Geospatial Systems - Rachael Sanderson
In partnership with
G.19a Cassie Building, Newcastle University, NE1 7RU
About Rachael Sanderson
Prior to joining the CDT in 2019, I graduated from Durham University with an Integrated Masters in Human Geography. When undertaking my undergraduate degree, I developed skills in qualitative analysis, as well as an interest in quantitative methodologies, particularly how they have been critiqued. I joined the CDT because of its multidisciplinary nature that allows me to combine these interests, and the training available with the provision of MRes funding.
Throughout my time within the CDT, I have had the opportunity to develop my skills in GIS and data science, learning from experts and encouraging my interests in the cutting-edge ways to use geodata to further understanding. I developed an interest in the potential of different quantitative methodologies, particularly ways to measure and visualise social phenomena, and evaluating these methods as a social scientist. My long term goals are to continue my skill development in GIS and data science, and particularly to continue to apply my strengths as a social scientist to evaluate societal inequalities with links to policy decisions.
A multi-scalar analysis of peripheralization in the UK
My PhD will support the identification of left behind places. Left behind places are defined as communities that experience stagnation or decline, causing negative socio-economic impacts that lead to discontent. This trend is associated with disconnection, with the communities feeling isolated and ignored. The PhD evaluates three ways of measuring connectivity. Firstly, it measures the accessibility of a place by public transport. Defining accessibility as the ease with which a place can be accessed, I calculate the average travel time to reach a neighbourhood from anywhere in the country, in relation to the population size and available jobs. Secondly, I measure connectivity through migration, calculating how evenly dispersed the origins and destinations of incoming and out-going internal migrants are. A migration flow forms a social connection between places; somewhere that only interacts with a small number of places is at risk of being isolated and left behind. Finally, I use social media data to quantify individual connectivity to the Twitter network. Social media is an information source, so this approach identifies who is connecting to the network, and who is excluded. Comparing the results of these three studies will reflect on how left behind can be defined in different ways.