Today is the first day of our new Gecomputation and Spatial Analysis (GSA) pathway on our undergraduate degree. Over the summer Jon Reades, Naru Shiode and I have been developing module material and today we (well, Jon and I) finally get to use it with our students. We provide a very brief overview of the pathway on the About page of this website, but I thought today is opportune moment to discuss it in a little more depth.
As highlighted in a recent report by the UK Economic and Social Research Council, human geography in the UK has been recognised for its conceptual innovation, but its current low levels of quantitative and technical training is of concern. For example, concerned with such low levels of training in quantitative methods, in a recent paper Ron Johnston and colleagues argued that the curricula of current undergraduate programmes in geography are failing to develop graduates that can “appreciate the underlying principles of quantitative analyses and their important role in the formation of an informed citizenry in data-driven, evidence-based policy societies”.
These societies are produced as digital technologies become pervasive throughout society and science. Global positioning system (GPS) technologies that allow the precise location of mobile devices on the Earth’s surface have become miniaturised and mainstream (e.g. in smart phones), generating geo-data not before available. Governments and other organisations are now opening up their digital databases on schools, crime, health and other public services for re-use and investigation by others (e.g., UK OpenData). Investigation of these (often) geo-referenced and large digital datasets requires computation to ensure patterns can be identified efficiently and in a reproducible manner. Put together, as Elvin Wyly recently discussed, the multiple aspects of this ‘big data’ digital revolution create new geographies and provide new means to explore and understand geography. Although an older concept, this has led to a resurgence in the idea of Geocomputation.
Recognising this issue, and in the context of the importance of the ‘big data’ revolution and the increasingly pervasive influence of computing devices outlined above, we set out to develop the GSA pathway. The pathway will enable students to develop the skills needed to undertake independent geographical inquiry using the latest datasets and computational tools, and to understand how they do and can shape the geographical world. Important for developing a curriculum in this context is acknowledging that the aim is not to produce computer programmers with no means of thinking critically about how their tools inform or change the geographical world, but to produce geographers that comprehend how new data and computational tools can be used to understand geography and that have the technical skills to use those tools.
Geography as a discipline has often had a critical or radical streak aiming to promote social change or combat oppression (e.g., see Antipode). If our future social and geographical world is to be based in-part on ‘data-driven evidence-based policy’ as Johnstone argues then Geography students at least need the basis of the technical skills and understanding to contribute to driving social change in that data and technology-driven world. A significant challenge for the GSA pathway then is the need for students to learn new skills such they are empowered to be able to employ computational techniques for data analysis.
This first module on the pathway, named simply Geocomputation, is foundational in that students will be learning skills and methods that they are unlikely to have encountered previously but which they will need if they are to continue to use understand the possibilities of (and use!) these new forms of data and technology in future. However, it is also important that whilst skills are learned the curriculum is not so narrow as to prevent curiosity about the geographical world or inhibit the geographical imagination. Consequently, we’ll be pushing students to ‘learn by doing’ and take an inquiry-based learning approach – my own experience of learning computational skills shows that these skills are best acquired when using them to work towards answering some particular question.
We’re looking forward to putting this theory into practice. We start today but hope to continue learning through the process and will post updates here when we can…