Dates:
22, 23 and 24 March 2023
Place:
Faculty of Sciences – University of Lisbon (Faculdade de Ciências da Univesidade de Lisboa),
Campo Grande, C6, Amphitheatre 6.1.36
1749-016 Lisboa Portugal
T (+351) 217 500 000
EVENT SPEAKER:
Prof. Paula Moraga is an Assistant Professor of Statistics at King Abdullah University of Science and Technology (KAUST), and the Principal Investigator of the GeoHealth group. Prior to KAUST, she was appointed to the academic statistics positions at Lancaster University, Harvard School of Public Health, London School of Hygiene & Tropical Medicine, Queensland University of Technology, and University of Bath. She received her Ph.D. in Mathematics from the University of Valencia, and her Master’s in Biostatistics from Harvard University.
Paula’s research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance. She develops spatial and spatio-temporal statistical methods to understand the geographic and temporal patterns of diseases, assess their relationship with potential risk factors, detect clusters, measure inequalities, and evaluate the impact of interventions. She also works on the development of statistical software and interactive visualization applications for reproducible research and communication, and the impact of her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries. She has published extensively in leading journals and is the author of the book “Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny” (2019, Chapman & Hall/CRC)..
More information:
https://www.paulamoraga.com/
About:
In this short course, organized by Centro de Estatística e Aplicações (CEAUL), we will learn statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. We will also learn how to create interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policymakers. We will work through several fully reproducible data science examples using real-world data such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. We will cover the following topics:
– Spatial data including areal, geostatistical and point patterns;
– R packages for retrieval, manipulation and visualization of spatial data;
– Statistical methods to describe, analyze, and simulate spatial data;
– Fitting and interpreting Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches;
– Communicating results with interactive dashboards and Shiny web applications.
The course materials are based on the book “Geospatial Health Data:
Modeling and Visualization with R-INLA and Shiny” by Paula Moraga (2019, Chapman & Hall/CRC) which is freely available at https://paula-moraga.github.io/book-geospatial/.
Prerequisites: It is assumed participants are familiar with R and it is recommended a working knowledge of generalized linear models.
Participants should bring their laptops with R and RStudio installed.
Program:
Day 1 – March 22
9h00 – 10h30 Lecture 1: Introduction to Spatial Data Science using R
Coffee Break
11h00 – 13h00 Lecture 2: Areal data: spatial autocorrelation and modeling. Making maps with R
Day 2 – March 23
8h30 – 10h30 Lecture 3: Geostatistical data: spatial interpolation
Coffee Break
11h00 – 13h00 Lecture 4: Geostatistical data: model-based geostatistics. Interactive dashboards with flexdashboard and Shiny
Day 3 – March 24
8h30 – 10h15 Lecture 5: Point processes: simulation and complete spatial randomness
Coffee Break
10h45 – 12h00 Lecture 6: Point processes: intensity and clustering
For more information, click here.
Referência Projeto: Projecto FCT: UIDB/00006/2020