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Research Title
Real-time Geodemographics for Business and Service Planning
More about Mikaella
Work Experience
- Jacobs – GIS Analysis Intern (June 2022 – September 2022)
- Greater London Authority – Spatial Data Analysis Intern (June 2021 – July 2021)
Education
- University College London (UCL) PhD in Human Geography (October 2022 - Present)
- University College London (UCL) BA in Geography with Social Data Science (September 2019 - June 2022)
Teaching
I have been involved in the following teaching:
- Postgraduate Teaching Assistant, QMUL , Introduction to research methods (2022/2023)
- Postgraduate Teaching Assistant, UCL, Geocomputation (GEOG0030) (2023/2024)
- Postgraduate Teaching Assistant, UCL, Data, Politics and Society (GEOG0163) (2023/2024)
- Postgraduate Teaching Assistant, UCL, Data Analysis (POLS0010) (2023/2024)
- Postgraduate Teaching Assistant, UCL, Introduction to Quantitative Research Methods (POLS0008) (2023/2024)
Research Interests
Geodemographics present a conventional organising framework for representing the ways in which neighbourhoods are differentiated. They use a range of techniques for summarising large volumes of data into summary profiles that policymakers find helpful in making resource allocation decisions. There has been long-standing research of geodemographics in the UK with popular geodemographic classifications including the Output Area Classification and the Workplace Zone Classification. These classifications use variables pertaining to individuals at their normal places of residence and work respectively, albeit not identified for specific points in time. They are therefore not fully representative of the activity patterns of the population beyond night-time residence and work.
This research aims to combine a range of dynamic and conventional datasets to address the research gap introduced above and thus create a more dynamic representation of individuals’ everyday lives. A crucial dataset in the development of the current research is the raw timestamped in-app mobile location dataset that facilitates analysis on interactions of individuals in space and time. Other datasets include census data as well as consumer preference survey data, both of which will provide in-depth information on the socio- demographic and economic characteristics of individuals. By developing a classification that combines people’s place of residence, work, and any intermediate activity patterns they engage in, it will be possible to provide a tool for business location planning and service provision.
