SaLLy - Spatio-temporal Modeling

Spatiotemporal modeling is a research field that combines spatial and temporal analysis to understand and predict changes in a system over both space and time. It involves the development of statistical and mathematical models that can analyze and integrate information from multiple sources, such as satellite images, sensor networks, and weather data, to predict patterns and trends in the spatial and temporal dimensions. Applications of spatiotemporal modeling can be found in various fields, including environmental science, epidemiology, transportation planning, and urban development. Spatiotemporal models can range from simple linear regression models to complex machine learning algorithms that use sophisticated techniques like deep learning and neural networks. The goal of spatiotemporal modeling is to provide accurate and reliable predictions that can be used to inform decision-making in a wide range of contexts.

Meet Our Spatio-temporal Modeling Team

Rodrigo Bulhões

Rodrigo Bulhões

Research Coordinator & Team Leader

Ongoing Projects and Activities

Spatio-temporal modeling of the number of fire outbreaks, by municipality and by Brazilian biome, between 2011 and 2021;

Spatio-temporal modeling of water volume data for reservoirs in Northeast Brazil.

Key Publications

- da Silva, K.L.S., López-Gonzales, J.L., Turpo-Chaparro, J.E., Tocto-Cano, E., and Rodrigues, P.C. (2023). Spatio-temporal visualization and forecasting of PM10 in the Brazilian state of Minas Gerais. Scientific Reports, 13, 3269.

- Encalada-Malca, A.A., Cochachi-Bustamante, J.D., Rodrigues, P.C., Salas, R., and López- Gonzales, J.L. (2021). A spatio-temporal visualization approach to the exploration of PM10 concentration data in Metropolitan Lima. Atmosphere 12, 609.

The complete list of publications from SaLLy members can be found in our Google Scholar profile.