geospatial statistics

Spatial Models and Extreme-Value Methods for Wildfire Risk Assessment

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B3 L5 R5220

The statistical modeling of spatial and extreme events provides a framework for the development of techniques and models to describe natural phenomena in a variety of environmental, geoscience, and climate science applications. In a changing climate, various natural hazards, such as wildfires, are believed to have evolved in frequency, size, and spatial extent, although regional responses may vary.

Better health from better data

1 min read ·
The fast-developing science of geospatial statistics offers new insights into diverse health challenges, from cancer hotspots to problems of air pollution and the spread of COVID-19. Three new projects should yield a better understanding of what causes disease in Saudi Arabia and how to fight it. KAUST also stands to make a real contribution to the region by improving health in the Middle East and North Africa (MENA). The field of geospatial statistics and health surveillance, known as GeoHealth, uses data from multiple sources to create disease maps of geographical areas. When information is

Hanan Alahmadi

Ph.D. Student, Statistics
Hanan Alahmadi is a PhD candidate in Statistics at the King Abdullah University of Science and Technology (KAUST), studying under the supervision of Professor Paula Moraga in her Geospatial Statistics and Health Surveillance Research Group. Education Profile M.Sc. Statistics, KAUST, 2021 B.Sc. Mathematics, Taibah University , 2017 Research Interest Geospatial data analysis and health surveillance Spatio-temporal disease data Statistics and spatial epidemiology Geospatial modeling Disease mapping Location Building 1, Al-Khawarizmi, 4th floor, 4212-WS12 Publication Joint Quantile Disease Mapping

Elias Teixeira Krainski

Postdoctoral Research Fellow, Statistics
Elias T Krainski is a Post-Doctoral fellow In Statistics at the King Abdullah University of Science and Technology (KAUST), under the supervision of Professor Håvard Rue in his research group. Education and Early Career Postdoctoral fellow, Dalhousie University, 2019-2020. Adjunct professor, Universidade Federal do Paraná, 2018-2019. PhD, Norwegian University of Science and Tecnology, 2018. Research interests Efficient Bayesian methods for practical implementation, Spatio and spatio-temporal statistics, INLA.

Damilya Saduakhas

Ph.D. Student, Statistics
Damilya Saduakhas is a Ph.D. candidate in the KAUST Stochastic Processes and Applied Statistics Research group (StochProc) under the supervision of Professor David Bolin. Before joining KAUST, Damilya obtained a B.Sc. degree in mathematics from the Nazarbayev University, Kazakhstan. Research Interests Damilya's research interests include geospatial statistics, multivariate spatial processes, environmental modeling, and applications.