eprintid: 10172718 rev_number: 13 eprint_status: archive userid: 699 dir: disk0/10/17/27/18 datestamp: 2023-10-06 09:53:10 lastmod: 2024-07-01 06:10:28 status_changed: 2023-10-06 09:53:10 type: thesis metadata_visibility: show sword_depositor: 699 creators_name: Jeong, Byeonghwa title: An Integrated Deep Learning Model based on Retail, Residential and Footfall Changes data to Predict Retail Gentrification ispublished: unpub divisions: UCL divisions: B03 divisions: C03 divisions: F26 note: Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. 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. Retail 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. We 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. Taken 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. date: 2023-06-28 date_type: published oa_status: green full_text_type: other thesis_class: doctoral_embargoed thesis_award: Ph.D language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2033230 lyricists_name: Jeong, Byeonghwa lyricists_id: BJEON98 actors_name: Jeong, Byeonghwa actors_id: BJEON98 actors_role: owner full_text_status: public pages: 445 institution: UCL (University College London) department: Geography thesis_type: Doctoral citation: Jeong, Byeonghwa; (2023) An Integrated Deep Learning Model based on Retail, Residential and Footfall Changes data to Predict Retail Gentrification. Doctoral thesis (Ph.D), UCL (University College London). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10172718/2/An_Integrated_Deep_Learning_Model_based_on_Retail__Residential_and_Footfall_Changes_to_Predict_Retail_Gentrification.pdf