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

Historical dataset of population level and growth rate for the Helsinki, Finland metro area from 1950 to 2025.

The Helsinki Biobank is part of the FinnGen biobank consortium. All individuals from Finland of legal age (18 years and over) have the right to participate in biobank research, and guardians can provide consent on behalf of minors. Providing samples to the biobank is voluntary and is based on written consent. Participants are recruited through attending the University of Helsinki, Kymsote, Eksote or Päijät-Häme Hospital, all in Finland, or by returning a consent form online.

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).

Finland Index: HSE: Month End: OMX Helsinki 25 data was reported at 3,792.810 04Mar1988=500 in Nov 2018. This records a decrease from the previous number of 3,960.230 04Mar1988=500 for Oct 2018. Finland Index: HSE: Month End: OMX Helsinki 25 data is updated monthly, averaging 2,386.310 04Mar1988=500 from Sep 2001 (Median) to Nov 2018, with 207 observations. The data reached an all-time high of 4,353.740 04Mar1988=500 in Aug 2018 and a record low of 1,107.380 04Mar1988=500 in Mar 2003. Finland Index: HSE: Month End: OMX Helsinki 25 data remains active status in CEIC and is reported by Helsinki Stock Exchange. The data is categorized under Global Database’s Finland – Table FI.Z001: Helsinki Stock Exchange: Index.

Finland Shares: HSE: Market Value data was reported at 223,114.536 EUR mn in 2017. This records an increase from the previous number of 208,839.445 EUR mn for 2016. Finland Shares: HSE: Market Value data is updated yearly, averaging 154,609.117 EUR mn from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 349,350.852 EUR mn in 1999 and a record low of 9,920.141 EUR mn in 1991. Finland Shares: HSE: Market Value data remains active status in CEIC and is reported by Helsinki Stock Exchange. The data is categorized under Global Database’s Finland – Table FI.Z004: Helsinki Stock Exchange: Shares.

See the average Airbnb revenue & other vacation rental data in Helsinki in 2025 by property type & size, powered by Airbtics. Find top locations for investing.

Dataset Card for OPUS News-Commentary

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  Source… See the full description on the dataset page: https://huggingface.co/datasets/Helsinki-NLP/news_commentary.

This dataset is based on a new model for nationwide small area divisions for Finland. It has been done in GSFI project funded by Eurostat. Project grant agreement number is 101112903 - 2022-FI-GEOS-GSFI.

Pilot contains data from five municipalities (Helsinki, Vantaa, Espoo, Kajaani and Parainen). The readme-file (ReadMeGSFI_pilot_En.txt) descibes the dataset and method in more detail. Please notice that this is a pilot dataset that may contain flaws and errors.

During the project development process: 1. A stakeholder survey was conducted on the need and content of small areas. 2. decision to propose creating an area classification system where small areas are based on smaller building blocks as base areas. 3. Different approaches and datasets were tested for forming these base areas. 4. A pilot version of the actual small areas was created based on these base areas. The work resulted in a method where, from GIS-datasets, base areas are formed, and from those, small areas.

The proposed small area division model is based on an approach where a dataset of very finely detailed base areas is first created. This dataset serves as the foundation for defining the actual statistical small areas. The base areas consider urban structure and geographical factors. With base areas, it's possible to create small areas based on various criteria. The definition of statistical small areas relies on population size criteria, ensuring data privacy when publishing statistical information.

The pilot version is a draft and proposal, not yet the final dataset. When forming the final dataset, it is important to note that there are some gaps in the GIS-datasets used for base-areas, requiring manual checks. Additionally, the data generated using the automated method includes some objects that are not optimally delimited. The proposal is based on a vision of a target situation where, using this dataset, different types of small areas for various needs can be easily produced with different boundary values and criteria.

This dataset provides information about the number of properties, residents, and average property values for Helsinki Circle cross streets in Hollywood, FL.

Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Precious Metals and Mining data was reported at 182.532 NA in Apr 2025. This records a decrease from the previous number of 196.536 NA for Mar 2025. Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Precious Metals and Mining data is updated monthly, averaging 221.195 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 604.940 NA in Feb 2012 and a record low of 50.483 NA in Oct 2023. Nasdaq Helsinki: Index: Gross Total Return: OMX Helsinki Precious Metals and Mining 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's main stock market index, the Helsinki 25, rose to 5466 points on December 3, 2025, gaining 0.45% from the previous session. Over the past month, the index has climbed 0.58% and is up 23.90% compared to the same time last year, 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 December of 2025.

This is a parallel corpus made out of PDF documents from the European Medicines Agency. All files are automatically converted from PDF to plain text using pdftotext with the command line arguments -layout -nopgbrk -eol unix. There are some known problems with tables and multi-column layouts - some of them are fixed in the current version.

source: http://www.emea.europa.eu/

22 languages, 231 bitexts total number of files: 41,957 total number of tokens: 311.65M total number of sentence fragments: 26.51M

This dataset provides information about the number of properties, residents, and average property values for Helsinki Circle cross streets in Boca Raton, FL.

The register of public areas (YLRE) is a system of the Helsinki Urban Environment Division that contains information about the city’s street and green areas. The dataset is maintained for the use of the division and does not, as a rule, cover the assets in public areas that are the responsibility of the City of Helsinki’s other organisational departments or institutions. The information in the register of public areas is used to order maintenance for street and park areas in Helsinki. The information in the register is used, for example, in reporting on the activities of the Street and Park Department and in asset management of public areas.

The data includes the location of object in street and green areas and their classification into assets on the basis of their intended use or functionality, for example. Material and template information of the objects is also included. The dataset includes the maintenance categories of street and green areas, as well as the parties responsible for the maintenance and winter care of the objects. Owner and rental information is also stored for some feature.

YLRE data is published as a WMS and WFS service divided into nine different map levels: street areas, green areas, street sections (areas, points, lines), green sections (areas, points, lines) and bicycle and pedestrian traffic areas.

The street areas and green areas contain large area geometries. The division of areas is largely based on detailed plan entries. The street and green sections specify the contents of the area-like geometries of the street and green areas. Street and green sections can be area, line or point geometries on top of the street and green areas. Three different geometry types are on their respective levels. Area-type street or green sections may include, for example, driving lanes, park walkways or planting areas. Line-type section include, for example, different fences or walls. The sections recorded as points are often furniture or accessories, such as benches, play equipment or waste containers.

The features on the Bicycle and pedestrian traffic level are included in the normal levels of area-type street and green sections, but they have also been combined to form their own level to facilitate the use of the data.

The register is updated on the basis of street and park plans, detailed plans, area planning and topographic surveying. Some of the data have been compiled on the basis of aerial photographs. There are shortcomings and errors in the information on the features, which should be taken into account in the use of the data. Particularly for point features, such as furniture and equipment, the information may not correspond to the actual situation. Some of the boundaries of the street and green are based on terrain measurements, while others are adapted indicatively from aerial photographs, and therefore the boundaries are not precise. In the absence of material information, the default material is recorded as the material of the feature. The owner and rental information for the features is not recorded comprehensively.

The YLRE data in the WFS service is updated once a day at night, so the data provided by the service is the situation of the previous day from the register.

Example query for the WFS service: https://kartta.hel.fi/ws/geoserver/avoindata/wfs?version=1.0.0&request=GetFeature&typeName=avoindata:YLRE_Katualue_alue&maxFeatures=5

The data is available for download in several formats, including JSON, KML, CSV, Esri Shape, and XML. Available formats can always be found under OutputFormat in the GetCapabilities query for the service.

Example of a CSV search: https://kartta.hel.fi/ws/geoserver/avoindata/wfs?request=GetFeature&service=WFS&version=1.1.0&typeName=avoindata:YLRE_Katualue_alue&outputFormat=csv

The fields of the map levels of the service are described in a separate document: https://kartta.hel.fi/avoindata/dokumentit/2019_ylre_tietokuvaukset.pdf (in Finnish)

Source: Register of public areas in the City of Helsinki.

Previewing the data in the kartta.hel.fi service:

• Street Areas
• All map layers can be found on the website: https://kartta.hel.fi/ > Layers > Streets and Parks > Street Areas

Coordinate systems:

• ETRS-GK25 (EPSG:3879)

API address:

• WMS API: https://kartta.hel.fi/ws/geoserver/avoindata/wms
• WFS API: https://kartta.hel.fi/ws/geoserver/avoindata/wfs

Layers:

• YLRE_Katu_ja_viherosat_ajorata_alue
• YLRE_Katu_ja_viherosat_eiliikenne_alue
• YLRE_Katu_ja_viherosat_eiliikenne_piste
• YLRE_Katu_ja_viherosat_eiliikenne_viiva
• YLRE_Katu_ja_viherosat_kevytliikenne_alue
• YL

Comprehensive Airbnb dataset for Helsinki, Finland providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

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

The dataset contains a photorealistic 3D mesh model from Kalasatama region in Helsinki, Finland. Dataset was created using both terrestrial laser scanning (Leica RTC360) and UAV-based (DJI P4 Pro+) photogrammetry. 3D reconstruction was completed with RealityCapture without manual mesh or texture editing. The dataset contains 30 million polygons in OBJ file format and 50 8k texture files in PNG file format. Model can be further optimized per application basis. Work was done in Aalto University (The Research Institute of Measuring and Modeling for the Built Environment) with support from the City of Helsinki.

Urban fabrics for the Helsinki region 2016, 2030 and 2050 GIS dataset represents modelled urban fabric areas (walking, transit, and automobile urban fabrics) in the Helsinki city region (14 municipalities) in Finland. The data is associated with the regional MAL 2019 (land use, housing, and transport) work and was developed at the Finnish Environment Institute (Syke). The method used to produce the data has also been applied to other city regions in Finland (Helminen et al., 2020) and is an application of Newman et al.'s (2016) theory of three urban fabrics. The method is based on the overlay analysis of three variables: population and job density, accessibility to local services, and public transportation supply, with threshold values set for each variable. The definition of threshold values is based on previous applications of urban fabrics (Ristimäki et al., 2017) and a workshop conducted for urban planning and transportation professionals in the Helsinki metropolitan area. All accessibility measures used in creating the data were calculated as Euclidean distances.

The data was created using ArcMap Advanced software (version 10.6) and includes shapefiles for each modeling year's urban structures (UF_2016, UF_2030, and UF_2050) as well as description styles (UF_Fi.qml and UF_En.qml) in Finnish and English for the QGIS software. The names of the structures are in the fields 'Kudos' (in Finnish) and 'UrbFab' (in English). The coordinate system of the data is EPSG:3067. Detailed descriptions of the data and the method can be found in the report 'Helsingin seudun kaupunkikudokset 2016, 2030 a 2050' (Tiitu et al., 2018, in Finnish) and in the downloadable ReadMe files below (both in Finnish and English).

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto kuvaa mallinnettuja kaupunkikudosten alueita (jalankulku-, joukkoliikenne- ja autokaupunki) Helsingin seudun (14 kuntaa) alueelta Suomesta. Aineisto liittyy seudun MAL 2019 -työhön, ja se on kehitetty Suomen ympäristökeskuksessa (Syke). Menetelmää, jolla aineisto on tuotettu, on sovellettu myös muille Suomen kaupunkiseuduille (Helminen ym. 2020), ja se on sovellutus Newmanin ym. (2016) kolmen kaupunkikudoksen teoriasta. Menetelmä perustuu päällekkäisanalyysiin kolmesta muuttujasta: asukas- ja työpaikkatiheys, lähikaupan saavutettavuus ja joukkoliikenteen tarjonta, sekä muuttujille asetettuihin kynnysarvoihin. Kynnysarvojen määrittely perustui kaupunkikudosten aiempiin sovellutuksiin (Ristimäki ym. 2017) sekä Helsingin seudun maankäytön ja liikenteen suunnittelijoille suunnattuun työpajaan. Kaikki aineiston muodostamiseen käytetyt saavutettavuudet on laskettu linnuntie-etäisyyksinä.

Aineisto on muodostettu ArcMap Advanced -ohjelmistolla (versio 10.6.) ja se sisältää shp-tiedostot kunkin mallinnusvuoden kaupunkikudoksille (UF_2016, UF_2030 ja UF_2050) sekä kuvaustekniikan (UF_Fi.qml ja UF_En.qml) suomeksi ja englanniksi QGIS-ohjelmistolle. Kudosten nimet ovat sarakkeissa Kudos (suomeksi) ja UrbFab (englanniksi). Aineiston koordinaattijärjestelmä on EPSG:3067. Aineiston ja menetelmän tarkka kuvaus on luettavissa raportista Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 (Tiitu ym. 2018) sekä alla ladattavista ReadMe-tiedostoista.

References

Helminen V., Tiitu M., Kosonen, L. & Ristimäki, M. (2020). Identifying the areas of walking, transit and automobile urban fabrics in Finnish intermediate cities. Transportation Research Interdisciplinary Perspectives 8, 100257. https://doi.org/10.1016/j.trip.2020.100257

Newman, L. Kosonen & J. Kenworthy (2016). Theory of urban fabrics; planning the walking, transit/public transport and automobile/motor car cities for reduced car dependency. Town planning Review 87 (4): 429–458. http://hdl.handle.net/20.500.11937/11247

Ristimäki M., Tiitu M., Helminen V., Nieminen H., Rosengren K., Vihanninjoki V., Rehunen A., Strandell A., Kotilainen A., Kosonen L., Kalenoja H., Nieminen J., Niskanen S. & Söderström P. (2017). Yhdyskuntarakenteen tulevaisuus kaupunkiseuduilla – Kaupunkikudokset ja vyöhykkeet. Suomen ympäristökeskuksen raportteja 4/2017. Suomen ympäristökeskus, Helsinki. http://hdl.handle.net/10138/176782

Tiitu M., Helminen V., Nurmio K. & Ristimäki M. (2018). Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050. MAL 2019 publication. https://www.hsl.fi/sites/default/files/uploads/helsingin_seudun_kaupunkikudokset_loppuraportti_27082018_0.pdf

License / Lisenssi

Syke applies Creative Commons By 4.0 International license for open datasets.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. The source references for credits can be found in the metadata of each data product.

Suomen ympäristökeskuksen (Syke) avointen aineistojen käyttölupa on Creative Commons Nimeä 4.0 Kansainvälinen.
Lisenssin kohteena olevaa dataa voi vapaasti käyttää kaikin mahdollisin tavoin edellyttäen, että datan lähde mainitaan: Lisenssinantajan nimi ja aineiston nimi.

Credits / Lähdemerkintä

Urban Fabrics for the Helsinki Region / Source: Finnish Environment Institute Syke 2018.
Where applicable, please also cite the references listed above.

Helsingin seudun kaupunkikudokset / Lähde: Syke 2018.
Viittaa myös soveltuvin osin yllä listattuihin lähteisiin, jos hyödynnät näitä aineistoja esimerkiksi raporteissa tai tutkimusartikkeleissa.

The data includes computational and measured daily values from the Finnish Meteorological Institute's weather observation station. The calculated values are the daily average air temperature and the daily precipitation. Measured values include minimum and maximum air temperature, minimum ground temperature and snow depth. The extremes of air temperature are measured at 06 and 18 UTC and the minimum ground temperature at 06 UTC. These represent extreme values over the previous 12-hour period (18-06 and 06-18 UTC).