This is the code repository for Hands-On Geospatial Analysis with R and QGIS 3.4 [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
This course introduces you to the full workflow from acquiring data, data wrangling and analysing to outputting and publishing visualisation products. We will touch on a variety of datasets including remote sensing data and techniques to incorporate machine learning in the analytical steps in QGIS. We will further investigate geospatial analysis using the most up-to-date R packages such as ggplot2, raster, sf, Leaflet and Shiny. By the end of the course, you will be able to produce interactive maps and professional cartographic products, deploy them as a Shiny application, and critique on a variety of end-results.
- Develop a Shiny application for geospatial data processing and visualizations using R and QGIS 3.4.
- Implement an efficient and reproducible workflow for geospatial analysis.
- Create interactive and professional mapping products and publish them on open applications.
- Conduct advanced geospatial analyses that address practical issues such as land cover using machine algorithms.
- Use modern and novel techniques to code with best practices.
- Utilize skills to serve a wide range of groups, including governmental organizations, Academia, consulting firms, and natural-resource industries.
- Critique a variety of geospatial data products and optimize your geospatial abilities to communicate your findings effectively
To fully benefit from the coverage included in this course, you will need:
● Familiarity with geospatial data
● Basic knowledge of Geographic Information Systems
● Basic knowledge of programming is helpful (i.e. in any of R, Python, or Matlab)
This course has the following software requirements:
● R version 3.5.0 or later. Download here: https://www.r-project.org
● RStudio. Download here: https://www.rstudio.com
● QGIS 3.4. Download here: https://qgis.org/en/site/forusers/download.html
This course has been tested on the following system configuration:
● OS: Windows 10 or Mac El Capitan (10.11) to Mac Sierra (10.12) and newer
● Memory: 4GB of RAM
● Storage: 5GB of free hard drive space