As global warming is causing a significant reduction in the extent and the thickness of Arctic sea ice, a narrative for future large-scale commercial shipping in the Arctic has started to evolve. However, while most efforts have aimed to assess the financial benefits along with potential savings in greenhouse gas emissions, few studies have explored risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. This study aims at addressing this gap in the literature, by assessing the risk factors associated with Arctic shipping, in particular, risks along the Northern Sea Route (NSR) connecting European and Asian ports. Its main component includes a global least-cost analysis, aiming to assess global and Arctic risk factors associated with commercial shipping, to derive the least risk paths from 10 major ports in Europe to 10 in Asia. To project the results into the future, and thus assess the future potential of the NSR as a viable and less risky alternative, a dataset on predicted sea ice thickness within 2010–2099, restricted the analysis based on the penetration capabilities of three selected vessel types. Ultimately, the study concludes that maritime transportation, bound for East-Asia, commencing from most northern European ports, could benefit from the NSR, not just in terms of distance savings, cost reduction and less CO2 production, but also as a safer alternative. Lastly, two recommendations drawn from our results aim to assist decision-makers in facilitating future Arctic transportation by addressing accessibility and safety issues. Highlights

Population growth in urban centres and the intensification of segregation phenomena associated with international mobility require improved urban planning and decision-making. More effective planning in turn requires better analysis and geospatial modelling of residential locations, along with a deeper understanding of the factors that drive the spatial distribution of various migrant groups. This study examines the factors that influence the distribution of migrants at the local level and evaluates their importance using machine learning, specifically the variable importance measures produced by the random forest algorithm. It is conducted on high spatial resolution ($100\times 100$ grid cells) register data in Amsterdam and Copenhagen, using demographic, housing and neighbourhood attributes for 2018. The results distinguish the ethnic and demographic composition of a location as an important factor in the residential distribution of migrants in both cities. We also examine whether certain migrant groups pay higher prices in the most attractive areas, using spatial statistics and mapping for 2008 and 2018. We find evidence of segregation in both cities, with Western migrants having higher purchasing power than non-Western migrants in both years. The method sheds light on the determinants of migrant distribution in destination cities and advances our understanding of the application of geospatial artificial intelligence to urban dynamics and population movements.

Accurate and consistent estimations on the present and future population distribution, at fine spatial resolution, are fundamental to support a variety of activities. However, the sampling regime, sample size, and methods used to collect census data are heterogeneous across temporal periods and/or geographic regions. Moreover, the data is usually only made available in aggregated form, to ensure privacy. In an attempt to address these issues, several previous initiatives have addressed the use of spatial disaggregation methods to produce high-resolution gridded datasets describing the human population distribution, although these projects have usually not addressed specific population subgroups. This paper describes a spatial disaggregation method based on self-training regression models, innovating over previous studies in the simultaneous prediction of disaggregated counts for multiple inter-related variables, by leveraging multi-output models based on gradient tree boosting. We report on experiments for two case studies, using high-resolution data (i.e., counts for different subgroups available at a resolution of 100 meters) for the municipality of Amsterdam and the region of Greater Copenhagen. Results show that the proposed approach can capture spatial heterogeneity and the dependency on local factors, outperforming alternatives (e.g., seminal disaggregation algorithms, or approaches leveraging individual regression models for each variable) in terms of averaged error metrics, and also upon visual inspection of spatial variation in the resulting maps.

Unused energy is released into the environment from a number of sources, such as energy-intensive industries, whereas it could be used in district heating (DH) systems to make a significant contribution to decarbonising the heating sector. Despite its essential role, surplus heat (SH) is currently under-utilised and large amounts are wasted. Furthermore, there is a lack of detailed data in the literature on the sources that could potentially be captured. This paper examines the historical distribution of DH systems and their relationship with SH in Denmark, a country with very advanced DH. It also considers the potential for medium and low-temperature sources to be included alongside high-temperature industrial SH, which has been the dominant resource to date. Specifically, it utilises visual and statistical interpretation of the geospatial distribution of the utilised amounts of SH at municipal level. The analysis shows through classified choropleth mapping the dynamic temporal and spatial changes in the field, where the potentials depend on the availability of sources and their corresponding activities, but also shows that many municipalities with high and increasing DH shares could utilise SH from various sources that are not currently used such as in the food and beverages and retail and trade sector. Further potential from manufacturing and industrial activities could also be included, especially in the municipalities of the capital region, while significant low-temperature SH across the country would require a boost from heat pumps or upgrading of DH systems.