Spatial Data Sciences

Have you ever wondered why a phenomenon occurs in a specific location? What tools do we have to model and predict phenomena with a significant spatial component? Spatial Data Science offers insights into these questions.

Spatial Data Science is an interdisciplinary field that integrates traditional data science methods-such as Machine Learning and Artificial Intelligence-with spatial analysis methodologies, including Geographic Information Systems, Geostatistics, and Remote Sensing. This combination enables us to understand, characterize, and manage big spatial data.

These tools are essential for fields that require advanced predictions, such as human behavior modeling, pandemic dynamics, extreme climatic events, and oceanographic applications. In fact, spatial data science plays a crucial role in the IT core business of companies like Google, Uber, and Amazon.

This seminar explores various methods and applications of Spatial Data Science, aiming to foster collaboration and strengthen synergies between research centers in this rapidly growing field.

From the very beginning (2022), two fundamental pillars have guided the structure of these seminars to ensure the discipline's visibility within the school: the diversity of applications, and the originality and creativity in the application of Spatial Data Science algorithms and methodologies.

It is with great satisfaction that, on this fifth anniversary, we observe that these guiding principles have far exceeded our expectations. The breadth of applications and the originality of the SDS algorithms showcased in the selected presentations of this year's programme stand as a clear testament to the maturity of the discipline.

Enrollment is possible until 15^th May here.