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The MRS Census and GeoDems group champions new thinking and new talent; one area they have been particularly impressed with is the CDRC Masters Dissertation Scheme (MDS)

This programme offers an exciting opportunity to link students on Masters courses with leading retail companies on projects which are important to the retail industry. The scheme provides the opportunity to work directly with an industrial partner and to link students’ research to important retail and ‘open data’ sources. The project titles are devised by retailers and are open to students from a wide range of disciplines.

MRS CGG are proud to have been granted permission to publish abstracts from the dissertations and we are sure the students have a great future ahead of them.

This abstract is by Tong Li

Title: Understanding the impact restrictions on mobility and working from home has had on Greater London train station hubs as desirable retail environment locations

Academic Institution: University College London

Industry Sponsor: CASA and LDC

Background and Motivation
Since the outbreak of COVID-19 and the accompanying restrictions on mobility have caused sustained retail losses and long-lasting changes to consumer travel and shopping habits. By addressing the new trend of using spatial big data and clustering methods for retail site-selection, this study sought to assess the performance of the retail environment around 11 major London station hubs during the various phases of the outbreak of COVID-19 using key indicators, tries to identify those areas that shown robustness, vulnerabilities and resilience in facing of external impacts. The results show that Liverpool Street station was more vulnerable during the lockdown phases and did not show sufficient resilience to face the post-pandemic era.

In contrast, the Westfield based retail environment around Stratford station hub, which had the most passengers' volume across Greater Britain during the lockdown, demonstrated robustness and resilience in the face of the impact. This study allows for the interpreting, extending and validating the results using more data, and has exhibited the potential to apply the indicators detecting retail health and the methods identifying retail environments with particular features to other retail areas.

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