Maps

A map portrays a new urban-rural typology based on the grid-level data. New Nordic urban-rural typology is a grid-based classification of areas developed by the Nordic Thematic groups 2021-2024 to enable more accurate cross-Nordic statistical comparisons. The seven classes are defined based on population density, proximity measures and land cover parameters. Read more about the typology here . Inner urban area is the most densely populated part of the urban core. Urban cores are clustered cells summing up to at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterion (population density and building floor space). Outer urban area is the least densely populated part of the urban core. Urban core areas are clustered cells with at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterions (population density and building floorspace). Peri-urban area is the intermediate zone between urban core and the rural. It is based on generalized travel-time estimates from the edges of outer urban areas (6 min travel-time zones) and smaller urban settlement (4,5 min travel-time zones). Local centers in rural areas are population centers located outside urban areas, small towns and large parish villages where population is between 5000-14999 inhabitants. Rural areas close to urban areas have a rural character that are functionally connected and close to urban areas. In average this means 20-30 of minutes’ drive time from the edge of outer urban area. This class overwrites the area classes ‘Rural heartland’ and ‘Sparsely populated rural areas’.  Rural heartland. Rural areas with intensive land use, with a relatively dense population and a diverse economic structure at the local level. Most of the agricultural land is in this class. Sparsely populated rural areas. Sparsely populated areas with dispersed small settlements that are located at a distance from each other.…

A map portrays a new urban-rural typology based on the grid-level data. New Nordic urban-rural typology is a grid-based classification of areas developed by the Nordic Thematic groups 2021-2024 to enable more accurate cross-Nordic statistical comparisons. The seven classes are defined based on population density, proximity measures and land cover parameters. Read more about the typology here . Inner urban area is the most densely populated part of the urban core. Urban cores are clustered cells summing up to at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterion (population density and building floor space). Outer urban area is the least densely populated part of the urban core. Urban core areas are clustered cells with at least 15 000 inhabitants, and these are divided into Inner and Outer urban areas based on density criterions (population density and building floorspace). Peri-urban area is the intermediate zone between urban core and the rural. It is based on generalized travel-time estimates from the edges of outer urban areas (6 min travel-time zones) and smaller urban settlement (4,5 min travel-time zones). Local centers in rural areas are population centers located outside urban areas, small towns and large parish villages where population is between 5000-14999 inhabitants. Rural areas close to urban areas have a rural character that are functionally connected and close to urban areas. In average this means 20-30 of minutes’ drive time from the edge of outer urban area. This class overwrites the area classes ‘Rural heartland’ and ‘Sparsely populated rural areas’.  Rural heartland. Rural areas with intensive land use, with a relatively dense population and a diverse economic structure at the local level. Most of the agricultural land is in this class. Sparsely populated rural areas. Sparsely populated areas with dispersed small settlements that are located at a distance from each other.…

The map portrays the urban-rural typology based on the municipal level data.

The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers as of December 2021.

This map shows location of algae production by production method in the Nordic Arctic and Baltic Sea Region in 2019 Algae and seaweeds are gaining attention as useful inputs for industries as diverse as energy and human food production. Aquatic vegetation – both in the seas and in freshwater – can grow at several times the pace of terrestrial plants, and the high natural oil content of some algae makes them ideal for producing a variety of products, from cosmetic oils to biofuels. At the same time, algae farming has added value in potential synergies with farming on land, as algae farms utilise nutrient run-off and reduce eutrophication. In addition, aquatic vegetation is a highly versatile feedstock. Algae and seaweed thrive in challenging and varied conditions and can be transformed into products ranging from fuel, feeds, fertiliser, and chemicals, to third-generation sugar and biomass. These benefits are the basis for seaweed and algae emerging as one of the most important bioeconomy trends in the Nordic Arctic and Baltic Sea region. The production of algae for food and industrial uses has hence significant potential, particularly in terms of environmental impact, but it is still at an early stage. The production of algae (both micro- and macroalgae) can take numerous forms, as shown by this map. At least nine different production methods were identified in the region covered in this analysis. A total of 41 production sites were operating in Denmark, Estonia, the Faroe Islands, Iceland, Norway, Germany, and Sweden. Germany has by far the most sites for microalgae production, whereas Denmark and Norway have the most macroalgae sites.

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 shows the share of biofuels in final energy consumption in transport in the Nordic Arctic and Baltic Sea Region in 2018. There has been considerable political support for biofuels and in the EU, this debate has been driven by the aim of reducing dependency on imported fuels. For instance, 10 per cent of transport fuel should be produced from renewable sources. The darker shades on the map represent higher proportions, and the lighter shades reflect lower proportions. As presented by the map, only Sweden (20.7%) had reached the 10 per cent target in the Nordic Arctic and Baltic Region in 2018. Both Finland (8.3%) and Norway (8.3%) were close by the target, while the other countries in the region were still lagging behind, particularly the Baltic countries.

The map shows the share of cross border commuters in the total employees with residence in a NUTS2 Nordic Region in 2015. The darker the blue, the higher the share. For the most NUTS2 regions in the Nordic, the percent is lower than 0,5%, indicating the commuting workers are the absolute minority in the total employed people. Åland (2,6%) and the South Sweden region (2,7%) stand out with more than 2% of employees in the region commuting cross-border for work. The destination country for Åland workers is Sweden, while for Swedish workers living in the south is Denmark. The commuting pattern is also apparent for the Swedish NUTS2 regions along the border line with Norway, with relatively higher percent of cross border workers commuting to Norway compared with other Nordic NUTS2 regions. At a finer scale (e.g., NUTS3) would show higher percentages in a number of regions, e.g., by taking only the NUTS 3 region – Skåne instead of the NUTS 2 region South Sweden (Skåne+Blekinge) or the border regions between NO and SE.

The map builds on statistics of cross-border commuters with residence in a NUTS2 Nordic region commute for work in 2015. For each NUTS2 region, the map shows the total number of commuters who commute to other Nordic countries for work. The number of commuters is categorised into three groups visualised in different shades: the darker, the higher the number of commuters. In addition, the most common country these commuters commute to from each region is identified by specific colours. For example, the darkest red indicates a region with at least 2,000 commuters working in another Nordic country, of which the largest group number of commuters works in Denmark. The most commuters were from the region of South Sweden (16 543) in 2015, and the majority of them commuted to Denmark for work. Norway is the most popular destination for work commuters in the Nordic Region, e.g., all Swedish regions except for the South Sweden region, all the regions in Denmark except for the Copenhagen region, and Iceland. Sweden is more attractive for work commuters living in Finland, Copenhagen region, and bordering regions in Norway.

The map shows the geographical delimitation of cross-border regions and committees financed by the Nordic Council of Ministers.

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 proportion of households within each municipality that did not have access to a fixed-line broadband connection with a download speed above 30 Mbps in 2018. Put another way, the map shows the proportion of households which only had access to basic broadband. In Danish, Icelandic and Swedish municipalities, the proportion of households which only have basic broadband is rather small. In contrast, more than half of all households rely on basic broadband in many Norwegian and Finnish municipalities. The situation in Finland is particularly striking, with several municipalities in which over 75% of households have only basic Internet access. The average coverage by municipality type shows a clear digital divide between urban and rural municipalities. On average, fast broadband is available to all but 4% of households in urban municipalities. In contrast, approximately one third of households in rural municipalities do not have access to any faster broadband than 30 Mbps. The largest urban-rural digital divide is to be found in Norway and Finland. However, the pace of fibre development has never been higher. Particularly noteworthy is the strong growth in fibre-based broadband taking place outside of the densely populated areas.

The map shows the proportion of the population aged 30-34 years old, who had a tertiary education at the European level in 2019. Purple shades indicate higher proportions, and pinkish shades reflect lower proportions. It is common to show the education attainment for the age group 30-34 since it is an age group where most people have finalised their studies. The focus on this age group makes it easier to see recent trends and outcomes of policies. Overall, over 40% of Europeans aged 30-34 years old had a tertiary education in 2019. Young people in the Nordic countries are among the most educated, with approximately half of 30 to 34-year-olds achieving a tertiary education across all Nordic countries. The highest proportions can be found in the capital regions. Stockholm is particularly noteworthy, with over 60% of 30 to 34-year-olds having had a tertiary education. Regions with prominent universities also stand out – for example, Skåne, Uppsala, Västerbotten and Västra Götaland (Sweden), Trøndelag (Norway) and Østjylland (Denmark).

This map depicts the community impact (CI) of second home users in 2018[1] The indicator illustrates the impact of occasional second home visitors to the municipality. CI is defined as the ratio between annual inhabitants (AI) and regular population (CI= AI/Regular population). AI is a statistical variable that estimates the overall population of the municipalities, both the permanent inhabitants but also the seasonal second home visitors that also utilize the local infrastructure, welfare and planning resources.  AI is defined as three times the number of second homes plus the regular population. (AI= Regular population + 3 x number of second homes). Number three represents the estimate of average household size that visits the second home. Dark orange tones indicate high impact of seasonal inhabitants and light orange indicates lower impact of the seasonal variation. In populous municipalities with few recreational homes the two population measures (AI & regular population) will be almost identical, yielding a ratio approaching 1. The ratio between the regular population and the calculated annual population is large in municipalities with relatively few inhabitants and a high number of second homes. Nordic average for community impact by second home users was 1.2. Southern Savonia in Finland was the region with highest regional community impact. From other Nordic countries, the regions of Oppland in Norway, Suðurland in Iceland, and Jämtland Härjedalen in Sweden also stood out with higher ratios. On a municipal level the highest community impact can be found from Skorradalshreppur (30.0) in Iceland. Moreover, Grímsnes- og Grafningshreppur (19.0) (IS) and Kustavi (11.2) (FI) were the municipalities where the community impact also exceeded the ratio of 10. A large amount of second homes implies that there is a significant flow of people who are not permanently registered in these areas. This flow of people has both highly…

This map is included in the introduction of the State of the Nordic Region 2020 report. The map is based on the typology provided by Eurostat (2018), in which the 2016 NUTS 3 regions are classified in three categories according to their population density in 2011 and 2015, in 1 km² grids. In predominantly urban regions, at least 80% of the total population is urban, while in intermediate regions, 50–80% of the population lives in urban clusters. In regions that are predominantly rural, less than 50% of the population lives in urban areas. Read the digital publication here.

This map illustrates the changes of number of second homes in the Nordic countries between 2010-2017. The colors of the circles in the map indicate the change in the number of second homes between 2010 and 2017. Red tones indicate decrease and blue tones indicates growth in the number of second homes. In total, the number of second homes were growing with 4.2 % within the Nordic countries.  Vestfold in Norway was the regions were the growth was strongest and the actual number of second homes in 2017 was approx. 3000 higher than in 2010. In some areas the number of second homes are decreasing (indicated on map by the red colors). This map has been developed in the Nordregio project “Urban-rural flows of seasonal tourists – local planning challenges and strategies”. Within the frame of this project three plausible reasons for the decrease have been identified: 1) that the houses have been teared down or abandoned, 2) that the houses have been turned into permanent houses, and 3) that the statistics from the housing registers includes errors due to different perceptions on how to report (different opinion on what a house is).

This map depicts the total number of second homes per municipality (size of the circles). Dark blue indicates higher shares second homes in relation to municipal population and light blue indicates lower shares (purple the lowest). In total, there are 65 secondary homes per 1000 inhabitants in the Nordic Countries. The main areas for secondary homes – both in numbers and in relation to permanent inhabitants are mid-eastern lake areas (Etelä-Savo/Södra Savolax) and south-west archipelago in Finland including Åland, southern mountains area (Dalarna and Jämtland Härjedalen), Stockholm archipelago and Öland in Sweden, southern mountain area in Norway (Oppland and Buskerud), northern Sjælland in Denmark and municipalities in vicinity to Reykjavík in south of Iceland.

This map depicts the community impact (CI) of second home users in 2017[1]. The indicator illustrates the impact of occasional second home visitors to the municipality.CI is defined as the ratio between annual inhabitants (AI) and regular population (CI= AI/Regular population). AI is a statistical variable that estimates the overall population of the municipalities, both the permanent inhabitants but also the seasonal second home visitors that also utilize the local infrastructure, welfare and planning resources.  AI is defined as three times the number of second homes plus the regular population. (AI= Regular population + 3 x number of second homes). Number three represents the estimate of average household size that visits the second home. Dark orange tones indicate high impact of seasonal inhabitants and light orange indicates lower impact of the seasonal variation. In populous municipalities with few recreational homes the two population measures (AI & regular population) will be almost identical, yielding a ratio approaching 1. The ratio between the regular population and the calculated annual population is large in municipalities with relatively few inhabitants and a high number of second homes. Nordic average for community impact by second home users was 1.2. Southern Savonia in Finland was the region with highest regional community impact. From other Nordic countries, the regions of Suðurland in Iceland, Oppland in Norway and Jämtland Härjedalen in Sweden also stood out with higher ratios. On a municipal level the highest community impact can be found from Skorradalshreppur (28.9) in Iceland. Moreover, Grímsnes- og Grafningshreppur (19.2) (IS) and Kustavi (11.3) (FI) were the municipalities where the community impact also exceeded the ratio of 10. A large amount of second homes implies that there is a significant flow of people who are not permanently registered in these areas. This flow of people has both highly positive…

Co-operation Committees funded by the Nordic Council of Ministers in 2018 The Nordic countries have a long history of cross-border co-operation. In parts, this co-operation has been carried out through regional cross-border committees which cooperate across the state borders. The activities of some of these committees have been going on since the 1960’s. The aim of the committees are increasing growth and development of the actual border region, building on the concept that bigger, dynamic and well-integrated regions will be able to retain and attract companies and people for further growth and development. The cross-border regions are geographically delimited by their member institutions, which consist of government organisations on either regional or municipal level in neighbouring countries. These can be municipalities, counties, local authority associations or other organisations that deal with regional development. In some cases, the same regional body might be involved in several cross-border committees. Such areas are hatched in the map.