Core Spatial Data Analysis: Introductory GIS with R and QGIS

This lecture presents a brief introduction to what the course is about and the course instructor. 

Link to data used in this course and code files. Brief description of the software and packages to be installed prior to staring

In this section the students are going to be introduced to spatial/geo-spatial data and how they differ from GIS. The students will then be introduced to the project they are going to work with, along with an introductions of the different questions they can answer through spatial data analysis. We will talk about the important applications of spatial data analysis, sources of free data and tools to be used in this course (R and QGIS). Briefly present the different softwares and packages that need to be installed.

This lecture will introduce some of the most common spatial data concepts that we will encounter most frequently. I will touch upon Coordinate reference systems and projections. I will also briefly introduce Raster data, vector data and point data

In this lecture we will quickly reiterate the things we have learnt in the previous lectures. By now we have installed the both QGIS, R and the basic R packages needed for spatial data analysis. We are now familiar with terms such as latitude-longitude, coordinates and know the difference between raster and vector data. We are acquainted with our project study site and some of the questions spatial data analysis can help us answer. If we are all comfortable with this, its time for a quiz and next sections

A few questions to make sure you are up to speed on the topics covered in Section 1

Briefly demonstrate how raster data (with different extensions) and of different extents can be read in to QGIS

In this lecture we will see how we can convert the coordinates of raster data from UTM to Latitude-longitude and vice versa.

In this lecture we will see how to apply arithmetic operations to raster bands. We will also compute NDVI in R

In this lecture we will see how to convert continuous value raster data to categorical raster, i.e. assign unique categories to a given range of raster values

We will be introduced to an R package- SDMTools. The functions of this package can be used for a number of landscape ecology related computations. In this lecture, we will see how the attributes relating to different raster classes or categories maybe computed in R

In this lecture we will see how landscape level statistics such as percentage land-cover, patch related statistics can be computed in QGIS. This lecture will also introduce a very useful QGIS Plugin- Lecos or Landscape Ecology.

In this lecture we will see how the pixel size (and extent) of a given raster can be modified using another raster as a baseline

In this lecture we will see how to extract the given portion of a raster or clip a raster using polygons in R.

In this lecture we will how a Digital Elevation Model can be examined within R. Further we will see how to derive topographic products such as slope, aspect from elevation data

In this lecture, we will see how to carry out basic statistical operations on raster data in R; including extracting descreptive statistics, correlation and linear regression

We will quickly discuss the things we have learnt so far in section 2 before moving to a brief quiz on raster data analysis

What is tthe resolution of Landsat data in metres?

In this lecture we will see how we can read in vector data using QGIS

In this lecture we will explore the different functions and packages that let us read in vector data in R

In this lecture we will explore and visulaize shapefile attributes in R

In this lecture we will explore basic map visualizations using in-built spatial data of R with our own shapefiles

In this lecture we will see how to isolate specific portions of a shapefile by sub-setting and split a shapefile using a unique attribute

In this lecture we will see how to split and merge shapefiles based on a given unique attribute in QGIS

In this lecture we will quickly reiterate the things we have learn in Section 3

In this quiz, we will quickly reiterate the concepts we learnt in Section 3