Ecología espacial aplicada con R
Analyze point patterns with spatial statistics! Learn Moran's I, IDW, and kriging (ordinary & universal) for geoanalysis.
Course Content
Ecología espacial aplicada con R is an intermediate-level Udemy course taught by Adrian Cidre Gonzalez. It runs 8h 14m, is taught in Spanish, and includes a certificate of completion. Learners rate it 4.6 out of 5 from 6 reviews, with 36 students enrolled. It is a paid course, first published April 2025.
Explore Applied Ecology with R: A Comprehensive Course
This course provides a practical understanding of ecological principles using the powerful programming language R. You'll gain hands-on experience analyzing ecological data and creating spatial models. The course is structured into four blocks, each focusing on key ecological concepts and techniques.
Block 01 - Point Processes: Delve into the analysis of ecological phenomena occurring at specific locations.
- Introduction to Point Processes: Learn the fundamentals of analyzing point data.
- Density-Based Analysis: Master techniques for analyzing patterns based on point density.
- Distance-Based Analysis: Explore methods for understanding relationships based on distances between points.
- R Exercises: Apply your knowledge with practical exercises using R.
Block 02 - Area Data: Focus on analyzing ecological data distributed across areas.
- Introduction to Area Data: Understand the basics of working with spatial data.
- Spatial Autocorrelation: Learn to identify and analyze spatial patterns and dependencies.
- R Exercises: Practice analyzing area data with R.
Block 03 - Spatial Interpolation I: Discover techniques for estimating values at unmeasured locations.
- Introduction to Spatial Interpolation: Get a foundational understanding of spatial interpolation methods.
- Deterministic Methods: Explore various deterministic interpolation techniques.
- Geostatistics Principles: Learn the underlying principles of geostatistics.
Block 04 - Spatial Interpolation II: Expand your skills in spatial estimation.
- Geostatistical Methods: Dive deeper into advanced geostatistical techniques.
- R Exercises: Reinforce your skills with practical R exercises.
Each block contains 2-4 topics, combining theoretical classes, practical exercises, self-assessments, and assigned exercises. Throughout the course, you'll utilize the R programming language to perform practical analyses.
Prerequisites: Basic familiarity with R is recommended for optimal learning.
New to R? If you're starting from scratch, consider these related courses:
- [Introduction to Data Analysis and Visualization with R](link to course)
- [Introduction to Spatial Data Analysis with R](link to course)
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