Geospatial Data Science with R
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 3h 54m | 2.76 GB
Instructor: Dr. Xiao Ping (XP) Song

• Hands-on learning with step-by-step code walkthroughs after each lecture
  
• Fully downloadable code notebooks complete with scripts, data processing workflows, and accompanying explanations
  
• All slides available as downloadable PDF
  
• No prior coding experience needed!
  
• Set up the computing environment for R programming following best practices
  
• Utilize RStudio, R Projects and R Markdown Notebooks for coding efficiency and reproducibility
  
• Use appropriate syntax, data structures, functions and software packages in R
  
• Understand and differentiate between various representations of spatial data, such as vector and raster formats
  
• Load, process and export spatial datasets, including those that exceed available memory (RAM)
  
• Select and use the appropriate coordinate reference systems
  
• Apply geometry and spatial operations to manipulate vector data
  
• Manipulate and summarize raster data to extract information from satellite imagery and other sources
  
• Create interactive and publication-ready maps and visualizations
  
• Develop workflows to automatically process and visualize geospatial data
  
• Apply techniques learnt to real-world problems in environmental monitoring and population demography
  

• No programming experience needed. We will set up the computing environment together and cover the fundamentals of coding
  
• Basic understanding of geospatial concepts and data is beneficial but not mandatory
  
• A computer to follow along with the coding exercises (MacOS, Windows). This will help you practice and reinforce your understanding of the techniques learnt
  

• Set up R Environment: Follow best practices in setting up your computing environment using RStudio, R Projects and R Markdown Notebooks
  
• R Fundamentals: Utilize appropriate syntax, data structures, functions and software packages for the given analysis
  
• Understand Spatial Data: Recognise the differences between vector and raster formats, and how various types of spatial data can be represented and analyzed
  
• Handle Spatial Datasets: Learn the techniques to load, process, and export spatial datasets, even when dealing with large files that exceed available memory (RAM)
  
• Coordinate Reference Systems: Learn the importance of coordinate reference systems (CRS) and be able to select and apply the appropriate CRS for your analyses
  
• Vector Data: Apply geometry and spatial operations to manipulate vector data
  
• Raster Data: Manipulate and summarize raster data to extract information from satellite imagery and other sources
  
• Data Processing Workflows: Develop scripts to automatically process and visualize geospatial data
  
• Create Engaging Visualizations: Develop publication-ready maps and visualizations to effectively communicate your findings to a broader audience
  
• Practical Applications: Apply your newfound skills to perform environmental monitoring and analyze population demography
  

• Beginners who want to use geospatial data as a stepping stone into coding
  
• Data scientists, researchers or developers who want to start working with spatial data and the open-source geospatial ecosystem
  
• Geospatial or GIS professionals seeking to automate and enhance the reliability of their work
  

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