-grained velocities of three established drivers of high latitude biodiversity. The produce fine-grain climate velocity measures, 50-m resolution monthly temperature and precipitation data averaged for 1981–2010 were first developed, and based on it, the three bioclimatic variables (growing degree days, mean January temperature and annual climatic water balance) were calculated for the whole study domain. In the next phase, similar future climate surfaces were produced based on data from an ensemble of 23 global climate models, extracted from the CMIP5 archives for the years 2070–2099 and the three RCP scenarios (RCP2.6, RCP4.5 and RCP8.5)26. In the final step, climate velocities for each the 50 x 50 m grid cells were measured using climate-analog velocity method (Hamann et al. 2015) and based on the distance between climatically similar cells under the baseline and future climates. The results revealed notable spatial differences in the high velocity areas for the three bioclimatic variables, indicating contrasting exposure risks in protected areas situated in different areas. Moreover, comparisons of the 50-m baseline and future climate surfaces revealed a potential wholesale disappearance of current topoclimatic temperature conditions from almost all the studied PAs by the end of this century. Calculation of climate change velocity metrics for the three climate variables The overall process of calculation of climate velocities included three main steps. (1) In the first step, we developed high-resolution monthly average temperature and precipitation data averaged over the years 1981–2010 and across the study domain at a spatial resolution of 50 × 50 m. This was done by building topoclimatic models based on climate data sourced from 313 meteorological stations (European Climate Assessment and Dataset [ECA&D]) (Klok et al. 2009). Our station network and modelling domain covered the whole of Finland with an additional 100 km buffer. However, it was also extended to cover large parts of northern Sweden and Norway for areas >66.5°N, as well as selected adjacent areas in Russia (for details see Heikkinen et al. 2020). This was done to capture the present-day climate spaces in Finland which are projected to move in the future beyond the country borders but have analogous climate areas in neighbouring areas; this was done to avoid developing a large number of velocity values deemed as infinite or unknown in the data for Finland. The 50-m resolution average air temperature data were developed for the study domain using generalized additive modelling (GAM), as implemented in the R-package mgcv version 1.8–7 (R Development Core Team 2011; Wood 2011). In this modelling we utilised variables of geographical location (latitude and longitude, included as an anisotropic interaction), topography (elevation, potential incoming solar radiation, relative elevation) and water cover (sea and lake proximity), and subsequent leave-one-out cross-validation tests to assess model performance (for full process description, see Aalto et al. 2017; Heikkinen et

In 2023 the number of pupils in pre-primary education in Finland amounted to 52,570, of whom roughly 16,732 attended pre-primary education in the capital region of Uusimaa including Helsinki.