Call for Papers: Spatiotemporal Data Science: Theoretical Advances and Applications
- Federico Amato, Swiss Data Science Centre, École polytechnique fédérale de Lausanne (EPFL) and Eidgenössische Technische Hochschule Zurich (ETH) , Switzerland, email@example.com
- Luigi Lombardo, University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), The Netherlands, firstname.lastname@example.org
- Marj Tonini, Institute of Earth Surface Dynamics, University of Lausanne, Switzerland, email@example.com
- Antonino Marvuglia, Luxembourg Institute of Science and Technology (LIST), Luxembourg, firstname.lastname@example.org
- Daniela Castro-Camilo, School of Mathematics and Statistics, University of Glasgow, UK, email@example.com
- Fabian Guignard, Institute of Mathematical Statistics and Actuarial Science, University of Bern, Switzerland, firstname.lastname@example.org
Most environmental processes are characterized by variations in both space and time. These spatio-temporal phenomena have been traditionally investigated using linear statistical approaches, as in the case of physically-based models and geostatistical models. Additionally, the rising attention toward machine learning, as well as the rapid growth of computational resources, opens new horizons in understanding, modelling and forecasting complex spatio-temporal systems through the use of stochastic non-linear models.
This special issue aims at exploring the new challenges and opportunities opened by the spread of data-driven statistical learning approaches in Earth and Soil Sciences. We invite cutting-edge contributions related to methods of spatio-temporal statistics and data mining on topics including, but not limited to:
● advances in spatio-temporal modeling using geostatistics and machine learning;
● uncertainty quantification and representation;
● innovative techniques of knowledge extraction based on clustering, pattern recognition and, more generally, data mining.
We also expect the submission of papers related to applications in the fields of environmental sciences and quantitative geography. A non-complete list of possible applications includes:
● natural and anthropogenic hazards (e.g. floods; landslides; earthquakes; wildfires; soil, water, and air pollution);
● interaction between geosphere and anthroposphere (e.g. land degradation; land use change; urban sprawl);
● socio-economic sciences, characterized by the spatial and temporal dimension of the data (e.g. census data; transport; commuter traffic).
September 1, 2020: Opening Paper Submission
November 30, 2021: Deadline for Paper Submission
April 1, 2022: Notification of Acceptance for last papers
June 15, 2022: SI Finalization
Papers should be submitted to the Special Issue entitled “Spatiotemporal Data Science: Theoretical Advances and Applications” at: https://www.editorialmanager.com/serr/default.aspx