Packt – Species Distribution Models with GIS and Machine Learning in R-ZH

Packt – Species Distribution Models with GIS and Machine Learning in R-ZH
English | Size: 556.80 MB
Category: Tutorial

Key Features
Learn hands-on applications of the most important R concepts with limited mathematical jargon
Get started with time series from scratch
Explore how to use both GIS and machine learning techniques for spatial data analysis

What You Will Learn

Read data into the R environment from different sources
Become well-versed with basic spatial data concepts and data types
Analyze spatial data using R
Implement GIS and machine learning methods in spatial data analysis
Harness the power of GIS and machine learning in R for ecological modeling
Discover GIS and data analysis tasks related to SDMs including accessing species-presence data via R
Perform common GIS techniques on raster and other spatial data


In this course, you’ll work with real-world spatial data from Peninsular Malaysia to gain hands-on experience with mapping habitat suitability in conjunction with classical SDM models, such as MaxENT and Bioclim, and machine learning alternatives, such as random forests. The course will ensure that you are equipped to put spatial data and machine learning analysis into practice right away. You’ll have developed the skills necessary for working with ecological data and impress  potential employers with your GIS and machine learning skills in R.

Throughout the course, you’ll learn how to map suitable habitats for species using R. You’ll also explore common ecological modeling techniques and species distribution modeling (SDM) using real-life data. As you advance, the course will guide you in implementing some of the common Geographic Information Systems (GIS) and spatial data analysis techniques in R and use it to access ecological data.  You’ll perform common GIS and data analysis tasks related to SDMs, including accessing species-presence data, and get to grips with classical SDM techniques.

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