LAUSR

The interactions between geoenvironmental and anthropic processes are increasing due to the ever-growing population and its related side effects (e.g., urban sprawl, natural resource and energy consumption, etc.). Natural hazards, land degradation, environmental pollution and climate change are some of the most evident results of the “interactions” between geosphere and anthroposphere. At a finer spatial scale, geo-environmental and geo-engineering issues in urban contexts or in the proximity of infrastructures represent a wide set of challenges that directly impact on the critical zone (e.g., Giardino and Houser 2015). In this context, spatial and spatiotemporal data are crucial for the analysis, modelling and forecasting of the possible interactions between human activities and the geoenvironment. The technological developments achieved in field and laboratory instruments, including geophysical, proximal and remote-sensing devices, increase exponentially the amount and heterogeneity of geoenvironmental data that can be collected (e.g., Pereira et al. 2018). The ever-growing quantity of geoenvironmental data is distinguished by an extreme variability of information characteristics including its spatial sampling network geometry, its spatial support, its uncertainty, and its typology (e.g., hard/soft). The cited aspects have a strong influence on the spatial analysis methodologies that can be adopted (e.g., Kanevski and Maignan 2004; Daya et al. 2018). Moreover, the choice of a specific spatial data analysis methodology is also dependent on the objectives of the study and on users’ specific knowledge. And, of course, there is no single approach that will always perform better than all the others. This means that finding satisfying solutions in specific applications requires a good understanding of the strengths and weaknesses of existing approaches. To that end, their behaviors need to be investigated in a wide variety of situations, which may ultimately lead to the creation of a sort of collection of “spatial data analysis recipes”, capable to highlight their common “ingredients” and specific “taste”. These considerations prompted us to organize during the last three European Geosciences Union (EGU) General Assemblies (2016, 2017 and 2018) a conference session titled “Learning from spatial data: unveiling the geoenvironment through quantitative approaches” (conveners: S. Trevisani, I. Bogunović, M. Cavalli, S. Crema, J. Golay, P. Pereira, A. Piedade, G. Teza). This initiative has been promoted in the last few years also in the context of the annual meetings of the “Geosciences and Information Technologies” group (GIT, a section of the Italian Geological Society, http://www.giton line.eu), with a scientific session specifically dedicated to spatial data analysis. The session has been envisioned for exploring how the modern earth scientists use advanced geospatial analysis methodologies in their research. The present topical session in the Journal of Environmental Earth Sciences has been promoted during the EGU-2017 meeting; the choice of the Journal has been specifically performed given the focus of the session on geoenvironmental challenges. In particular, the interest of the session and, ultimately, of the present topical collection, is on studies presenting intuitive and applied mathematical/numerical approaches, capable of highlighting their key * S. Trevisani [email protected]