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<https://discovery.ucl.ac.uk/id/eprint/10172718> <http://purl.org/dc/terms/title> "An Integrated Deep Learning Model based on Retail, Residential and Footfall Changes data to Predict Retail Gentrification"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10172718> <http://purl.org/ontology/bibo/abstract> "Retail gentrification is widely considered to hinder urban sustainability and trigger social discord in cities in the Global North. The central motivation for this thesis is to predict the British high streets in which retail gentrification is likely to occur.\r\n\r\nRetail gentrification is commonly observed to entail changes in retail composition, with new retail offers patronised by the young and middle class, as well as an increase in the volume of footfall. To estimate these changes, this study employs innovative and continuously updated datasets supplied by ESRC’s Consumer Data Research Centre (CDRC). These data make it possible to develop detailed representations of the preconditions to and processes of retail gentrification in terms of retail and residential change as well as footfall activity. The study period includes the COVID-19 pandemic, and a series of analyses are developed to assess any consequential and enduring effects upon retail gentrification.\r\n\r\nWe project changes in their structure and activities between the present day and 2040 using a deep learning Retail, Residential and Footfall changes (RRF) model. This is comprised of Autoencoder, Long/Short-Term and Bayesian Neural Network components. Using its predictions, gentrified high streets in England are identified by investigating high streets that exhibit changes in composition and function. We also explore the geographical distribution of these locations as well as their noteworthy characteristics.\r\n\r\nTaken together, this research develops a retail gentrification model using unique data and state-of-the-art deep learning techniques. It also explores the implications of the modelling effort for managing the phenomenon of retail gentrification through policy interventions."^^<http://www.w3.org/2001/XMLSchema#string> .
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