Maps

This map shows the country of birth of the largest minority group in the Nordic countries. This map shows the country of birth of the largest minority group in each municipality in each Nordic municipality in 2022. The different colours represent the largest minority group, defined as a specific country or a group of countries. Using country of birth as an identity marker reveals an interesting geographic pattern of minority populations at the municipal level. Even with the increase in migration, native-born populations remain the largest group in each municipality (i.e. Swedish-born people are the largest group in every municipality in Sweden). They also constitute the majority in each municipality, with the exception of one small municipality with a majority-minority population. For visual simplicity, countries of birth are grouped. The four large Nordic countries are shown separately. The EU15 countries and EU accession countries constitute separate groups. The countries of the former Soviet Union (minus the Baltic states) are another group. Afghanistan, Eritrea, Iran, Iraq, Somalia and Syria together constitute a group of countries from which many of the migrants to the Nordic countries came as refugees over the past decade. People born in Finland are the largest minority group in many regions near the Finnish border in northern Sweden, as well as a group of municipalities around Stockholm. In several border municipalities in Sweden, people born in Norway are the largest minority group. Swedish-born persons are the largest minority group in most municipalities in northern and western Finland (by percentage). People born in one of the EU15 countries form the largest minority group in southern Denmark, near the border with Germany, as well as a few other scattered municipalities in southern Sweden and Norway. Following several EU expansions starting in 2004, many people from the EU accession countries have…

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 percentage-point change in foreign-born populations in the Nordic countries. This map shows the percentage-point change in foreign-born populations by region (small map) and municipality (big map) between 2000 and 2022. The blue shades indicate an increase in the number of foreign-born while the red shades indicate a decrease. The recent growth in foreign-born populations differs among the Nordic countries, regions, and municipalities. During the period 2000-2022, much of both the absolute and percentage-point increases in the foreign-born populations took place in suburbs around the capital cities and other large urban centres. However, with few exceptions, every municipality across the Nordic Region saw increases in foreign-born populations. In Iceland, the foreign-born population increased from 5% to 20%. The largest percentage-point increases in the foreign-born populations were in municipalities in the southwest, which had small populations and small foreign-born shares. Of the larger municipalities, Reykjanesbaer and the Capital Region had the largest absolute and percentage point increases. The foreign-born population in Norway grew from 7% to 17%. The municipalities with significant increases in foreign-born populations include several suburban areas near Oslo, as well as scattered municipalities elsewhere that had small foreign-born shares in 2000. The percentage of foreign-born residents in Oslo increased from 16% to 28% between 2000 and 2022. No municipalities experienced a decline in foreign-born population during this period.

This map shows the internal net migration in 2021 as a percentage of the total population. This map shows the internal net migration in 2021 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 2021, internal net migration was positive (indicated by shades of blue) or at least balanced (shown in yellow) in many of the municipalities in central and northern Sweden and in central and eastern 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. Iceland, Greenland, and Åland exhibited mixed internal migration flows, with some municipalities experiencing positive net migration while others faced negative net migration. The Faroe Islands stand as an exception, maintaining only positive internal migration at the municipal level for this year.

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 presents a typology of internal net migration in regions by considering average annual internal net migration in 2020-2021 alongside the same figure for 2018-2019. The colours on the map correspond to six possible migration trajectories: Dark blue: Internal net in migration as an acceleration of an existing trend (net in-migration in 2020-2021 + increase compared to 2018-2019) Light blue: Internal net in migration but at a slower rate than previously (net in-migration in 2020-2021 + decrease compared to 2018-2019) Green: Internal net in migration as a new trend (net in-migration in 2020-2021 + change from net out-migration compared to 2018-2019) Yellow: Internal net out migration as a new trend (net out-migration in 2020-2021 + change from net in-migration compared to 2018-2019) Orange: Internal net out migration but at a slower rate than previously (net out-migration in 2020-2021 + decrease compared to 2018-2019) Red: Internal net out migration as a continuation of an existing trend (net out-migration in 2020-2021 + increase compared to 2018-2019)

The map presents a typology of internal net migration by considering average annual internal net migration in 2020-2021 alongside the same figure for 2018-2019. The colours on the map correspond to six possible migration trajectories: Dark blue: Internal net in migration as an acceleration of an existing trend (net in-migration in 2020-2021 + increase compared to 2018-2019) Light blue: Internal net in migration but at a slower rate than previously (net in-migration in 2020-2021 + decrease compared to 2018-2019) Green: Internal net in migration as a new trend (net in-migration in 2020-2021 + change from net out-migration compared to 2018-2019) Yellow: Internal net out migration as a new trend (net out-migration in 2020-2021 + change from net in-migration compared to 2018-2019) Orange: Internal net out migration but at a slower rate than previously (net out-migration in 2020-2021 + decrease compared to 2018-2019) Red: Internal net out migration as a continuation of an existing trend (net out-migration in 2020-2021 + increase compared to 2018-2019) The patterns shown around the larger cities reinforces the message of increased suburbanisation as well as growth in smaller cities in proximity to large ones. In addition, the map shows that this is in many cases an accelerated (dark blue circles), or even new development (green circles). Interestingly, although accelerated by the pandemic, internal out migration from the capitals and other large cities was an existing trend. Helsinki stands out as an exception in this regard, having gone from positive to negative internal net migration (yellow circles). Similarly, slower rates of in migration are evident in the two next largest Finnish cities, Tampere and Turku (light blue circles). Akureyri (Iceland) provides an interesting example of an intermediate city which began to attract residents during the pandemic despite experiencing internal outmigration prior. From a rural perspective there are…

The map shows the average internal net migration in 2020 and 2021 for Nordic municipalities. Blue dots indicate positive internal net migration (more people moving in than out) and red dots indicate negative internal net migration (more people moving out than in), while the size of the dots represents the extent of the positive or negative trend. Internal migration refers to a change of address within the same country. The map shows substantial outmigration from the Nordic capitals, as well as from Gothenburg and Malmö in Sweden. Alongside increased suburbanisation, the map also provides some evidence of growth in medium-sized cities and smaller cities within commuting distance of larger cities.    

The map shows the type (positive or negative) and size of population change from 2010 to 2019 in all nordic municipalities. In Finland, Denmark, and Greenland there is a clear pattern of population growth in and around the larger cities and population decline in rural areas. Geographical and administrative differences mean that a much larger number of rural municipalities in Finland are dealing with population decline. Sweden experienced substantial population growth between 2010 and 2019, primarily due to high levels of international immigration. As a result, many rural areas also experienced population growth, particularly in the south of Sweden. However, in the more sparsely populated municipalities in the north of Sweden, the pattern is somewhat similar to that observed in Denmark and Finland, albeit with population decline in lower absolute numbers. Both Iceland and the Faroe Islands experienced substantial growth of their tourism industries within the period. This enabled some rural areas to maintain or even grow their populations. Norway exhibits more balanced population development in general, with a mix of population growth and decline in rural areas throughout the country.

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.