CASA Graduate Loubna Sasso wins SLA Masters Award 2022

Congratulations to recent CASA graduate Loubna Sasso, who has won the 2022 Society For Location Analysis (SLA) Masters Award for her dissertation, ‘Exploring areas of upgrade based on the 15-minute city concept – A case study of London’. Loubna's success is yet another scoop for CASA in student dissertation awards, following seven RGS-IBG Research Group Dissertation Prizes in 2020, 2021 and 2022 and a joint runner-up selection in the 2020 SLA Masters Award for Xiaomei Ge.

Loubna graduated this year with an MSc in Smart Cities and Urban Analytics. She has a passion for urban innovation and the use of data and technology to inform urban strategies, policies and design solutions that address current and future global challenges. With experience in urban design and geospatial analytics, she is currently seeking an opportunity to expand on her passion and put her knowledge and skills in the field of geospatial sciences to practice. Read her winning dissertation synopsis below.

Dissertation Synopsis
In collaboration with Transport for London, the project seeks to address climate change through a long term sustainable decarbonization strategy. This involves rethinking the way cities operate and accordingly how people interact and function within them through the concept of the 15 minute city. The model is tailored to adapt to the context of London’s decentralized urban structure and accordingly every 100m grid spatial unit across the city is analyzed using the measures of proximity, diversity and density.  The methodological approach used R programing language to assesses proximity to groups of clustered services rather than dispersed standalone services. By doing so, the study provides insight on the performance of different areas across London based on their ability to efficiently satisfying multiple needs within a single 15-minute walk. Moreover, the methodological approach advances from other studies by providing a holistic approach that looks at diversity of destination clusters as well as demand accessing these services (i.e., the population density) whilst integrating a walking speed using a network analysis to calculate proximity from origins to destination attraction points. A normalized score between 0 to 1 is then assigned to the outcome of the proximity and diversity measures and the combined scores are plotted against the outcome of the population density to understand areas where demand is unsatisfied by proximity and diversity of services offered. The results can aid policy makers in understanding which areas in London are underperforming and therefore would benefit the most from investment upgrades to ensure equitable and inclusive spaces based on the fifteen-minute city concept.  Moreover, the results of the study can easily be integrated and act as extension to the existing TFL, Web-based Connectivity Assessment Toolkit platform (Webcat). Sharing the finding of this study with TFL can support the implementation of alternative modes of transport that align with the Mayor’s Transport strategy including increasing the number bus journeys to support communities where 15-minute city scores are low.

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