R-QGISIntegratingSpatialAnalysis

This was an intermediate course of three sessions with a focus on developing skills in data visualisation, analysis and integration using both R studio and QGIS. Each session in the workshop begins with a 40-minute presentation of the principles to be covered before a practical exercise is offered. The exercises facilitate first-hand experience with exactly how these skills can be put to use in a practical manner.
All the material in this repo has been developed by Andrew Mc.Lean

• The first session offers an introduction to more advanced analysis and visualisation of datasets in R. The presentation shows how different aspects of datasets can be picked out, analysed and compared to one another, rather than simply using R to produce graphs of the dataset itself. The practical exercise focuses on an example of urban data, showing how the rank-size rule can be used to understand trends in the different populations of cities. This use of real data in a specific example showcases the possibility for more advanced analysis of statistical data in R.

• The second session acts to show how QGIS can be used to deepen understanding of the urban data analysed in session one. The presentation focuses primarily on emphasising how analysing the same data with different software, one statistical and the other GIS, can produce very different results, without changing the data themselves. The practical exercise continues with the urban example from session one but focusses on the physical location of the cities, and the spatial data. This shows how closely modern roads connecting the cities line up with the least cost path analyses as well as visualising the relationship between the physical location and population size of the cities.

• Friday 14 May: The final session builds on the first two in order to show how R and QGIS can be effectively integrated. The presentation demonstrates how QGIS can be used to generate additional data and how these data can then be analysed in R. In the practical exercise, the urban example is again used. This applies demographic data to the cities and allows for new values to be added to the dataset of cities based on raster values surrounding the cities. These new values are then imported into R and analysed and visualised in new ways.

What you are going to find in this repo

• .ppt presentations used during the course
• example code
• exercises with solutions
• data

This repo has a license