Hello GIS

Spatial analysis can yield fascinating insights into geographical relationships. However, at times it can be difficult to work with. You will get lots of error messages and have software crash. The academic staff are here to help you work through these practicals but we do not know everything. It’s a good idea to become familiar with online sources of help, such as:

Want to see what you can do with spatial analysis…check out this ‘What’s Next’ video produced for the ESRI conference…

Intended course learning outcomes

After having taking this module, you should be able to:

  • Develop a working knowledge of QGIS and R to support the application of GI Science techniques

  • Visualise geographic information through producing appropriate maps to high cartographic standards

  • Carry out spatial data management tasks (joining attribute to geometry data, cleaning data, converting between file formats and spatial reference systems)

  • Interpret data and apply relevant spatial analyses (e.g. auto correlation/hot spot analysis, areal interpolation, point in polygon/buffer analysis, spatial statistical analysis) to answer a variety of spatial problems

  • Explain and evaluate common issues with geographic data such as representation and uncertainty

  • Apply and critique (spatial) statistical analysis techniques to infer relationships between spatial phenomena

  • Experience the diversity of the global spatial data landscape and evaluate the relative drawbacks and merits of different spatial datasets

There is a lot of information within this practical book and we do not expect you to read everything we link to. You should attend each lecture, go through every practical and do some associated reading.

This is a 15 credit module, equivalent to 150 hours of study (including the taught sessions). Outside of our lectures and practical sessions (3 hours a week) you should be spending an extra 12 hours a week on this module.

How to use this book

This website is hosted on GitHub and holds all the practical instructions and data. Data used within the practicals is available online, however occasionally websites can undergo maintenance or be inaccessible due to political factors such as government shutdowns.

To get the most out of this book spend a few minutes learning how to control it, in the top left of this webpage you will see this tools bar:

From left to right these buttons will let you:

  • control the side bar

  • search the entire book for a specific word

  • change the text size, font, colour

  • propose an edit if you see a mistake that I can review

  • view the webpage in the ‘raw’ RMarkdown format, we cover RMarkdown in the course

  • information about shortcuts for this book and most others like it

In addition the icon in the top right of the page takes you to the GitHub repository for this book, we cover GitHub in the course, but it’s basically where the online files for the book are stored.

Getting started

One of the issues with GIS is that many of the files we will be working with are quite large. Fortunately in recent years UCL has seriously beefed up the storage available for students. You now get 100GB of free storage, which should be plenty for the work you will be doing this year! The Bartlett faculty has several gigabytes of storage space available on their central servers, so before we get started, we will connect to our N drive to carry out all of our practical work over the coming weeks.

The data we use in this practical book is representative of what you will find when conducting independent analysis. Some books and website will give you perfectly ‘clean’ and ‘ready to use’ data, we have not done this on purpose as it’s very important to master data wrangling (also called data manipulation). In the ‘real world’ data is messy and it’s vital you know how to deal with it. Take this quote from the New York Times for example…

“Data scientists, according to interviews and expert estimates, spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”

Self guided learning

The lectures and practicals of this course only form a part of the learning process. You are expected to undertake wider reading and explore new methods and approaches. We have provided guidance on useful resources throughout the course to use as a starting point but you are encouraged to go beyond our recommedations and fully engage with applied GIS research, methods and visualisation techniques.

If you find a practical particularly easy or straightforward then please move on to the next one. Practicals that look at analytical relationships also have extension activities for you to try.

If you are struggling to use R don’t worry…here is some advice from a tweet and interview with Hadley Wickham, chief scientist at RStudio…


It’s easy when you start out programming to get really frustrated and think, “Oh it’s me, I’m really stupid,” or, “I’m not made out to program.” But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later.

You can also go through other free resources including:

At the end of every practical there is a link to an anonymous Google feedback form, let us know if something is unclear and we will go over it in a future session.