Module Introduction

Welcome to this year’s Geocomputation module, a course that introduces you to both the principles of spatial analysis and the use of programming for data analysis.

Over the next ten weeks, you’ll learn about the theory, methods and tools of spatial analysis whilst implementing small research projects, first using Q-GIS, and then using the R programming language within the R-Studio software environment. You’ll learn how to find, manage and clean spatial, demographic and socio-economic datasets, and then analyse them using core spatial and statistical analysis techniques. The course is an excellent precursor for those of you interested in a career in (spatial) data science!

The course will consist of approximately 10 lectures, 10 self-led practicals, 5 seminars (held online) and 5 coding help sessions (held online), further details of which are provided on the next page, Module Information.

For now, if you’ve not watched the Introduction video on Moodle, you can catch up below:

Geocomputation Introductory Video

Remember you must have joined our Geocomputation Team on Microsoft Teams to be able to watch our lecture videos - instructions are provided on Moodle.

Learning Objectives

As you’ll have read in the Module Catalogue entry, the main learning objectives for the module are as follows:

  • Understand the ways in which digital representations of the observable world are created, and how representations of neighbourhood communities are built from publicly available Open Data.
  • Gain practical experience of the use of analytical methods to profile small areas of London.
  • Understand the nature of geographic data, and the concepts of spatial autocorrelation, modifiable areal units and neighbourhood classification.
  • Understand the sources and operation of uncertainties in the creation of geographic representations, and the importance of generalisation, abstraction and metadata.
  • Gain practical experience of software, map design and visual communication.
  • Develop practical skills in data acquisition and analytics, which may be useful in the planning of dissertations.

We hope that you’ll learn many other things during the module and it inspires you to think about how you might use spatial analysis, GIS and programming in your future career!

Getting in touch during the module

The module is convened and taught by Dr Joanna Wilkin - you can contact her at j.wilkin [at] ucl.ac.uk or, for online office hours, you can book a half hour slot using MS Bookings.

The module is further supported by two Postgraduate Teaching Assistants: Jakub (Kuba) Wyszomierski and Nikki Tanu. They will host coding help sessions on the alternative weeks to our scheduled seminar sessions.

Acknowledgements

Putting together a workbook such as this is no easy feat - but it’s also something that after a little time with R-Studio, you’d be able to produce! The reason for this, as we’ll repeat throughout the course, is that there is an incredible amount of resources available online that can help you learn the skills required to produce a website like this (e.g. using Git with R, using GitHub to host websites, R-Markdown, basic CSS styling). These skills firmly fall outside of the requirements for this course, but something you can build on in your spare time and over your future career. Believe it or not, we as lecturers are also always still learning - particularly, as you’ll find if you continue in spatial data science, the tools and technology available to us is continuously changing!

Content-wise, the lectures and practicals for this course are all original this year to the Geography Department at UCL. There is some overlap between this course and the Principles of Spatial Analysis module that is run at the Master’s level, e.g. the extensions offered in several of the practicals.

Aesthetics-wise, the R package and analysis artwork used within this book has been produced by allison_horst, whilst much of the artwork used in information boxes has been produced by Desirée De Leon, as well as by Jo. You can find Allison’s images on the stats illustration GitHub repository and Desirée’s on the rstudio4edu GitHub repository.

Yihui Xie’s Authoring Books with R Markdown and rstudio4edu’s book, A Handbook for Teaching and Learning with R and RStudio were key resources in the creation and editing of this book. In addition, the CASA0005 practical handbook (by Dr Andy MacLachan and Adam Dennett) alongside our own Principles of Spatial Analysis practical handbook (by myself and Justin van Dijk) served as inspiration for the structure and formatting of this book. For some practicals, additional acknowledgements are made at the end where code or inspiration has also been borrowed!

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