Research Method: Critical Spatial Data Science

30th May 2023

Funded Project:
Methods for Change Phase 2

This ‘how to’ guide outlines the Critical Spatial Data Science research method used by Dr Caitlin Robinson from the University of Bristol. The artwork was created in collaboration with Jack Brougham.

Social scientists from the Methods for Change project came together to discuss the research methods they use and how methods create change in society. Drawing from the expertise in the Aspect network, they collaborated on a series of ‘how to’ guides which are step-by-step instructions and top-tips for adopting these methods in a range of sectors. Visual and multisensory pieces, including comics, illustrations, posters, booklets, short films and animations were then developed in collaboration with creatives to capture the key value of these research methods with a view to being able to convey them to a variety of audiences.

It is hoped that these resources will be useful for people in higher education, commercial, public sector, third sector and community organisations who are interested in experimenting with, and expanding professional skills in, the adoption of social science research methods.

Critical Spatial Data Science (or Geographic Data Science) analyses quantitative data with some form of spatial identifier – for example, a coordinate, a street name, or a census block – to generate new knowledge.

In carrying out analysis of spatial data, a critical analysis is typically underpinned by theories that help us to understand – and therefore to best represent – complex real-world processes. Critical Spatial Data Science has wide-ranging applications with the potential to provide new insights into the distribution and dynamics of populations and societies across space and time. However, most commonly it seeks to understand and evidence socio-spatial inequalities, for example, inequalities in health, infrastructure, or education. Critical Spatial Data Science is founded on principles of openness, transparency, and reproducibility. At its best, the approach can be used to evidence and challenge injustice and have real-world impacts beyond academia. The field is rapidly expanding, drawing on an increasingly diverse range of spatial methods and data.

Download the Critical Spatial Data Science ‘how to’ guide.

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