Call for Papers: Spatiotemporal Data Science: Theoretical Advances and Applications

Guest Editors:

- Federico Amato, Swiss Data Science Centre, École polytechnique fédérale de Lausanne (EPFL) and Eidgenössische Technische Hochschule Zurich (ETH) , Switzerland,

- Luigi Lombardo,​ University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), The Netherlands, ​ 

- Marj Tonini,  ​I​nstitute of Earth Surface Dynamics, University of Lausanne, Switzerland,

- Antonino Marvuglia, ​Luxembourg Institute of Science and Technology (LIST), Luxembourg, a​

- Daniela Castro-Camilo, S​chool of Mathematics and Statistics, University of Glasgow, UK, d​  

- Fabian Guignard, ​ ​Institute of Mathematical Statistics and Actuarial Science, University of Bern, Switzerland,


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: