Spatial and Spatial Temporal Statistics: Modeling and Applications

Instructor: , Assistant Professor of Biostatistics, Dornsife School of Public Health

Dates: Monday, June 24 - Friday, June 28

Times: 1:00 p.m. - 4:00 p.m. EST

Format: In-person instruction

This course provides an introduction to spatial statistics. The course will cover various types of spatial data including discrete space and continuous space data and will compare/contrast the challenges encountered when analyzing these types of data. The course will cover standard and state-of-the-art methods from the statistical literature for modeling spatial data and delve into their motivations. Models will be fitted using a combination of code written from scratch and existing/freely-available software tools e.g., R, STAN etc.

Learning objectives:

• Familiarity with different types of spatial data
• Knowledge of spatial and spatiotemporal modeling approaches
• Familiarity with software (BUGS, NIMBLE, CARBayes, INLA, spX etc.).
• Building open-source user friendly software that focuses on spatial data ad applications.

Prerequisite knowledge: None

Technical requirements: R/R-Studio (Posit)

Continuing Education Credits*: 1.5 CEU or 15 CPH

Host or Publisher Drexel Urban Health Collaborative Summer Institute