Maps

This map shows the employment rate for the age group 55-74 years in the Nordic municipalities (big map) and regions (small map) in November 2022. Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 55-74 years. Older adults (55+) were less affected by the pandemic. In general, this age group has a stronger attachment to the labour market, and a higher proportion work in occupations where remote work was possible. For quite some time, the Nordic Region has boasted a higher employment rate among older adult workers compared to the rest of Europe, although this gap has narrowed somewhat in recent years. In 2022, 75.5% of the Nordic population aged 55–64 were employed, whereas the EU average stood at 62.3%, representing a difference of 13 percentage points. In 2012, however, this difference was 21 percentage points, and in 2002, it was 29. The map shows variation between the Nordic countries, with Finland exhibiting a lower employment rate for this age group. The highest rates were observed in the Faroe Islands (62%), Greenland (61%), Åland (56%), and the Swedish regions of Jämtland-Härjedalen (55%) and Jönköping (54%). The Labour Force Survey (LFS) is the official source for labour market statistics and the only source that is comparable across countries. The data comes from a standardised survey that is conducted in the same way in all EU countries, as well as in many non-EU countries, including in Norway, Iceland and the Faroe Islands. The LFS is the main source for the map, however since it is not possible to break down at a municipal level, register data has been made to create estimates at a municipal level.

These maps shows the employment rate in 2022 for those born in a EU country (top left) and those born outside of the EU (bottom left), as well as the change in employment rate between 2020 and 2022 for those born in the EU (upper right) and outside the EU (lower right). The data is displayed at NUTS 2 level and comes from the labour force survey (LFS). The category ‘foreign-born’ is quite heterogeneous and consists of everything from labour migrants to refugees – two groups who face quite different conditions and have different connections to the labour market. The employment rate for people born in another EU country – a group that includes a large proportion of labour migrants – has been on par with the employment rate for native-born people for a long time. As can be seen in the top-left figure in the map, in 2022 all NUTS2 regions except Southern Denmark had an employment rate of 75% or more for this group. The highest employment rate was observed in the Swedish NUTS2 regions of Middle Norrland, Stockholm and Western Sweden, followed by Oslo in Norway and Iceland. The employment rate for people born outside of the EU (a group that largely consists of refugees) has been lower for a long time than that of native-born people and those born in the EU. While the employment rate for people born in non-EU countries is still lower than for natives (a 15 percentage point difference (pp) in Sweden, 11 pp in Norway, 7 pp in Denmark and Finland, and 2 pp in Iceland), this gap has been closing in the last couple of years since the pandemic. Between 2020 and 2022, the employment rate for those born outside of the EU rose almost eight percentage points in Denmark…

This map shows the internal net migration in 2022 as a percentage of the total population. This map shows the internal net migration in 2022 as a percentage of the total population. The small map shows the result on a regional level, and the big map on the municipal level. Internal (sometimes referred to as domestic) net migration refers to migration between municipalities and regions within the same country. International migration is excluded. In 2022, internal net migration was positive (indicated by shades of blue) or at least balanced (shown in yellow) in several municipalities in central and southern Sweden and Norway, as well as in central and northern Finland – areas that traditionally were more likely to lose population due to internal migration. Conversely, several municipalities in the capital regions – such as Stockholm, Oslo, and Copenhagen – exhibited negative internal net migration. Several municipalities across the Nordic Region, including in more remote and rural areas, continued to register positive internal net migration in this year. In Iceland, positive migration predominated in most of the municipalities, and the Faroe Islands also presented positive net migration. Greenland oscillated mainly between positive and negative migration flows, while Åland presented the whole spectrum of positive, balanced, and negative net migration for this year.

This map shows the employment rate for Nordic municipalities (big map) and regions (small map) in 2022 (measured in November). Employment rate refers to the share of population in a particular age group that is in employment (LFS definition), in this map it is measured for the age group 20-64 years. At a national level, all of the Nordic countries have employment rates higher than the EU average (74.6%). The highest rates were in the Faroe Islands (93%), Åland (86%) and Iceland (83%), followed by Sweden (82.2%), Denmark (80.1%), Norway (80.9%), Finland (78.4%) and Greenland (75%). Of the EU countries, only the Netherlands (82.9%) had a higher employment rate than Sweden in 2022. As the map shows, most of the regions and municipalities had employment rates above 75% (the green areas on the map) in 2022, meaning that three-quarters of people aged 20–64 were working. In addition to the Faroe Islands, Iceland and Åland, the Swedish regions of Jämtland-Härjedalen, Halland and Jönköping also had employment rates of more than 85%. The only regions with employment rates under 75% were the Finnish regions of North and Southern Karelia and KeskiSuomi. On a municipal level, the highest employment rates were found in rural municipalities, mainly in Iceland and the Faroe Islands, as well as some municipalities in Österbotten in Finland (such as Pedersöe and Korsholm). Of the intermediate municipalities (between urban and rural), the highest employment rates were in the Swedish municipalities Habo (Jönköping), Hammarö (Värmland), Nykvarn (Stockholm), Gällivare (Norrbotten), Kungsbacka (Halland), Svedala (Skåne) and Mosfellsbær (Höfuðborgarsvæðið) in Iceland. Of the urban municipalities, the employment rate was highest in Garðabær and Kópavogur in the Capital Region of Iceland, Tyresö and Täby in the Stockholm region, Partille in Västra Götaland in Sweden, and Bærum and Rælingen in Viken in Norway. The lowest employment…

This Christmas, we’re gifting all fellow data nerds our first-ever interactive 3D map! The map displays the number of residents in each 1km grid cell, with the height of the bars representing the number of residents in each area.

This map shows the municipal and regional borders of the Nordic Region as of 1 January 2023*, the names refer to the regions. The Nordic Region is vast and has a diverse physical geography, stretching from the northern edge of the European mainland to north of the Arctic Circle. It consists of Denmark, Finland, Iceland, Norway and Sweden, as well as the three self-governing regions, the Faroe Islands and Greenland (both part of the Kingdom of Denmark) and Åland (part of the Republic of Finland). The table below summarises the administrative structure in each of the Nordic countries. These structures form the basis for the Nomenclature of Territorial Units for Statistics (NUTS) classification, a hierarchical system dividing European states into statistical units for research purposes. In general, the NUTS and Local Administrative Units (LAU) classifications follow existing divisions, but this may differ from country to country. Light grey frames represent the regional level presented In Nordregio’s map. As of 1 Jan 2023, there were 66 regions at this level. Dark grey frames show the local units represented in most of the municipal maps. As of 1 Jan 2023 there were 1,133 units at this level.

Voting turnout in 2023 (or latest national election). The map illustrates voter turnout as a percentage point relative to national averages, highlighting differences in participation levels between countries. This method removes inter-country differences in participation levels, providing a clearer view of the urban-rural divide. Lower turnout is observed in eastern Finland, northern Sweden, and the more rural parts of Denmark. In Norway, the lowest turnout occurs in the north and in municipalities outside Oslo. Nationally, the highest voter turnouts are in the Faroe Islands (88%), Sweden (84.2%), and Denmark (84.16%). Lower participation rates are found in Iceland (80.1%), Norway (77.2%), Finland (68.5%), and Greenland (65.9%).

Members of NATO (North Atlantic Treaty Organization) and non-NATO members as of March 2024. Countries that joined after 2015: Montenegro (2017), North Macedonia (2020), Finland (2023) and Sweden (2024). 

Members of NATO (North Atlantic Treaty Organization) and non-NATO members as of April 2023. Countries that joined after 2015: Montenegro (2017), North Macedonia (2020), and Finland (2023). 

The map shows all routes in our sample with significant travel time benefit for electric aviation. They are 203 in total. A route has a significant travel time benefit if the travel time for both car and public transportation exceeded 1,5 times the travel time for electric aviation. I.e., if one of the existing transport modes is faster or up to 1,5 times the travel time for electric aviation, electric aviation does not have the potential to improve accessibility between the two destinations, according to our analysis.

The map shows all routes between urban and rural areas where electric aviation has significant time benefits compared to other traffic modes. Yellow lines are already served by aviation, while blue color indicates non-existent routes where electric flight would reduce the travel time between destinations. Our motivation for focusing on urban-rural routes was based on the assumption that electric aviation can increase the access for rural areas to public facilities and job opportunities, as well as the possibility of connecting remote areas with national and international transport systems. The result, though, can only be understood in terms of travel time benefits between the areas, and thus reveals little about accessibility to mentioned opportunities. The following are examples of themes to be investigated further within the main project. Identify regional hubs Among others, the project FAIR (2022) has addressed the need to update the flight system to a more flexible aviation network, that meet travelers’ needs with smart mobility. This can be done by identifying demands and establishing regional hubs for electric aviation, which can serve remote and regional areas. The potential of Hamar and Bodö in Norway as regional hubs should be studied more closely.

The map shows all routes between urban areas separated by water, and where electric aviation has significant time benefits compared to the fastest traffic mode. Yellow lines are already served by aviation, while red color indicates non-existent routes where electric flight would reduce the travel time between destinations. The result is in line with our assumptions, that there is a lack of fast connections between potential labor markets in urban areas, which are geographically close but separated by open water.

The map visualizes all routes with significant travel time benefit, which are already served with commercial flights. Information on existing routes has been obtained from the report Nordic Sustainable Aviation (Ydersbond et al, 2020) and applies to the year 2019. Since then, routes may have been added or removed, which is important to bear in mind in future investigations. However, choosing a later year risk giving equally misleading results, as flights decreased drastically during the pandemic. Statistics for 2019 provide a picture of the demand that existed before the pandemic, which is the latest stable levels that can be obtained. Whether air traffic will ever return to the same levels as before the pandemic is too early to say.   The majority of routes are found in Norway, along the coastline, which confirms earlier knowledge that Norway has a more extensive and coherent aviation network than the rest of the Nordic region.

This map shows the travel time calculations for electric aviation versus travelling by public transportation. Routes represented by any nuance of green, are routes with significant travel time benefits for electric aviation in comparison with public transportation. The darker the nuance of green, the larger time benefit for electric aviation. The beige color represents routes where the travel time for public transportation is the same or up to 1,5 times the travel time for electric aviation. The red color represents routes where public transportation is faster than electric aviation. Purple lines represent routes where no public transportation is available.  These were also routes where we could see significant time benefits for electric aviation. The number of changes when commuting with public transport may have a negative impact on perceived accessibility. In this accessibility analysis, however, we stay with the same criteria for public transport as for travel by car. For future research, the number of changes when commuting by public transport could be considered in the comparison.

This map shows the travel time calculations for electric aviation versus traveling by car. Routes represented by any nuance of green, are routes with significant travel time benefits for electric aviation in comparison with car. The darker the nuance of green, the larger time benefit for electric aviation. The beige color represents routes where the travel time for car is the same or up to 1,5 times the travel time for electric aviation. The red color represents routes where car is faster than electric aviation.

The map shows all routes with a maximum distance of 200 km divided into three categories, based on the airports’ degree of urbanization: Routes between two rural airports, routes between one rural and one urban airport and routes between two urban airports. The classification is based on the new urban-rural typology. We restricted the analysis to routes between rural and urban areas as well as routes between urban areas that are separated by water. Those are 426 in total. We based our criteria on the assumption that accessibility gains to public services and job clusters can be made for rural areas, if better connected to areas with a high degree of urbanization. Because of possible potential to link labor markets between urban areas on opposite sides of water urban to urban areas that cross water are also included. This is based on previous research which has shown the potential for electric aviation to connect important labor markets which are separated by water, particularly in the Kvarken area (Fair, 2022). Our choice of selection criteria means that we intentionally ignore routes where electric aviation may have a potential to reduce travel times significantly. There might also be other important reasons for the implementation of electric aviation between the excluded routes. Between rural areas, for example, tourism or establishing a comprehensive transport system in the Nordic region, constitute reasons for implementing electric aviation. Regarding routes between urban areas over mainland, the inclusion of more routes with the same rationale as above – that significant time travel benefits could be gained between labor markets with electric aviation (for example between two urban areas in mountainous regions where travel times can be long) – can be motivated. Some of those routes can be important to investigate at a later stage but are outside the…

This map classifies all airports by a degree of urbanisation. The classification is based on the new urban-rural typology. We classified all airports localized within any of the top five urbanization classes (Inner urban area, Local center in rural area, Outer urban area, peri-urban area, or Rural area close to close to urban) as Urban. All other airports, localized within the bottom two classes (Rural heartland or Sparsely populated rural area) were classified as Rural. No adjustments were made based on the proximity of the airports to urban areas. During the process we considered adjustments in the categorization based on the airports’ potential catchment area from a close urban area. For example, one can assume that Gällivare Lappland airport in the north of Sweden, has its main catchment area from Gällivare which is classified as a local center in rural area (i.e. Urban). The airport, though, is localized within the category Rural heartland. Yet, we decided to let the typology determine to which category each airport belong.

This map shows all possible electric aviation routes of a maximum distance of 200 kilometres within the Nordic region. First generation electric aviation will have a limited range due to battery capacity. According to the report Nordic Sustainable Aviation, routes up to 400 kilometers constitute an initial market for electric airplanes in the Nordic region. However, also shorter distance routes under 200 km, where cruise speed is less important and in sparsely populated regions where passenger volumes are very small, will be the focus (Ydersbond et al, 2020). The first generation of aircrafts that rely solely on electric power have a defined maximum range of 200 km (Heart Areospace, 2022). For this accessibility study, we only included routes of a maximum distance of 200 kilometers. This selection gave us 1001 possible routes in total.

This map shows all airports within the geographical scope which may be operated with commercial flight. To limit our selection of airports, we used a combination of two official airport code systems: IATA (International Air Transport Association) and ICAO (International Civil Aviation Organization). IATA-codes specify the airport as a part of a commercial flight route. However, the IATA system, is not solely limited to airports. Other locations, such as bus or ferry stations can also apply for an IATA location code, as long it is included in an airline travel chain. The ICAO-code, on the other hand, indicates that the location is an airport, but not necessarily for commercial flights In order to obtain a selection of airports that met our criteria, an airport was included only if it had both an IATA-code and an ICAO-code.  Three different sources are used: 1) Swedavia (lists all airports in the Nordics that Swedavia traffics today). This is our main source, but it does not include all existing airports in the Nordic countries. Therefore, we also use two other sources: 2) Avcodes: Airport code database, from which other airports, that are not served by Swedavia, are obtained. 3) Wikipedia. Finally, the listed airports are checked against Wikipedia, to verify if any airports have been missed through the other sources. This selection gave us 186 airports in total.

A map portrays tertiary education attainment level around the Nordics, based on a grid level data.