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The publication of tourism statistics often does not keep up with the highly dynamic tourism demand trends, especially critical during crises. Alternative data sources such as digital traces and web searches represent an important source to potentially fill this gap, since they are generally timely, and available at detailed spatial scale. In this study we explore the potential of human mobility data from the Google Community Mobility Reports to nowcast the number of monthly nights spent at sub-national scale across 11 European countries in 2020, 2021, and the first half of 2022. Using a machine learning implementation, we found that this novel data source is able to predict the tourism demand with high accuracy, and we compare its potential in the tourism domain to web search and mobile phone data. This result paves the way for a more frequent and timely production of tourism statistics by researchers and statistical entities, and their usage to support tourism monitoring and management, although privacy and surveillance concerns still hinder an actual data innovation transition., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Minora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. However, information on the distribution of population is often based on place-of-residence statistics from official sources, thus ignoring the changing population densities resulting from human mobility. Existing assessments of spatio-temporal population are limited in their detail and geographical coverage, and the promising mobile-phone records are hindered by issues concerning availability and consistency. Here, we present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km 2 resolution. The results reproduce and systematically quantify known insights concerning the spatio-temporal population density structure of large European cities, whose daytime population we estimate to be, on average, 1.9 times higher than night time in city centers.

Critical infrastructures (CIs) are assets, systems, or parts thereof that are essential for the maintenance of socioeconomic functions, health, safety and well-being of people. The exposure of CIs to natural and man-made hazards poses a risk to the economy and society. The spatial distribution of CIs and their economic value are a prerequisite for quantifying risk and planning suitable protection and adaptation measures. However, the incompleteness and inconsistency of existing information on CIs hamper their integration into large-scale risk frameworks. We present here the 'HARmonized grids of Critical Infrastructures in EUrope' (HARCI-EU) dataset. It represents major CIs in the transport, energy, industry and social sectors at 1 km 2 expressed in sector-specific, economically-relevant units. The HARCI-EU grids were produced by integrating geospatial and statistical data from multiple sources. Correlation analysis performed against independent metrics corroborates the approach showing average Pearson coefficients ranging between 0.61 and 0.95 across the sectors. HARCI-EU provides a consistent mapping of CIs in key sectors that can serve as exposure information for large-scale risk assessments in Europe.

Understanding uncertainties in land cover projections is critical to investigating land-based climate mitigation policies, assessing the potential of climate adaptation strategies and quantifying the impacts of land cover change on the climate system. Here, we identify and quantify uncertainties in global and European land cover projections over a diverse range of model types and scenarios, extending the analysis beyond the agro-economic models included in previous comparisons. The results from 75 simulations over 18 models are analysed and show a large range in land cover area projections, with the highest variability occurring in future cropland areas. We demonstrate systematic differences in land cover areas associated with the characteristics of the modelling approach, which is at least as great as the differences attributed to the scenario variations. The results lead us to conclude that a higher degree of uncertainty exists in land use projections than currently included in climate or earth system projections. To account for land use uncertainty, it is recommended to use a diverse set of models and approaches when assessing the potential impacts of land cover change on future climate. Additionally, further work is needed to better understand the assumptions driving land use model results and reveal the causes of uncertainty in more depth, to help reduce model uncertainty and improve the projections of land cover., (© 2016 John Wiley & Sons Ltd.)

Green infrastructure (GI), a network of nature, semi-natural areas and green space, delivers essential ecosystem services which underpin human well-being and quality of life. Maintaining ecosystem services through the development of GI is therefore increasingly recognized by policies as a strategy to cope with potentially changing conditions in the future. This paper assessed how current trends of land-use change have an impact on the aggregated provision of eight ecosystem services at the regional scale of the European Union, measured by the Total Ecosystem Services Index (TESI8). Moreover, the paper reports how further implementation of GI across Europe can help maintain ecosystem services at baseline levels. Current demographic, economic and agricultural trends, which affect land use, were derived from the so called Reference Scenario. This scenario is established by the European Commission to assess the impact of energy and climate policy up to 2050. Under the Reference Scenario, economic growth, coupled with the total population, stimulates increasing urban and industrial expansion. TESI8 is expected to decrease across Europe between 0 and 5 % by 2020 and between 10 and 15 % by 2050 relative to the base year 2010. Based on regression analysis, we estimated that every additional percent increase of the proportion of artificial land needs to be compensated with an increase of 2.2 % of land that qualifies as green infrastructure in order to maintain ecosystem services at 2010 levels.

Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on 'land use intensity' measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models' results were compared with estimations generated using the 'null model' (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the 'null model', but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models' performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions.