Maps

This map shows the share of foreign-born of the total population in the Nordic countries. This map shows the share of foreign-born of the total population in Nordic municipalities (big map) and regions (small map), and municipalities (big map) in 2022. Iceland has the highest share of foreign-born residents in the Nordic Region, at 22%. Mýrdalshreppur, the municipality in the south containing the village of Vik, has the largest foreign-born population, at 58%. It is also the only municipality in the Nordic Region with a majority non-native population. Other municipalities in the south, some of which are quite small, also have significant foreign-born populations. Reykjanesbær, near Keflavik airport, is the largest municipality with a sizeable foreign-born population, at 29%. In Reykjavíkurborg, 20% of the population is foreign-born, about the same as the national average. Many municipalities with tiny populations in the Westfjords and the north also have small shares of foreign-born persons. In 2022, 17% of the population of Norway was foreign-born. Municipalities with high shares of foreign-born include Oslo (28%), several suburban municipalities near Oslo, and a few in the north – which have small overall populations but large numbers of foreign workers employed in the fishing industry. In Sweden, 20% of residents are foreign-born, with large differences in distribution by region and municipality. At the regional level, Stockholm has the highest share of foreign-born persons (27%), followed by Skåne, including the city of Malmö (24%). The percentage of foreign-born persons in Västra Götaland, which encompasses Gothenburg, is the same as that of Sweden as a whole. The regions with low shares of foreign-born persons are in the north of the country – Dalarna, Gävleborg, Västernorrland, Jämtland, Västerbotten, and Norrbotten – plus the island of Gotland, which has the lowest share (9%). There are no municipalities in which foreign-born…

This map shows the total fertility rate (TFR) in 2018. The total fertility rate is the number of children a hypothetical cohort of women would have in one year. Data on total fertility rates is not available at the regional or municipal levels for all of the Nordic countries. The figures are estimated based on multiplying the general fertility rate by 30 (representing the typical number of reproductive years, between 15 and 45), assuming the general fertility rate is constant throughout this period. The general fertility rate is the number of births per woman during the childbearing years. At the regional level, age and gender composition are indicative of past trends but also harbingers of future population change. In 2018, prior to the pandemic, most Nordic municipalities had fertility levels in line with their respective national levels. Most municipalities within Greenland and the Faroes had fertility rates above 1.5 children per woman, consistent with national rates of about 1.9. Municipalities in Sweden and Denmark typically had fertility rates of 1.5 or higher, consistent with their national rates of 1.7. By contrast, many municipalities in Norway and Finland had fertility rates of 1.5 or lower. Iceland and Åland also showed varying rates across municipalities, ranging from 1 to over 1.5.

This map shows the natural population change per 1,000 persons in 2022. This map shows the natural population change per 1,000 persons in 2022 (i.e. between 1 Jan and 31 Dec 2022). Natural population change refers to births minus deaths (i.e. population change disregarding migration). The small map shows the result on a regional level, and the big map shows changes on the municipal level. Red shades refer to population decrease, blue shades to population increase, and yellow shades to balanced development. The map shows that levels of natural population change do not only vary across but also within the Nordic countries. Urban areas such as Stockholm and Gothenburg in Sweden; Oslo and Bergen in Norway; Copenhagen and Aarhus in Denmark; as well as Helsinki and Turku in Finland all experienced positive natural population change. This can be attributed to the comparatively young population age structure of these urban centres. Young people of child-bearing age often cluster in cities for study and work, and many start families there. By contrast, rural and remote areas often have a higher proportion of older people and, as such, tend to register more deaths than births, resulting in negative natural population change. These patterns are particularly pronounced in Finland but also in the northern parts of Sweden and Norway. Nonetheless, there are exceptions. In Iceland, Greenland, and the Faroe Islands, which had comparatively high levels of natural population growth at national level, a majority of municipalities, including many in rural areas, still registered more births than deaths in 2022 (77% of municipalities in Iceland, 60% in Greenland, 67% on the Faroe Islands). In the other Nordic countries, only a minority of municipalities, mostly in urban centres, recorded natural population increase in 2022 (30% in Norway and Sweden, 26% in Denmark, 12% in Finland).

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 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.

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%).

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.

The map shows the change in new registered passenger cars from 2019 to 2020. In most countries, the number of car registrations fell in 2020 compared to 2019. On a global scale, it is estimated that sales of motor vehicles fell by 14%. In the EU, passenger car registrations during the first three quarters of 2020 dropped by 28.8%. The recovery of consumption during Q4 2020 brought the total contraction for the year down to 23.7%, or 3 million fewer cars sold than in 2019. In the Nordic countries, consumer behaviour was consistent overall with the EU and the rest of the world. However, Iceland, Sweden, Finland, Åland, and Denmark recorded falls of 22%–11% – a far more severe decline than Norway, where the market only fell by 2.0%. The Faroe Islands was the only Nordic country to record more car registrations, up 15.8% in 2020 compared to 2019.  In Finland, Iceland, Norway, and Sweden, there were differences in car registrations in different parts of the country. In Sweden and Finland, the position was more or less the same in the whole of the country, with only a few municipalities sticking out. In Finland and Sweden, net increases in car registrations were concentrated in rural areas, while in major urban areas, such as Uusimaa-Nyland in Finland and Västra Götaland and Stockholm in Sweden, car sales fell between 10%–22%. Net increases in Norway were recorded in many municipalities throughout the whole country in 2020 compared to 2019.

The map shows changes in number of people travelling to work in the Nordic municipalities with the biggest populations comparing November 2021 to a pre-pandemic baseline. The map compares the average number of people who travelled to work on a weekday in January and February 2020 with the number of people who travelled to work on 11 November 2021 in the ten largest cities of Denmark, Finland, Norway and Sweden. The date was selected as the reference date as it is considered to be a relatively typical Thursday. It also represents a point when few movement restrictions were in place in the Nordic countries. As can be seen from the map, all of the municipalities highlighted recorded a fall in work-related travel on 11 November compared to the pre-pandemic baseline. It was biggest in Stavanger (-36%), followed by Stockholm (-31%), Oulu (-30%), Bærum (adjacent to Oslo) (-29%), Frederiksberg (adjacent to Copenhagen) (-29%) and Helsinki (-29%). In general, the decrease was highest around the capital regions and larger cities, but there were exceptions, for example, Jyväskylä (-26%), Örebro (-25%), Jönköping (-21%), and Randers (-20%). Several large municipalities also stood out because their patterns did not change so much, for example, Helsingborg (-3%) and Västerås (-7%) in Sweden; Viborg (-3%) and Odense (-8%) in Denmark. 

The map illustrates the average share of employees who commuted to another Nordic country between between 2015 and 2018 in Nordic regions (NUTS 2). Between 2015 and 2018, an average of approximately 49,000 people held a job in a Nordic country in which they were not residents. This indicates that, on average, 0.5% of the Nordic working-age population commuted to a job in another Nordic country. This is below the EU27 average of 1%, with the highest numbers found in Slovakia (5.1%), Luxembourg (2.8%) and Estonia (2.6%). Some of these people cross borders daily. Others work in another country by means of remote working combined with occasional commuting across borders.  Within the Nordic Region, the largest cross-border commuter flows are in the southernmost parts of Sweden, regions in the middle of Sweden and in Åland, where more than 1% of the working population commutes to another Nordic country. However, there may be individual municipalities where cross-border commuting is substantially higher. For example, the employment rate in Årjäng Municipality, Sweden, increases by 15 percentage points when cross-border commuting is taken into account. These municipalities are not reflected on NUTS 2 level when averages are calculated. In terms of absolute numbers in 2015, the highest numbers of commuters were from Sweden: Sydsverige (16,543), Västsverige (7,899) and Norra Mellansverige (6,890). The highest number of commuters from a non-Swedish region were from Denmark’s Hovedstaden (2,583).   Due to legislative barriers regarding the exchange of statistical data on cross-border commuting between the Nordic countries, more recent data is not available. 

The map shows the percentage change for work mobility in Nordic regions and municipalities compared to a pre-pandemic baseline.  The maps compare Nordic mobility patterns in each quarter of 2020 and 2021 with a pre-pandemic baseline. Based on Google data, the panels illustrate the impact of national restrictions and how those restrictions hampered work mobility. As the restrictions were both national and regional in nature, some regions and municipalities were more affected than others.  The darker areas in the map show that work mobility decreased the most Q2 and Q3 2020 and in Q3 2021. The panels also show that mobility decreased later in Sweden than in the other Nordic countries. However, the decrease in Q3 in both 2020 and 2021 may partly be explained by the summer vacation months, when work mobility tends to decrease anyway. In Q4 2021, the overall situation seems to improve, although the pattern is mixed. In a few municipalities the situation is almost back to pre-pandemic baseline while in most municipalities, there is still less mobility in the labour market compared to the pre-pandemic situation.