A comprehensive course to explore fundamentals related to statistics focused on spatial data analysis. Through cutting-edge methods and techniques, you will learn how to predict and infer spatial patterns effectively. Also, designed to be undertaken with R, a command line-driven program, free of charge, with powerful statistical and spatial analytical packages.
Why Choose Our Module?
Practical Outcomes: As you progress through the module, you will achieve a comprehensive set of outcomes, including a solid review of statistics for spatial analysis, proficiency in geostatistical techniques, the ability to assess spatial autocorrelation and model uncertainty, and the skills to address real-world challenges in natural resource management, environmental modeling, and urban planning.
Upon successful completion of this module, you will be able to:
Review Foundations: Gain a comprehensive grasp of basic statistics for spatial data analysis.
Geostatistical Proficiency: Utilize geostatistical techniques, with a focus on variogram functions for trend assessment and kriging for spatial correlation analysis.
Spatial Insight: Understand spatial autocorrelation and conduct thorough model uncertainty checks.
Coding Competence: Acquire proficiency in coding and programming using R and diverse spatial data analysis packages.
Module Commencement: October 18, 2023 (Wednesday)
Participants without knowledge of the R language are encouraged to study this introduction to R: <https://cran.r-project.org/doc/manuals/R-intro.pdf>
For application details and further information, visit TUCaN (13-B0-0006-ue Geostatistics) or contact us through <email@example.com> or <firstname.lastname@example.org>.