Maps

The map shows the population change by component 2010-2019. The map is related to the same map showing regional and municipal patterns in population change by component in 2020. Regions are divided into six classes of population change. Those in shades of blue or green are where the population has increased, and those in shades of red or yellow are where the population has declined. At the regional level (see small inset map), all in Denmark, all in the Faroes, most in southern Norway, southern Sweden, all but one in Iceland, all of Greenland, and a few around the capital in Helsinki had population increases in 2010-2019. Most regions in the north of Norway, Sweden, and Finland had population declines in 2010-2019. Many other regions in southern and eastern Finland also had population declines in 2010-2019, mainly because the country had more deaths than births, a trend that pre-dated the pandemic. In 2020, there were many more regions in red where populations were declining due to both natural decrease and net out-migration. At the municipal level, a more varied pattern emerges, with municipalities having quite different trends than the regions of which they form part. Many regions in western Denmark are declining because of negative natural change and outmigration. Many smaller municipalities in Norway and Sweden saw population decline from both negative natural increase and out-migration despite their regions increasing their populations. Many smaller municipalities in Finland outside the three big cities of Helsinki, Turku, and Tampere also saw population decline from both components. A similar pattern took place at the municipal level in 2020 of there being many more regions in red than in the previous decade.

The map shows the annual average internal net migration in 2010-2019. The map is related to the same map showing net internal migration in 2020. The maps show several interesting patterns, suggesting that there may be an increasing trend towards urban-to-rural countermigration in all the five Nordic countries because of the pandemic. In other words, there are several rural municipalities – both in sparsely populated areas and areas close to major cities – that have experienced considerable increases in internal net migration. In Finland, for instance, there are several municipalities in Lapland that attracted return migrants to a considerable degree in 2020 (e.g., Kolari, Salla, and Savukoski). Swedish municipalities with increasing internal net migration include municipalities in both remote rural regions (e.g., Åre) and municipalities in the vicinity of major cities (e.g., Trosa, Upplands-Bro, Lekeberg, and Österåker). In Iceland, there are several remote municipalities that have experienced a rapid transformation from a strong outflow to an inflow of internal migration (e.g., Ásahreppur, Tálknafjarðarhreppurand, and Fljótsdalshreppur). In Denmark and Norway, there are also several rural municipalities with increasing internal net migration (e.g., Christiansø in Denmark), even if the patterns are somewhat more restrained compared to the other Nordic countries. Interestingly, several municipalities in capital regions are experiencing a steep decrease in internal migration (e.g., Helsinki, Espoo, Copenhagen and Stockholm). At regional level, such decreases are noted in the capital regions of Copenhagen, Reykjavík and Stockholm. At the same time, the rural regions of Jämtland, Kalmar, Sjælland, Nordjylland, Norðurland vestra, Norðurland eystra and Kainuu recorded increases in internal net migration. While some of the evolving patterns of counterurbanisation were noted before 2020 for the 30–40 age group, these trends seem to have been strengthened by the pandemic. In addition to return migration, there may be a larger share of young adults who…

The map shows the annual average international net migration from 2010 to 2019. The map is related to the same map showing net migration in 2020. At regional level, there are only minor changes between the net migration in 2010-2019 and 2020. All regions of Norway, all regions of Sweden except Gotland and Uppsala, and the regions of Österbotten in Finland, Midtjylland in Denmark and Norðurland eystra in Iceland experienced a slight decrease in international net migration I 2020 compared to 2010-2019. There is a more marked increase in net migration in the Faroe Islands, Greenland and the region of Norðurland vestra in Iceland, and a slight increase in the region of Austurland in Iceland. At municipal level, the maps show more changing patterns. In Denmark, Norway and Sweden, several municipalities – both in the capital, intermediate, and rural regions – had lower levels of international net migration in 2020 compared to 2010-2019. In Iceland and Finland, the picture is more balanced, with some municipalities showing a decrease, others an increase. In the Faroe Islands and Greenland, several municipalities/regions had an increase in international net migration.

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 annual average immigration flows above 3,000 people, and the growing diversity in their countries of origin Sweden and Denmark, in particular, experienced large inflows from non-Nordic countries during the period 2010-2019, with Sweden standing out as the Nordic country with by far the largest immigrant in-flows. A large portion of these arrivals were from war-torn Syria (an annual average of almost 15,000), followed by Poland (approximately 4,500), United Kingdom, Iraq, India and Iran (around 4,000 each). Denmark experienced a smaller number of inflows above 3,000 people, compared to Sweden. The largest non-Nordic inflows to Denmark were around 5,000 people (per sending country) and included migrants from the U.S., Germany, Romania and Poland. For Norway, large non-Nordic in-flows were limited to Lithuania and Poland. Similarly, Finland had only one major inflow, from Estonia.

This map shows change in share of biofuels in final energy consumption in transport in the Nordic Arctic and Baltic Sea Region from 2010 to 2018. Even though a target for greater use of biofuels has been EU policy since the Renewable Energy and Fuel Quality Directives of 2009, development has been slow. The darker shades of blue on the map represent higher increase, and the lighter shades of blue reflect lower increase. The lilac color represent decrease. The Baltic Sea represents a divide in the region, with countries to the north and west experiencing growth in the use of biofuels for transport in recent years. Sweden stands out (16 per cent growth), while the other Nordic countries has experienced more modest increase. In the southern and eastern parts of the region, the use of biofuels for transport has largely stagnated. Total biofuel consumption for transport has risen more than the figure indicates due to an increase in transport use over the period.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.

This map indicates the geographical areas involved in case studies and more in-depth research but does not cover municipalities and other areas involved in e.g. questionnaires or more general studies.

The map shows the share of employed people in tourism industry in 2017. On a national level the share of tourism is quite similar in the Nordic countries, except for Iceland where the share is more than double as high. In Iceland it is especially the sector “Accommodation and food service activities” that stands out. This category alone stood for 6.9% of the total employment in Iceland in 2017 . On a regional level Åland stands out with 14.7% of employment in tourism. In Åland it is mainly the category “Sea passenger transport” that is big. Only this category stands for 9.8% of the employment. Also other islands such as Gotland and Bornholm have a high share of employment in tourism as well as the capital cities of Copenhagen and Stockholm. Jämtland attracts many tourists in the winter. The regions with the lowest share of tourism employment include the Finnish regions Keski-Pohjanmaa and Etelä-Pohjanmaa (both 2,7%); Københavns omegn (2.9%) and Vestjylland (3.4%) in Denmark; Blekinge (3.3%) and Kronoberg (3.3%) in Sweden and Østfold (3.5%) in Norway. The data on employment by sector is classified using the NACE classification system (“nomenclature statistique des activités économiques dans la Communauté européenne”). To define which sectors that tourism comprise of, we have selected the Eurostat’s definition due to the fact that Eurostat has adapted the definition of UNWTO to a European context to make it more precise and to avoid overestimate certain economic activities (e.g. real estate activities). Their definition is also very close to the definition used by Tillväxtverket. Eurostat thus defines tourism as comprising the following economic activities : H4910 Passenger rail transport, interurban H4932 Taxi operation H4939 Other passenger land transport n.e.c. H5010 Sea and coastal passenger water transport H5030 Inland passenger water transport H5110 Passenger air transport I5510 Hotels and…

The travel times indicate the fastest morning connection outbound from Copenhagen Central Station or Malmö Central Station, departing after 6:30AMand arriving before 9:00AM. The station catchments are calculated by bicycle travel time for any time remaining beyond train travel. For instance, a 35-minute train ride and a 10-minute cycle ride results in a 45-minute total travel time. The shades of green indicate the travel time to other train stations and their surrounding areas in four main classes: up to 15 minutes, 16 to 30 minutes, 31 to 45 minutes and 46 to 60 minutes. The areas not highlighted in green on the map are further than one hour by train from either Copenhagen or Malmö main train stations. The map clearly shows that the vast majority of areas within the Capital Region of Denmark, a number of stations and areas which are part of the region of Zealand, for instance Slagelse and Næstved, as well as areas located along four main train corridors in Skåne (Malmö-Helsingborg, Malmö-Hässleholm, Malmö-Trelleborg and Malmö-Ystad) are within the one-hour travel time by train from/to Copenhagen and/or Malmö, thanks to the different train types (Öresund trains, regional trains and intercity trains). Areas of the GCR which are beyond the one-hour travel condition are the most northern part of the Capital Region of Denmark, the southern and western parts of Zealand (e.g. Kalundborg and Vordingborg) as well as most of the eastern half part of Skåne. In terms of population, the current situation provides this possibility to almost 3 million out of 4.3 million inhabitants, corresponding to 69% of the total population living in the Greater Copenhagen Region in 2020. The proportion of the total population increases to 75% when the region of Halland is excluded (as this was not initially part of the GCR when the…

This map shows the Greater Copenhagen Region, which constists of 85 municipalities and four regional authorities: the Capital Region of Denmark and Region Zealand on the Danish side, and the regions of Skåne and Halland on the Swedish side.

This map shows both the location of the four case studies of the Triborder activity 3 in the Greater Copenhagen Region and the local labour market areas in this cross-border region.

The map shows the “at-risk-of-poverty” (AROP) rate in the Nordic Region. For the period from 2004 to 2018, the AROP rate increased in all Nordic countries except Iceland. This trend was strongest in Sweden. In Finland the AROP rate has been decreasing during the past few years, in line with what has previously been indicated – namely, on account of economic turmoil. This points to one of the weaknesses of using the AROP rate alongside several other measures of inequality. That is, while people have become poorer due to the economic crisis, the at-risk-of-poverty rate has paradoxically gone down. In addition, the AROP rate for Finland is higher in 2018 than it was in 2004. Looking at these trends on a regional level over a period of time (between 2011 and 2018), we can see that the AROP rate has decreased in almost all areas of Finland, whereas the pattern is rath er more varied in the other Nordic countries (we can also see a cohesive area in the south of Denmark where the AROP rate has decreased.) Again, Sweden has the most regions displaying increases in the AROP rate. Finland and Sweden contain the largest differences between the regions with the highest and lowest AROP rate. Hence the greatest regional differences are to be found in Sweden and Finland. Sweden also has the highest average AROP rate. About the At-risk-of-poverty The at-risk-of-poverty rate is a common measure of relative poverty and social inclusion. Most notably, it has been used for monitoring the EU2020 goal of inclusive growth. The at-risk-of-poverty rate is normally defined as “the share of people with an equivalised disposable income (after social transfer) below the at-risk-of-poverty threshold, which is set at 60% of the national median equivalised disposable income after social transfer.” (Eurostat). The indicator is…

The map shows a typology, combining two indicators to display income disparities between and within municipalities. The map combines measurements of household disposable income (HDI) and the Gini Index to create four “types” of income distribution. Household disposable income is a common measure of income inequality. It measures the capacity of households (or individuals) to provide themselves with consumable goods or services. Comparing average HDIs is a convenient way of understanding inequality between municipalities. The Gini Index measures the extent to which the distribution of household income deviates from an equal distribution level. The Gini Index is therefore useful in understanding the inequality that exists within municipalities. Combining these measurements provides a comprehensive geographic overview of income in equality across the Nordic Region, both within and between municipalities. The municipalities shaded in yellow on the map have an average HDI above the Nordic average, as well as a Gini coefficient above the Nordic average (i.e. high income, but unevenly distributed). This category includes most of the wealthiest municipalities, including municipalities in the capital regions – e.g. most municipalities in the Stockholm Region (Lidingö, Danderyd, Ekerö, Täby, Sollentuna), Copenhagen (Gentofte, Hørsholm, etc.), and Helsinki (Kauniainen). Several municipalities in southern Sweden and Denmark also fall into this category. Most of these have average HDIs just above the Nordic average. The second category (blue on the map) consists of municipalities with HDI above the Nordic average and a Gini coefficient below the Nordic average (i.e. high income and even distribution). Most municipalities in this category are in Norway. Norway has a higher HDI and more even distribution than the other Nordic countries. The third category (green on the map) consists of municipalities with both an HDI and a Gini coefficient below the Nordic average (i.e. lower income, but more evenly distributed). This category…

This map shows a typology that divides the Nordic municipalities and regions into four migration categories: positive net migration for both males and females (green on the map), positive male net migration (blue on the map), positive female net migration (yellow on the map), and negative net migration for both males and females (red on the map). These migration flows on 30 to 39-year-olds are of particular interest since it is often assumed that the future of rural regions is dependent upon their capability both to retain their populations and to attract newcomers, returning residents and second home owners. In this context, the map provides a rather positive picture, because a considerable proportion of rural municipalities have experienced positive net migration among females, males, or both sexes across all the Nordic countries. Even so, there is negative net migration among both females and males in many municipalities in northern Sweden, north-eastern Norway and eastern Finland, in addition to several inland municipalities within these countries. Interestingly, there is negative net migration among both sexes across all the capital city municipalities of the Nordic Region. According to the regional map, the capital city regions of Denmark, Iceland and Norway all experienced negative net migration of young people aged 30-39 years between 2010 and 2019. The capital city region of Sweden experienced positive net migration of males and negative net migration of females while the capital city region of Finland experienced positive net migration overall. Despite the majority of peripheral regions experiencing negative net migration of 30 to 39-year-olds during the time period studied, there are also several interesting examples of rural regions which experienced positive female net migration, for example Nordjylland (Denmark), Pohjois-Savo (Finland), Austurland (Iceland), Møre og Romsdal (Norway), and Jämtland (Sweden).

This map shows a typology that divides the Nordic municipalities and regions into four migration categories: positive net migration for both males and females (green on the map), positive male net migration (blue on the map), positive female net migration (yellow on the map), and negative net migration for both males and females (red on the map). These migration flows of 20 to 29-year-olds are of interest since there is a particularly high level of internal migration among young adults across the Nordic countries compared to other EU countries. While the map shows that the great majority of municipalities experience negative net migration of young adults in favour of a few functional urban areas and some larger towns, it is possible to observe a number of exceptions to this general rule. The rural municipalities of Utsira, Moskenes, Valle, Smøla, Ballangen and Lierne in Norway have the highest positive net migration rates both for men and women. There are also positive net migration rates for males and females in the peripheral municipalities of Jomala, Kittilä, Lemland and Finström in Finland and Åland. There is positive male net migration but negative female net migration in Gratangen, Loppa, Gamvik, Drangedal and a few other Norwegian rural municipalities, plus Mariehamn in Åland, while several municipalities in remote areas of Finland have positive female net migration but negative male net migration. Some of these patterns may be related to specialised local labour markets, such as fisheries in Loppa, or recreational tourism in Kittilä. In general, the pattern of net migration among young adults is more diverse in Finland (where 72.0% of all municipalities have negative net migration), compared with 84.6% in Norway, 88.9% in Denmark and 89.0% in Sweden. However, it is important to remember that Danish, Finnish and Norwegian municipalities are smaller in size…

This map shows the average distance to the edge of the closest urban area for the population living outside urban areas in the Nordic municipalities and regions. These figures are of particular interest in the analysis of urban-rural patterns and the Nordic populations’ access to local services in sparsely population areas. The darker shades on the map indicate longer average distances to the edge of the nearest urban area, and the lighter shades indicate shorter average distances. While almost all Danish municipalities have an average distance of below 10 km from rural grid cells to the nearest urban area, a large share of the municipal populations of the remaining Nordic countries need to contend with longer average distances to local services. The largest distances can be found in several municipalities of Iceland and Norway (Árneshreppur 230 km, Hasvik 154 km), whereas the largest average distances for Finnish and Swedish municipalities are considerably shorter (Enontekiö 103 km, Storuman 52 km). Regarding within-country variation, shorter average distances can generally be found in southwestern Finland and southern Sweden, in comparison with the more remote parts of these countries. Both Norway and Iceland provide a rather more mixed picture, since there are municipalities with shorter average distances scattered across different parts of each country. Method used to calculate the degree of rurality In order to take into account access to services such as grocery stores, pharmacies, schools, community centres and public transport, the European definition of urban grid cells was used to create this map, i.e. a population density threshold of 300 inhabitants per km2 applied to grid cells of 1 km2. The closest distance was calculated from each rural grid cell centroid to the nearest urban grid cell centroid along the existing road network traversable by car, including car ferries, based on population…

This map shows the expected population change in Nordic municipalities by looking at the change between 2017 and 2040. The colours on the main map indicates the percentage of population change at municipal level between 2017 and 2040. Shades of red indicate a population decline, whereas shades of blue indicate a population increase. The colours on the regional map (bottom-right corner) indicates the type of region according to the Eurostat’s urban-rural typology (see: http://nordregio.org/maps/urban-rural-typology-of-nordic-regions/): shades of red indicate predominantly urban regions, yellow for intermediate regions and shades of green for predominantly rural regions. In the two types of regions classified as predominantly rural (“light green” and „dark green“ regions), population growth largely remains concentrated in larger towns and their suburbs. This is particularly the case in Sweden and Finland. For instance, the regional capitals Östersund in Jämtland (SE) and Seinäjoki in South Ostrobothnia (FI) are expected to have larger populations in 2040 than today, while most other municipalities in these regions will decline. In Norway and Iceland, a somewhat more nuanced pattern applies. Here population growth is not only expected in regional capitals and other larger towns, but also in some smaller and more remote municipalities. In Denmark, finally, population growth is expected for large parts of the country until 2040, in more rural and urban areas alike. Population loss is only expected in some of the most remote municipalities at the Northern, Eastern and Southern borders of the country. While the population loss in rural regions is hence expected to continue across the Nordic Region, it appears to be more pronounced in some countries than in others. In the predominantly urban (“red”) regions, the opposite pattern applies: With very few exceptions, municipalities in these regions are expected to experience a growth in population numbers until 2040.