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Russia is the largest natural gas supplier to the EU. The invasion of Ukraine was followed by a cut-off of gas supplies from Russia to many EU countries, and the EU is planning to ban or dramatically reduce its dependence from Russia. We provide a dataset of daily gas consumption in five sectors (household and public buildings heating, power, industry, and other sectors) with supply source shares in the EU27 & UK from 2016 to 2022. The dataset separates the contributions of Russian gas imports, and other supply sources, and accounts for storage to estimate consumption. The dataset was developed with a gas network flow simulation model based on mass flow balance by combining data from multiple datasets including daily ENTSO-G pipelines gas transport and storage, ENTSO-E daily power production from gas, and Eurostat monthly gas consumption statistics per sector. The annual consumption data was validated against BP Statistical Review of World Energy and Eurostat datasets. We secondly analysed the share of gas supplied by Russia in each country to quantify the 'gap' that would result from a cessation of all Russian exports to Europe. Thirdly, we collected multiple data sources to assess how national gaps could be alleviated by 1) reducing the demand for heating in a plausible way using the lower envelope of gas empirical consumption - temperature functions, 2) increasing power generation from sources other than gas, 3) transferring gas savings from countries with surplus to those with deficits, and 4) increasing imports from other countries like Norway, the US, and Australia from either pipelines or LNG imports, accounting for existing capacities. Our results indicate that it should be theoretically possible for the EU to make up collectively for a sudden shortfall of Russian gas if combining the four solutions together, provided a perfect collaboration between EU countries and with the UK to redistribute gas from countries with surplus to those with deficits. Further analyses are required to investigate the implications for the costs including social, economic, and institutional dimensions, political barriers, and negative impacts on climate policies with inevitable increases of CO2 emissions if the use of coal is ramped up in the power sector. [ABSTRACT FROM AUTHOR]

Road networks are important for modelling the urban geographic environment. It is necessary to determine the spatial relationships of road intersections when using maps to help researchers conduct virtual urban geographic experiments (because a road intersection might occur as a connected cross or as an unconnected bridge overpass). Based on the concept of using different map layers to organize the render order of each road segment, three methods (manual, semi-automatic and mask-based automatic) are available to help map designers arrange the rendering order. However, significant efforts are still needed, and rendering efficiency remains problematic with these methods. This paper considers the Discrete, Crossing, Overpass, Underpass, Conjunction, Up-overlap and Down-overlap spatial relationships of road intersections. An automatic method is proposed to represent these spatial relationships when drawing road networks on a map. The data-layer organization method (reflecting road grade and elevation-level information) and the symbol-layer decomposition method (reflecting road covering order in the vertical direction) are designed to determine the rendering order of each road element when rendering a map. In addition, an "auxiliary-drawing-action" (for drawing road segments belonging to different grades and elevations) is proposed to adjust the rendering sequences automatically. Two experiments are conducted to demonstrate the feasibility and efficiency of the method, and the results demonstrate that it can effectively handle spatial relationships of road networks in map representations. Using the proposed method, the difficulty of rendering complex road networks can be reduced. [ABSTRACT FROM AUTHOR]