Maps

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.

To secure better access to general practitioners for the rural population, the region of Västerbotten has developed the concept of virtual healthrooms (VHRs). These VHRs are unstaffed, which means that they have no regular health personnel in situ. They are equipped with distance-spanningtechnology, which means that patients can go there to take consultations from a practitioner online, conducting health checks such as measuringblood pressure or heart rate. The coloured patches on the map show those populated areas in Västerbotten where inhabitants can expect a reduction of travel distance to primary health care through the implementation of VHRs. The coloured patches are populated areas in Västerbotten (by 1000*1000m grid) with improved accessibility of health care resulting from the implementation of virtual health rooms. The colour indicates the total distance reduced.Distance is measured as being via the road network. The average distance to the closest primary health care facility (health centre or virtual health room) is 6 km for the overall population in Västerbotten. The implementation of VHRs means that around 3.5% of the 270,000 inhabitants of Västerbotten experience increased accessibility toa primary health care service. The travel distance for this portion of the population has been cut by almost 50%, from 42 km per person to 23 km per person. Patients may also use virtual health rooms to conduct teleconsultations with health professionals at specialised hospitals, which creates even greater potential from an accessibility standpoint.

The map illustrates the accessibility of highly specialised care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The only hospital offering highly specialised in-patient care is located in Umeå, and half of the regional population (51.0%) can reach the hospital within a 40-minute car ride. For the inhabitants of Storuman, however it takes more than a two-hours car ride to access specialised care in the hospital. The accessibility of health care services for rural dwellers are improved by means of distance-spanning digital solutions. Instead of making the effort of physically visiting a health care facility, they can access video consultations with general practitioners via virtual health care rooms, and then decide whether a follow-up physical visit to the specialised hospital is needed.

The map illustrates the accessibility of in-patient care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The accessibility of in-patient care shares similar characteristics to that of access to dropin out-patient care. The ten facilities mentioned above provide in-patient care, too. The only difference is that two facilities outside the region were included in the analysis. But they did not alter the overall picture, suggesting that inhabitants living along the border line tend not to cross the border to seek in-patient care. These hospitals are accessible to 56.3% of the population within a 10-minute car ride, and 97.1% of the population within a one-hour car drive.

The map illustrates the accessibility of out-patient drop-in care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. Health care facilities offering out-patient drop-in care are characterised by even geographical distribution across Västerbotten. Each inland municipality has one such heath care facility, apart from Norsjö. All ten facilities offer drop-in care during both office hours and evenings and weekends (24/7). According to the results of our accessibility analysis, 56.% of the regional population can access drop-in out-patient care within a ten minute car ride. Within a half-hour car ride, the drop-in care service is available to 83.3% of inhabitants.

The map illustrates the accessibility of primary care in Västerbotten in Sweden. The colours represent car ride times in minutes from the place of residency to the nearest health care facility within a certain service type, with a travel range of 10 minutes to two hours. The health care facilities are also located on the map. The 37 health care facilities in the region contribute to widespread coverage of primary outpatient care for the population in Västerbotten. Over 80% of the inhabitants can access such health care services within a 10-minute car ride, and a half-hour car ride can cover 95.8% of the regional population. In general, coastal municipalities have easier access to health care services than inland municipalities. As one of the inland municipalities, Storuman accommodates one health care facility which provides most of the health care services expected in relation to highlyspecialised care.

This map gives an overview of the S3 focus areas in the Swedish regions in 2019. The major S3 domains in Sweden shown in the map provide a good overview of the key specialisation areas in Sweden. It is possible, for example, to check which Swedish regions have “green”, “sustainable”, “environment” at their smart specialisation domains (marked in green in their respective infoboxes for the domains in the figure). The information illustrated in the map can assist Swedish regions when they are considering opportunities for S3 synergy and co-operation with each other. In Sweden, the Swedish Agency for Economic and Regional Growth (Tillväxtverket) is a central actor in assisting regions in their work with smart specialisation. Tillväxtverket promotes opportunities for cooperation between the Swedish regional S3 processes and provides relevant information and learning seminars related to S3. Read the digital publication here.

The map shows universities and other educational institutions on post-secondary and tertiary level located in the Arctic. The red circles indicate a location of a university, college, or campus areas within the Arctic. The size of the circle corresponds to the number of educational institutions in a specific location. There is a high density of educational education institutions around Anchorage (Alaska), in Iceland, the Faroe Islands and the Arctic Fennoscandia (see zoom-in maps). In the Yukon (Canada), the Yukon College is the main educational institution, which has several campus areas across the region. In the Russian Arctic the largest centres with higher educational institutions are in Murmansk, Naryan-Mar (Nenets), Nizhnevartovsk (Khanty-Mansi), Salekhard (Yamalo-Nenets), and Yakutsk (Sakha).

The map shows the annual natural population change rates in the Arctic subregions between 2013 and 2017. The blue tones indicate a positive change: subregions where the number of live births exceeds the number of deaths. The yellow colour indicates no or little change: subregions where the difference between births and deaths are close to zero. The red tones indicate negative change: subregions where the number of deaths exceed the number of live births. In the Artic the annual average natural population change rate was 0,66% between 2013 and 2017. The natural population change was positive especially in the Canadian Arctic, Alaska (USA), Greenland as well as in Yamalo-Nenets, Khanty-Mansi and in Sakha regions (Russian Federation). Natural population decline was the strongest in the Nordic Arctic, as well as in Murmansk, Magadan, and Kamchatka (Russian Federation).

The map shows the percent of individuals aged 25-64 with tertiary education as the highest attainment level in the Arctic regions in 2017. Tertiary education corresponds to International Standard Classification of Education (ISCED) 2011 levels 5-8, which represent bachelor or equivalent and all higher attainment levels. The dark green tones show regions where more than 30% of individuals attained tertiary education as highest level. The light green tones show regions where less than 15% of individuals attained tertiary education as highest level. Highest shares of working aged population with tertiary education were found in Troms (Norway, 43,8%) and Iceland (42,5%). All other Nordic Arctic regions as well as Yukon (Canada, 33,3%) and in some regions in Alaska (USA) had high shares of highly educated people. Northern Quebec (Canada) had the lowest share of working aged population that attained tertiary education (13,0%).

The map shows the percent of individuals aged 25-64 with upper secondary education as highest level attained in 2017 in Arctic regions. The upper secondary education corresponds to International Standard Classification of Education (ISCED) 2011 levels 3-4. The dark blue tones show regions where more than 80% of individuals attained upper secondary education as highest level. The lightest blue tones show regions where less than 60% of individuals attained upper secondary education as highest level. Among the working age population, the number of individuals with upper secondary education was the highest in Alaska (USA, over 90%). Individuals with upper secondary education attainment level was also high – above 80% – in the Yukon and Labrador (Canada), Norrbotten (Sweden), and in Lappi (Finland). The lowest share of individual with upper secondary education was in Greenland (45,6%), Chukotka (Russian Federation, 58,4%) and in Nunavut (Canada, 59,1%).

The map shows the regional labour force (active population) as share of total population in the Arctic regions in 2016. The active population includes all persons (aged 15 years old and over) with at least one current paid job or searching for one. The dark green tones show regions with high participation rates and correspondingly light green tones show regions with low participation rates. The Russian Arctic regions of Chukotka (83,6%), Yamalo-Nenets (78,0%), and Magadan (76,1%) had the highest participation rates. The lowest participation rates were in Lappi (Finland, 53,3%) and in Nordland (Norway, 59,9%).

The map shows unemployed persons as share of the labour force (aged 15 years old and over) in the Arctic regions in 2016. The dark orange tones show regions with high unemployment rates and correspondingly light orange tones show regions with low unemployment rate. Nunavut and Newfoundland and Labrador (Canada, 14,0% and 13,4%, respectively) had the highest unemployment rates. The unemployment rate was the lowest in Finnmark, Nordland, and Troms (Norway, 2,6%, 3,3%, and 3,3%, respectively), in the Faroe Islands (3,3%), in Iceland (3,0%), as well as in three Russian Arctic regions: Magadan (3,1%), Yamalo-Nenets (3,1%), and Chukotka (3,2%). The unemployment rate in the Arctic regions mostly follows the national averages. 

The map shows the share of the part-time employees over total employment in the Arctic regions in 2014. Part-time workers are considered as persons (aged 15 years old and over) who are working less than 30 hours per week. The dark blue tones show regions with high part-time employment incidence and correspondingly light blue tones show regions with low part-time employment incidence. The three Arctic regions of Norway had the highest part-time employment incidence: Nordland (59,5%), Troms (56,3%), and Finnmark (56,0%). The Russian Arctic regions of Yamalo-Nenets (0,9%), Khanty-Mansi (2,5%), and Chukotka (3,4%) had the lowest part-time employment incidence.